3D printing technology offers a significant potential in the construction sector via enhancing build efficiency and enabling greater design freedom. However, its widespread adoption faces challenges related to the multi-objective optimization of concrete performance and the lack of intelligent decision-making for printing parameters. To address these issues, this paper was to propose a hybrid modeling framework that could integrate physics-informed and data-driven approaches for the intelligent design of 3D printing parameters tailored to target material properties. The framework first developed a rheological performance prediction model based on the physical information equation (PIE). This model, combined with the PIE, was then used to construct an extrusion screw speed (ES) database. The framework could enable accurate determination of the printing speed (PS) via incorporating key parameters such as the target concrete flow rate (Q), printing nozzle diameter (PN), and printing layer height (PH). The proposed intelligent parameter design method could provide a quantitative tool for the collaborative control of multiple concrete properties, significantly enhancing the efficiency and reliability of process parameter optimization. This study could offer a theoretical foundation and methodological support for improving the build quality and process stability of 3D printing concrete, thus facilitating the technology's application and adoption in construction engineering.

A hybrid physics-data driven framework was developed for printing parameter optimization. This involved two distinct fusion strategies:

1. Physics-Informed Rheological Prediction ("PIE embedded in ML"): A Physics-Informed Convolutional Neural Network (PICNN) was constructed to predict rheological parameters (i.e., Yield Stress-YS, and Plastic Viscosity-PV). Physical equations describing the relationship between mix proportions and rheology were embedded as soft constraints into the CNN's loss function. The Dung Beetle Optimizer (DBO) was used for hyperparameter tuning, including the weights balancing data-driven loss and physics-informed loss.

2. Physics-Serialized Extrusion Speed Prediction ("PIE serialized with ML"): An ES prior database was built via coupling multiple physical equations (i.e., density, Bernoulli, torque) with the predicted rheological parameters from the PICNN model. A Random Forest (RF) model, optimized using DBO, was then trained on this database to predict ES intelligently based on material properties and equipment conditions.

Based on the principle of mass conservation, an explicit calculation equation for PS was derived as a function of Q, PN, and PH. This could complete the parameter optimization chain from material properties to process parameters.

The PICNN model for predicting YS and PV demonstrates a stable convergence during training. Compared to the pure data-driven CNN model, the PICNN achieves significantly lower losses (e.g., testing set average loss for YS: PICNN 198.92 Pa vs. CNN 307.80 Pa; for PV: PICNN 0.37 Pa·s vs. CNN 0.77 Pa·s), confirming that physical constraints enhance predictive accuracy and robustness. Furthermore, the predicted values from PICNN show a high consistency with the calculations from the pure PIE (e.g., low MAPE of 6.02% for YS and 2.60% for PV on testing sets), thus proving a great adherence to physical laws.

The RF model for ES prediction, trained on the physics-generated database, achieves a high accuracy with R² values of 0.99 (training set) and 0.97 (testing set), and RMSE values of 1.86 r/s (training) and 2.39 r/s (testing). The scatter plots show points closely distributed around the fit line, and error distributions are concentrated at low levels, indicating the model's effectiveness in learning the complex relationships for ES prediction.

The derived equation for PS effectively establishes the quantitative relationship with Q, PN, and PH. A case study presenting calculated PS values for various common combinations of these target parameters provides a practical reference for configuring printing processes, demonstrating the applicability of the complete optimization framework.

This study established a comprehensive physics-data hybrid modeling framework for optimizing 3D printing concrete parameters, achieving a precise matching between parameters and material rheological properties.

The physics-informed rheological prediction model (PICNN) significantly improved prediction accuracy and physical consistency for YS and PV compared to purely data-driven or purely physics-based approaches.

The physics-serialized extrusion speed prediction method, utilizing a physically-consistent database and an optimized RF model, enabled high-precision, robust intelligent recommendation of ES based on material attributes.

The complete parameter optimization solution, integrating rheological prediction, ES prediction, and PS calculation, provided a systematic and intelligent paradigm for 3D printing parameter design, reducing reliance on conventional trial-and-error methods.

This research could contribute to enhancing the forming quality and process stability of 3D printing concrete, facilitating the intelligent development of construction 3D printing technology. Future work could focus on more complex multi-objective optimization scenarios and real-time dynamic parameter control strategies.

Concrete pumping technology is widely applied in major construction projects. However, under long-distance or complex conditions, concrete transport often exhibits strongly non-steady characteristics due to the coupled factors like material rheology and pipeline topologies. This instability easily induces abnormal resistance surges, evolving into severe blockages. Conventional monitoring relies on post-event management or static pressure thresholds, triggering alarms only when pressure breaches a safety limit. Essentially, concrete blockage is a progressive dynamic accumulation of rheological resistance, leaving a critical "pressure accumulation" intermediate state. Static thresholds ignore this, causing severe pre-warning lag. AI methods show a potential, while intelligent perception faces three challenges, i.e., the binary classification defect overlooking the transitional state, the "majority class trap" from extreme class imbalance, and the contradiction between prediction accuracy and physical interpretability. To address these bottlenecks, this study was to propose a novel intelligent evaluation framework for non-steady concrete transport, deeply integrating rheological mechanisms with advanced machine learning architectures.

The research relied on the Zhenglong Hongshui River Bridge project, China. An embedded monitoring system with 600 Hz dynamic pressure sensors was seamlessly welded to the straight pipeline's inner wall near the pump outlet to acquire high-quality rheological resistance signals. During feature engineering, mechanical noises from valve reversing were eliminated, retaining only effective pressure-holding signals during main cylinder propulsion. Since deriving the exact friction coefficient via true flow velocity is a challenge in field engineering, macroscopic proxy variables were utilized. Assuming constant propulsion speed, the slope of the pressure curve was extracted to represent the transient growth rate of rheological friction. Standard deviation and range were extracted to quantify slip-layer stability and flow turbulence. To address extreme class imbalance in the 691-sample dataset (78.3% normal), the Synthetic Minority Over-sampling Technique (SMOTE) was applied to reconstruct the feature space, eliminating training bias. Subsequently, four ensemble algorithms (i.e., RF, XGBoost, LightGBM, GBDT) optimized by the Tree-structured Parzen Estimator (TPE) were used as base learners. Finally, a dynamic adaptive ensemble fusion architecture calculated sample-specific dynamic weights based on prediction confidence, replacing conventional static voting mechanisms.

The extracted time-domain features effectively map the dynamic evolution of in-tube rheological resistance. After SMOTE reconstruction and TPE optimization, the base models' potential to capture minority class abnormal conditions is maximized. During the comparative evaluation of the ensemble models, the study addresses the "accuracy paradox" inherent in highly imbalanced datasets. Conventional soft voting and hard voting models achieve an overall accuracy of approximately 0.90 simply because of the overwhelming majority class, dangerously masking high misdiagnosis rates for critical anomalies. The overall accuracy improves to 0.92 via implementing the dynamic adaptive fusion strategy. More importantly, the model substantially suppresses the omission rates for high-risk conditions. The recall rate for the "pressure accumulation" state reaches 91.43%, representing a 10.3% relative improvement over the soft voting method (i.e., 82.86%). For the most hazardous "blockage" state, the recall reaches 88.00%, outperforming the soft voting method (i.e., 76.00%) with a remarkable relative improvement of 15.8%. Furthermore, a SHapley Additive exPlanations (SHAP) visualization framework is introduced to verify the model's decision logic globally and locally. The global feature importance ranking confirms that the pressure curve slope is an absolute dominant feature triggering blockage predictions, followed by range and standard deviation. The SHAP beeswarm summary plots reveal that a sharp increase in these three features heavily biases the model toward a "blockage" decision. Conversely, smaller feature values drive the model toward a "normal" prediction. Local waterfall plots for individual samples further detail the positive and negative contributions of each feature to a specific prediction. This data-driven logic perfectly coincides with the physical reality that pipeline blockages instantly cause a sharp surge in rheological friction and severe dispersion in pressure values due to slip layer disruption. SHAP effectively explains the statistical feature attribution, and its strong alignment with engineering experience proves the model's physical rationality. Verifying strict causality requires a further extensive field validation across multiple complex scenarios.

Time-domain physical features validated the ability to characterize in-tube non-steady transport. The slope, standard deviation, and range extracted from high-frequency pressure sequences could serve as effective macroscopic proxies for concrete fluid resistance and slip-layer stability. The combination of SMOTE feature space reconstruction and TPE Bayesian hyperparameter tuning effectively overcome the small-sample fault warning dilemma under extreme class imbalance, eliminating training bias toward majority classes. The dynamic adaptive ensemble architecture demonstrated a core advantage in suppressing high-risk omission rates. It leveraged a minor overall precision increment to drastically reduce crucial fault omission risks via tolerating a minimal sacrifice in majority class accuracy, delivering an ample forward-intervention window. The SHAP attribution analysis achieved a white-box deconstruction of the ensemble decision logic. The alignment between the algorithm's reliance on slope surged and the true physical laws of rheological friction endowed the model with clear physical rationality, laying a solid theoretical foundation for the trustworthy application of AI technologies in intelligent civil engineering construction.

X-ray computed tomography (CT) is an important non-destructive technique for analyzing the microstructure of cement-based materials, which can characterize aggregate distribution, pore structure and interfacial transition zone. The accuracy of subsequent 3D reconstruction and quantitative analysis is highly dependent on the quality of image segmentation. However, concrete CT images generally have some problems such as low contrast between aggregate and matrix, blurred boundary and strong noise, which seriously limit the effect of conventional segmentation methods such as threshold method.

Deep learning performs well in complex image segmentation, but its training relies on large-scale and high-quality labeled data sets, while CT image labeling is difficult and time-consuming. In practice, there are often only a few labeled slices, leading to insufficient generalization ability and unstable performance of the model, especially in low contrast and fine structure areas. To solve the contradiction between model complexity and data availability, it is necessary to develop efficient learning strategies that can make full use of unlabeled data.

This paper was to propose a semi-supervised deep learning framework for automatic segmentation and three-dimensional reconstruction of concrete CT images. This method could effectively utilize a small number of labeled slices and a large number of unlabeled slices based on U-Net segmentation model and self-training mechanism. Through pseudo-label generation and confidence filtering, the boundary recognition and structural consistency under low contrast conditions could be improved.

The proposed framework consisted of three stages, i.e., initial supervised training, pseudo-label generation, and iterative semi-supervised optimization.

Firstly, U-Net was used as a backbone model, and its encoder-decoder structure was combined with jump connection, thus extracting high-level semantic and fine spatial features at the same time. The initial segmentation ability was obtained with 42 manually labeled CT slices, combined with Dice loss and binary cross entropy loss for training.

Secondly, the trained model was applied to 2000 unlabeled CT slices to generate pixel-level prediction and probability maps. Through the confidence threshold, the high-reliability region was retained as a pseudo-label, and the low-confidence region (usually near the fuzzy boundary) was excluded. After morphological operation, the isolated noise was removed, and the boundary continuity was improved.

Finally, the filtered pseudo-labels were merged with the original labeled data to form an augmented training set. The model was iteratively retrained on this mixed set, with labeled data providing stable supervision, and pseudo-labeled data supplementing structural information. Also, the learning rate attenuation strategy was introduced to ensure the stability and convergence of training. The Dice coefficient and the intersection-union ratio (IoU) were used to evaluate the segmentation performance.

The performance of manual annotation quality and initial supervision model is evaluated. The marking mask can be close to the aggregate boundary in different scales, indicating great marking consistency and reliability. The U-Net model trained with only 42 labeled slices can quickly learn the discriminant characteristics of aggregate and matrix, and obtain a better verification performance (i.e., the optimum verification Dice = 0.860 6). However, in low contrast and complex structure areas, the performance still fluctuates, indicating that the generalization ability is limited when the training data is extremely limited.

The semi-supervised learning strategy significantly improves the performance of the model. The pseudo-label visualization shows that the high confidence region is highly consistent with the actual aggregate distribution, and the uncertainty mainly appears at the aggregate-matrix interface. With iterative training, the indicators further improve. The Dice, IoU and accuracy of the final model (S-40) reach 0.971 2, 0.944 0 and 0.982 0, respectively, which are 11.7 %, 22.7 % and 7.4 % higher than the fully supervised baseline, respectively, and the loss is reduced by 82.9 %. These results show that high-confidence pseudo-labels can effectively enhance feature learning without compromising training stability.

Three-dimensional reconstruction demonstrates that the proposed semi-supervised framework effectively preserves the spatial continuity and geometric integrity of aggregate particles. Compared with the fully supervised baseline, the reconstructed aggregates exhibit markedly fewer adhesion artifacts, better-defined particle boundaries, and more complete representations of fine aggregates and complex interfaces. These improvements enable reliable quantitative characterization of aggregate morphology, connectivity, and spatial organization, demonstrating that the proposed segmentation framework provides high-fidelity input for three-dimensional reconstruction.

Quantitative analysis reveals that aggregate volumes follow a log-normal distribution and exhibit a pronounced surface area-volume scaling relationship (i.e., ), reflecting the geometric self-similarity of the reconstructed particles. Large particles contribute most of the total aggregate volume, whereas smaller particles occupy the interstitial spaces to form a dense skeleton-filling structure. The reconstructed aggregates are uniformly distributed throughout the specimen without evident segregation, and the extracted morphological descriptors yield a mean sphericity of 0.757, indicating moderate particle roundness while preserving angular characteristics. Collectively, these findings validate the reliability of the proposed reconstruction framework and provide a robust basis for quantitative three-dimensional microstructural characterization and aggregate gradation analysis.

This paper proposed a semi-supervised deep learning framework for concrete CT image segmentation and 3D reconstruction. This method effectively overcame the difficulties caused by limited labeled data and low imaging contrast via combining pseudo-label learning with U-Net architecture. The results showed that the model converged stably, the segmentation accuracy improved significantly, and an effective balance between manual labeling and high-precision microstructure analysis could be achieved. The framework could provide an extensible solution for the intelligent characterization of cement-based materials and have application prospects in the field of digital engineering.

The clinker calcination process involves high-temperature solid/liquid reactions, and the controllable preparation of multiple clinker phase compositions, which further affect subsequent synergistic hydration reactions. The relationship between composition and 28 d compressive strength is not simply linear, but rather a complex mapping governed by the coupling of multiple processes. Conventional point-by-point experimental screening makes it difficult to cover a sufficiently broad compositional space within a limited timeframe. This restricts the exploration efficiency of novel low-carbon clinker. Machine learning models can be used to learn the composition and property relationships between clinker phase and strength, and to support the screening of candidate clinker compositions. However, under small data conditions, these models are susceptible to insufficient training distribution coverage, resulting in a unstable cross-system predictive performance. Therefore, this study was to develop a self-iterative data augmentation framework to address the above-mentioned issues. This framework was to expand effective supervised information to optimize the model's predictive performance in sparsely sampled compositional regions. Subsequently, model interpretability analysis was conducted to identify the key clinker phases affecting 28 d compressive strength prediction and their response patterns. This could provide a testable model basis for the optimization of low-carbon clinker compositions.

This study defined each simulated clinker as one sample. The dataset contained 264 simulated clinker samples, with the compressive strengths ranging from 0 to 70.3 MPa. The model inputs consisted of 12 features, including 11 clinker phases and gypsum, while the output was the 28 d compressive strength. In the self-iterative data augmentation strategy, an artificial neural network (ANN) model was first trained using the experimental data. Virtual data were then generated under compositional constraints, and their 28 d compressive strengths were predicted by the model from the previous iteration to assign pseudo-labels. The virtual and experimental samples were subsequently combined for training, and the surrogate model was updated iteratively. Two sampling patterns were adopted for virtual sample generation, i.e., 1) Pattern A: dense sampling within the experimental samples' compositional systems, and 2) Pattern B: generating virtual samples at a ratio of 1 ∶ 1 from the in- and out-of-system regions of the experimental samples' compositional space. Before training, 36 experimental samples were set aside as a test set, which remained isolated throughout training, iteration and hyperparameter selection. The validation set consisted exclusively of real experimental samples, whereas virtual samples were included only in the training set. Model hyperparameters were determined based on the Bayesian optimization, with the validation-set mean squared error (MSE) as the selection criterion. Finally, the model predictive performance was evaluated by MSE, the coefficient of determination (R²), mean absolute error (MAE), and mean absolute percentage error (MAPE). After the final surrogate model was determined, multilevel model interpretation was further performed via combining constrained permutation feature importance, accumulated local effects and SHapley additive explanations (SHAP).

The self-iterative augmentation strategy effectively fills sparsely sampled regions, while maintaining feasible-domain constraints. This provides richer training information for subsequent accuracy evaluation and interpretability analysis of the surrogate model. Meanwhile, the data augmentation process does not generate a large number of near-duplicate samples, and the training set retains the necessary heterogeneity. The inclusion of virtual samples reduces the model's sensitivity to randomness in data partitioning. Moreover, the overall test set error and the in-/out-of-system errors decrease progressively over successive iterations, indicating an improved cross-system generalization performance. Pattern B in Iteration II achieves a favorable balance between predictive accuracy and extrapolation stability. Its overall test set MSE is 9.8, with in-system and out-of-system MSE values of 6.9 and 11.3, respectively. The R², MAE, and MAPE are 0.79, 2.36 and 5.11%, respectively. These results indicate that, under the current small data conditions, data augmentation can substantially improve the model's ability to identify performance trends. Subsequent interpretability analysis of the optimal surrogate model shows that different clinker phases can be classified as positive or negative driving phases. The analysis further indicates that when C₃A content exceeds approximately 10%, its positive driving effect on strength decreases rapidly. Although CA and C₁₂A₇ act as positive driving phases, their benefits diminish when their contents exceed 40%, and even shift to strongly negative responses.

The self-iterative data augmentation framework proposed could expand a supervised training information for low-carbon clinker systems characterized by small datasets and sparse compositional spaces. The evaluation bias introduced by pseudo-labels could be also suppressed under a rigorous evaluation strategy. The results showed that data augmentation significantly improved the prediction accuracy and cross-system generalization capability of the ANN surrogate model for 28 d compressive strength. In Iteration II, Pattern B achieved the optimum overall performance under the current data conditions. Although further iterations reduced some in-system errors, an error rebound was observed for out-of-system samples. This indicated that diminishing marginal information could gain from pseudo-labels constrained the model's generalization performance. The combined use of constrained PFI, ALE, and SHAP revealed the decision-making basis of the surrogate model in terms of global sensitivity, average response trends, and sample-level contributions. The results indicated that C₄A₃$ and CA were the main positive driving clinker phases for compressive strength enhancement, whereas C₃A, C₅S₂$ and C₂S were the main negative factors. Moreover, C₁₂A₇ exhibited a pronounced threshold effect. These findings could provide a testable reference for subsequent composition screening, constraint-boundary definition and inverse design of low-carbon clinker.

The chemical composition of cement clinker directly affects cement hydration, later-age strength development, and quality stability, and therefore serves as a key basis for clinker quality evaluation and production regulation. Conventional clinker performance analysis mainly relies on chemical composition testing, empirical rules, and mineral-phase estimation methods such as the Bogue calculation. Although these approaches have the physical meanings and practical engineering applicability, their predefined model forms are insufficient to fully capture the complex nonlinear strength relationships arising from the coupled effects of multiple oxide components and the water-to-cement ratio.

In recent years, machine learning methods have been widely applied to predict the performance of clinker and cement-based materials, providing some opportunities for modeling complex composition-property relationships. However, the existing black box models usually do not give clear mathematical formulas, which makes it difficult to directly explain how the strength develops. In addition, most studies mainly predict the strength from chemical composition. They are less able to work in the opposite direction, that is, to identify suitable adjustment directions and feasible ranges of clinker chemical components when a strength-improvement target is given, either as a specific value or as an interval. To solve these problems, this study was to develop an explicit modelling and inverse optimization method for clinker performance via combining symbolic regression and semantic backpropagation. This method could provide an interpretable prediction of 28 d compressive strength and target driven inverse regulation of clinker composition.

This study proposed an explicit representation and inverse optimization method for clinker performance by combining symbolic regression with semantic backpropagation. This method was designed to address the limited explicit characterization of clinker composition-strength relationships and the difficulty of regulating clinker composition according to the strength requirements. First, clinker samples from different sources were collected, and their chemical compositions and 28 d compressive strengths were used to build a clinker composition performance dataset. Then, a symbolic regression based on genetic programming was used to search for explicit relationships between clinker chemical composition and 28 d compressive strength in an expression space formed by variables, constants, and mathematical operators. Expression trees and their corresponding equations were obtained from the candidate models. To avoid physically unreasonable models or the loss of important variables caused by relying only on prediction error, a model selection strategy was introduced via considering physical prior constraints, model complexity, and variable coverage. This strategy could balance prediction accuracy, physical rationality, structural simplicity, and interpretability. Furthermore, a semantic backpropagation method driven by target strength variation was used to propagate the required strength improvement backward through the explicit expression structure to the input variables. Feasible adjustment intervals for individual chemical components and collaborative optimization schemes for multiple variables were then derived. In this way, the proposed method could transform target strength changes into practical suggestions for inverse regulation of clinker chemical composition.

The results show that the symbolic regression model selected by considering physical prior constraints, model complexity, and variable coverage achieves a good balance between prediction accuracy and interpretability. The final model includes all nine key input variables, including CaO, SiO₂, Al₂O₃, Fe₂O₃, MgO, SO₃, K₂O, Na₂O, and W/C. Its expression is not a simple linear combination, but is composed of product terms, ratio terms, and nonlinear terms. This structure allows the coupled effects of the main clinker oxides and W/C on the 28 d compressive strength to be explicitly described. The sensitivity analysis further shows that K₂O, Na₂O, W/C, CaO, and Al₂O₃ have relatively great effects on the model output. Among these variables, W/C shows a mainly negative effect around the most samples, while CaO makes a positive contribution under most local conditions. These results indicate that the obtained explicit model can capture the basic characteristics of strength development controlled by the combined effects of multiple clinker components.

The final explicit model demonstrates a satisfactory predictive performance on both the training and test datasets. For the training set, the RMSE, MAE, and R² are 1.303 MPa, 0.903 MPa, and 0.811, respectively. For the test set, the corresponding values are 1.409 MPa, 1.126 MPa, and 0.817 Compared with multiple linear regression, Ridge regression, random forest, support vector regression, XGBoost, and conventional symbolic regression, the proposed method achieves a better prediction accuracy on the test set. Meanwhile, in contrast to black-box machine learning models, the proposed model retains an explicit expression structure, which can be directly used for variable-relationship analysis and subsequent target-driven input interval back-propagation.

The inverse optimization results show that the proposed model can translate requirements for strength improvement into interpretable suggestions for composition adjustment. For a representative sample with a predicted strength of 55.42 MPa, when the target strength increase is set as [1.5, 3.0] MPa, single variable backpropagation identifies feasible adjustment intervals for CaO, SiO₂, Na₂O, W/C, and other variables. Among them, CaO and W/C require smaller minimum adjustment magnitudes, indicating that they may be more suitable for single variable regulation. When CaO, Na₂O, and W/C are backpropagated simultaneously, the feasible solutions are not simple combinations of independent adjustment intervals, but appear as several local feasible regions. This result reveals clear synergistic and compensatory effects among clinker components. Sampling validation further confirms that multiple joint adjustment schemes can shift the predicted strength into the target range. These findings indicate that the proposed method can provide practical candidate solutions for clinker strength improvement, quality evaluation, and inverse mix proportion regulation.

This study demonstrated a potential of symbolic semantic backpropagation in clinker performance prediction, explicit relationship analysis, and strength-target-driven composition regulation. The proposed method established an explicit quantitative relationship between clinker chemical composition and compressive strength at 28 d via combining symbolic regression with semantic backpropagation as well as incorporating physical prior constraints, variable coverage screening and target-interval-based backpropagation. It could also transform strength improvement targets into feasible adjustment intervals for individual variables and collaborative optimization schemes for multiple variables. Compared with conventional black-box models, this method could offer a greater interpretability for the relationship between composition and strength and provides more direct inverse guidance for clinker composition regulation. Overall, it could provide an interpretable and practical approach for clinker strength prediction, quality assessment, and inverse mix proportion regulation.

Increasing global energy demand and depleting non-renewable resources drive the need for sustainable energy storage. Cement-based energy storage materials that combine structural load bearing and energy storage are well-received due to their low cost and scalability. Cement-based supercapacitors utilize the cement matrix to form electrical double layers as well as enable underpotential deposition (UPD) and highly reversible sorption and redox processes. Though, hardened cement paste has insulating properties, while the continual change in its pore structure is at different concentrations of ions, significantly limiting ions' transport and storage capability. Adding conductive materials to create coupled charge carriers electron and ions can be a solution. The multi-scale complexity of these composites inhibits the ability of classical empirical methods to disentangle the interaction effects among competing effects. In this study, we developed a machine learning framework that could identify hierarchical relationships among structural descriptors, polarization losses and rate conditions, to enable the multi-objective optimization and inverse design of cement-based supercapacitors.

Sixty groups of cement-based positive electrode samples with single and composite conductive phase combinations were fabricated. Raw materials included ordinary Portland cement, three conductive carbons materials (i.e., carbon black (CB), carbon fiber (CF) and carbon nanotube (CNT)), electrolytic α-MnO₂ as an active material, and polycarboxylate superplasticizer for fluidity enhancement. Electrochemical tests employed a two-electrode system in a 1 mol/L ammonium acetate electrolyte at two charge-discharge rates (C-rate), which were 1.0 C and 1.5 C. The internal resistance drop (V_IR). quantifying polarization loss, was defined as a difference between the 1.8 V cut-off and the discharge starting voltage. Electrochemical impedance spectroscopy (EIS) was carried out (i.e., 0.1-10000.0 Hz, 10 mV amplitude) to extract the ohmic resistance (R_s) and charge-transfer resistance (R_ct) of representative samples. A database of 280 valid dates was constructed to predict energy density. In terms of feature engineering, the raw composition descriptors were supplemented by the total carbon mass fraction [w(C_total)], the relative ratios of w(CB), w(CNT) and w(CF), the active-to-conductive ratio w(MnO₂)/w(C_total), and conductive network index (i.e., N_et = a·CNT_ratio + b·CF_ratio + c·CB_ratio). Interaction terms V_IR × N_et, and V_IR × C-rate (i.e., V_IR × 1.0 C and V_IR × 1.5 C) were introduced, and the C-rate was treated as a one-hot categorical variable to represent rate-polarization coupling. Three tree-based regressors, i.e., Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees), were trained under identical preprocessing using a 5-fold GroupKFold cross-validation scheme based on formulation and voltage descriptors to avoid information leakage. Model accuracy was assessed by the coefficient of determination (R²), the root-mean-square error (RMSE) and the mean absolute error (MAE). The best-performing model was retained for an Optuna-based outer search of the network-index weights under the ordered constraint a≥b≥c≥0, followed by hyper-parameter tuning. Permutation importance and SHapley Additive exPlanations (SHAP) were then applied to interpret the optimized model.

Among the three baseline models, ExtraTrees show the optimum generalization, with an out-of-fold (OOF) R² of 0.7724 and an OOF RMSE of 0.0939. Replacing the empirically assigned weights of the network index with data-driven values increases the OOF R² to 0.8240, and subsequent hyper-parameter tuning gives a final OOF R² of 0.8354 with an RMSE of 0.0793 mW·h·cm^-2. The optimized weights follow an order of CNT (2.084) > CF (1.335) > CB (0.006), indicating that high-aspect-ratio fillers dominate the construction of continuous electron-transport pathways, whereas a particulate CB mainly improves local interfacial contact and contributes little to overall network continuity. This ranking is supported by EIS results on samples with a fixed total carbon content of 10%, in which substituting CNT for CB reduces R_s from 47.90 Ω to 32.68 Ω and R_ct from 45.35 Ω to 28.76 Ω. The SHAP analysis identifies the V_IR as the most influential predictor of energy density and shows that its negative contribution becomes dominant at > 0.5 V. The network index acts as the main positive regulator, and shifts from a restrictive to a reinforcing role once it exceeds 0.5, corresponding to the formation of an effective percolation pathway. The rate-polarization interaction terms give stronger negative SHAP contributions at 1.5 C rather than at 1.0 C, indicating that higher C-rates amplify ohmic losses and narrow the range over that the structural advantage can be exploited. These results indicate a coupled structure-polarization-rate regulation mechanism, in which the energy density is not determined by any single descriptor, but by the matching among conductive network construction, polarization control and rate condition.

A machine learning framework, incorporating an optimizable conductive network index and rate-polarization interaction features, was developed for cement-based supercapacitors. ExtraTrees outperformed RF and XGBoost, and after weight optimization and hyper-parameter tuning, gave a cross-validated R² of 0.8354 for energy density. Data-driven weight learning quantified the relative contributions of the three conductive fillers as CNT > CF > CB, which was consistent with the EIS results. The SHAP interpretation showed that the IR drop could be the primary controlling variable, the network index could be the main positive regulator, and the rate-polarization interaction could be a rate-amplified negative modulator. The energy density of cement-based supercapacitors thus depended on the matching among conductive network construction, polarization control and operating C-rate. The proposed framework could provide a data-driven and mechanism-informed basis for the prediction and optimal design of cement-based energy storage materials.

Lunar construction represents a strategic frontier in deep space exploration. Alkali-activated lunar regolith simulant (AALRS) is a highly promising in-situ construction material. Accurately characterizing, understanding and predicting its performance under extreme environments are critical to the safety of lunar structures during construction and service. However, the existing research shows significant discrepancies and controversies. These arise from a series of challenges, i.e., 1) the inherent compositional complexity and variability of lunar regolith simulant, 2) the coupled effects of multiple factors, 3) a general reliance on qualitative and localized understanding of microstructure, 4) the lack of an underlying foundation for understanding the mechanisms that influence macroscopic performance, and 5) the reliance on simple statistical analysis of experimental data without establishing quantitative association models between parameters and performance. To address these challenges, this study was to propose an interpretable, cross-scale and data-driven research paradigm of "algorithmic deconstruction-performance prediction-mechanism analysis".

AALRS pastes were prepared using lunar regolith simulant XJ-1, alkaline activators (i.e., sodium hydroxide or sodium silicate), and water at a specified alkali content of 0.075 and a water-to-binder ratio of 0.3. To simulate extreme environments, the samples were exposed to a vacuum (i.e, 20 ℃ and 100 Pa) and a thermal vacuum (i.e., 90 ℃ and 100 Pa) for 1, 3, 7, 14 d and 28 d, respectively. Macroscopically, the compressive strength was measured in a WAW-1000 testing machine at a loading rate of 480 N/s. The energy dispersive X-ray spectroscopy (EDS) mappings of key elements (i.e., Si, Al, Na, Ca, Mg, and Fe) were acquired in an accelerating voltage of 15 kV, a working distance of 10 mm, a dwell time of 100 μs/pixel, a process time of 6, and a channel count of 2048.

A quantitative analysis framework for EDS mappings driven by unsupervised clustering algorithms was developed. Implemented in Python, this framework automates the establishment of an interpretable analysis paradigm using libraries such as OpenCV, scikit-learn, and scikit-image. To address underfitting, overfitting, and poor performance of basic ML algorithms in complex hypothesis spaces, a two-level Stacking ensemble model was proposed. The first level integrates six heterogeneous base learners like AdaBoost, XGBoost, CatBoost, SVR, MLPNN, and RF. The predictions from these base learners served as input features for the second level by a 5-fold cross validation strategy. This second level employs a linear regression model as a meta-learner. To further enhance the predictive accuracy of the model, its configuration was optimized via applying six hyperparameter tuning algorithms (i.e., RS, BO, PSO, GWO, SA, and TPE) to efficiently search the hyperparameter space.

The results of compressive strength reveal the disparities governed by both the activation system and environment. The AALRS-SS consistently outperforms the AALRS-SH. The AALRS-SS under HV environment achieves a peak 28 d strength of 32.9 MPa, which is 3.65 times higher than its AALRS-SH counterpart under the same conditions. In addition, the HV environment proves crucial for the strength development, compared to the V environment. To understand the mechanisms behind these macroscopic differences, a quantitative EDS mapping analysis is employed. The results show that AALRS-SS generally contains a higher area fraction of reaction products and correspondingly lower proportions of unreacted XJ-1 and porosity. This indicates a higher overall degree of reaction and a denser microstructure in the AALRS-SS, providing a direct explanation for its superior strength. The HV environment facilitates a more homogeneous phase assemblage, compared to the greater heterogeneity observed in the V environment. The elemental analysis based on the superpixel clustering further reveals that calcium remains largely inert. The gels in the AALRS-SH exhibit a higher aluminum content, while those in the AALRS-SS are characterized at a higher silicon content. Based on the quantitative EDS analysis, the microstructural characteristics and reaction mechanisms of AALRS under different environments and activation systems are elucidated.

To capture the complex, non-linear relationships among material composition, environmental conditions, and mechanical performance, a two-level Stacking ensemble model is proposed. The model tuned with RS is identified as an optimal choice for its excellent balance of high performance and rapid computation after comparing multiple optimization algorithms on both accuracy and computational efficiency. This selected Stacking-RS model is interpreted based on the SHAP analysis to reveal the underlying strength development mechanisms. The analysis confirms the strong, positive, and monotonic effects of AC, CA and IT. In contrast, it identifies a critical negative threshold for MPD around 50 μm and reveals a non-monotonic influence of W/B. The effect of SM is complex, with significant interactions with other features. A graphical user interface (GUI) is developed based on the Stacking-RS model, thus facilitating the efficient design of AALRS. Furthermore, this study establishes a cross-scale, data-driven analytical framework that integrates algorithmic deconstruction, performance prediction, and mechanistic interpretation, wherein the microstructural analysis and macroscopic prediction modules mutually complement and validate each other to enable an intelligent understanding and accurate prediction of AALRS performance under extreme environments.

AALRS-SS under the HV environment exhibited the maximum strength. Its 28 d strength reached 32.9 MPa, which was 3.65 times greater than that of AALRS-SH (9.0 MPa). This was attributed to the SS system forming a higher proportion of gel products and a denser microstructure.

A quantitative EDS image analysis framework based on GMM and SLIC clustering algorithms was established. Compared with the V environment, the HV environment significantly increased the proportion of reaction products and compositional homogeneity, while reducing porosity. The reaction degree of AALRS-SS was significantly higher than that of AALRS-SH.

The reaction mechanisms of AALRS-SH and AALRS-SS under extreme environments were revealed. Under the V environment, AALRS-SH showed a significantly increased porosity with Na enrichment inside the pores. Under the HV environment, gels precipitated on the XJ-1 particle surfaces, forming a product layer that hindered subsequent dissolution, leading to a porous structure, and low strength. In contrast, AALRS-SS, due to the presence of silicate polymers, provided numerous nucleation sites through depolymerization under the HV environment, forming a relatively dense microstructure. Under the V environment, it provided a bonding force through self-assembly gelation of silicate polymers.

A Stacking ensemble model optimized by RS was identified as the best-performing model, showing excellent accuracy, generalization, and efficiency (i.e., R=0.9482, RMSE=5.58 MPa, MAE=4.07 MPa, and t=0.77 min).

The SHAP analysis (global, local, and dependence) showed that AC, CA, and IT were the most significant positive contributing features. The negative impact of MPD became more significant beyond 50 μm. The effect of W/B was non-monotonic. A GUI was developed based on the Stacking-RS model to enhance its practicality.

A data-driven, cross-scale intelligent analysis paradigm of "algorithmic deconstruction-performance prediction-mechanism analysis" was proposed. Its core lay in the macro-prediction module, which was used for strength prediction and identifying key factors, and the microstructure module, providing direct experimental evidence to elucidate the underlying strength development mechanism.

Despite progress on applying data-driven methods to chemically activated aluminosilicate cementitious materials, several critical challenges remain. Firstly, the inherent complexity and variability of raw materials, combined with the absence of standardized design specifications, lead to high-dimensional and redundant datasets that compromise the model performance. To address this issue, it is necessary to establish standardized open databases covering precursor composition, reactivity, activator parameters, and curing conditions, as well as the knowledge-embedded hybrid models that incorporate geopolymerization mechanisms and thermodynamic constraints. Secondly, data scarcity is a pervasive challenge in this field, with most datasets characterized by small sample sizes, severely limiting model generalizability. This issue can be alleviated through multi-source data fusion, data augmentation techniques, and transfer learning for small-sample scenarios. Lastly, the existing inverse design relies primarily on a two-step prediction-then-optimization framework, whereas deep generative models for direct material design remain largely underexplored. Integrating these advanced models with reaction kinetics and microstructure evolution can be essential to realize end-to-end inverse design.

The evolution of artificial intelligence technology offers a significant potential for advancing chemico-activated aluminosilicate cementitious materials technology. This promises to serve as a robust reference for the deep integration of artificial intelligence into future material development. Future efforts should focus on developing standardized workflows, improving model interpretability and generalizability, as well as leveraging multi-scale simulations and deep generative models to accelerate material innovation. Data-driven methods will enable more accurate prediction and design of chemico-activated aluminosilicate materials due to these advancements, thereby contributing to the sustainable transformation of the construction industry.

Magnesium phosphate cement (MPC) has attracted recent attention due to its rapid setting, high early strength, and excellent bonding performance, making it a promising material for rapid repair and strengthening of concrete structures. In composite repair systems, the interfacial behavior between MPC-based repair materials and Portland cement concrete (PCC) substrates plays a decisive role in the overall structural performance, durability, and failure mode. However, the interface between MPC and PCC is often characterized by microstructural heterogeneity, the mismatch of mechanical properties, and the presence of initial defects, significantly reducing interfacial fracture resistance, especially under complex loading conditions. Nano-modification is widely recognized as an effective strategy to enhance the mechanical properties and microstructure of cement-based materials. Nanoparticles of silica (NS) can accelerate hydration, refine pore structure, and improve interfacial bonding through physical filling and chemical coupling effects due to its extremely high specific surface area and high pozzolanic reactivity. Despite extensive studies on the mechanical performance and modification mechanisms of NS-modified MPC (NS-MPC), systematic investigations on the interfacial fracture behavior between NS-MPC and PCC under mixed-mode loading conditions remain limited. It is thus essential to clarify the interfacial fracture mechanism and fracture resistance of NS-MPC/PCC systems under different fracture modes. In this study, asymmetric semi-circular bending beam (ASCB) specimens were employed to comprehensively investigate the mixed-mode interfacial fracture behavior of NS-MPC/PCC interface. The combined effects of initial crack length and fracture mix on the interfacial bonding strength, fracture parameters and nano-modification were investigated.

NS-MPC was prepared with dead-burned magnesia (M), potassium dihydrogen phosphate (P), NS, borax retarder (Bo), and deionized water (W). An orthogonal experimental design L9(3⁴) was adopted to optimize the mix proportion, considering four factors (i.e., Mg/P mass ratio, water-to-cement ratio (W/C), borax content (Bo/M), and NS content (NS/C)). The compressive and flexural strengths at 3 h and 1 d were selected as evaluation indices. According to the standard GB/T 17671—2021, the specimens NS-MPC of 40 mm × 40 mm × 160 mm were prepared to conduct flexural and compressive strength tests. Based on the results of orthogonal analysis, the optimal mix proportion of NS-MPC was determined and further compared with conventional MPC at different curing ages.

The interfacial fracture performance was evaluated by ASCB test, and the pure mode Ⅰ, pure mode Ⅱ and mixed mode Ⅰ/Ⅱfracture were achieved via adjusting the support position. Different crack length ratios (i.e., a/R=0.1, 0.2, and 0.3) and mixed mode parameters were selected to systematically investigate the mechanical response of the NS-MPC/PCC interface under different fracture modes. The geometric factors of the ASCB specimens were calibrated by the finite element method to achieve the quantitative analysis of the interfacial fracture parameters.

The microstructure and elemental composition of the interface were characterized by scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS). After the samples were dried at a low temperature to terminate hydration and treated for conductivity, the interface morphology and the distribution characteristics of hydration products were analyzed under high vacuum conditions.

Based on the results of orthogonal experimental design, the mechanical properties of NS-MPC are systematically investigated. The optimal mix is determined to be M/P of 2.5/1, W/C of 0.14, Bo/M of 0.08, and NS/C of 0.04. NS-MPC exhibits significantly enhanced mechanical properties. The compressive and flexural strengths at 3 h increase to 47.3 MPa and 7.3 MPa, respectively, and increase to 76.5 MPa and 8.6 MPa at 1 d. From 7 to 28 d, the compressive strength increases slowly from 81.3 MPa to 88.7 MPa, and the flexural strength increases from 9.1 MPa to 9.4 MPa, which still maintains a high value. The flexural-to-compressive strength ratio of NS-MPC is 9%, which is higher than that of pure MPC (i.e., 5%). It is indicated that NS can improve the strength and the toughness.

The interface bonding strength of NS-MPC/PCC decreases with the increase of a/R. This is consistent across all the fracture modes (i.e., pure mode Ⅰ, pure mode Ⅱ, and mixed mode Ⅰ/Ⅱ). The interface bonding strength corresponding to pure mode II fracture is the maximum value, while that of pure mode I is the minimum value. The force-displacement response shows that the interface fracture process is dominated by brittle failure. The increase of crack length significantly reduces the peak load-bearing capacity and deformation ability, and promotes the crack to expand dominantly from the interface to bend and branch, especially at larger a/R rates.

The fracture parameter analysis indicates that the interface fracture toughness is highly sensitive to the fracture modes and a/R. As the mix of fracture increases, the effective fracture toughness shows an overall downward trend. The most unfavorable fracture condition occurs in the mixed mode I/II fracture, where the interface crack resistance is the lowest. Pure mode I fracture causes a damage to the interface, and the larger crack length significantly weakens the crack resistance of the pure mode II interface.

The microscopic analysis reveals that the interface of approximately 20 μm wide is formed between NS-MPC and PCC. The hydration products K-struvite can occur in the interface. Moreover, elements Mg, P, and Si appear certain degrees of infiltration and enrichment on the both sides of the interface. The addition of NS effectively improves the interface reaction activity and microstructure densification.

NS-MPC demonstrated significantly enhanced compressive and flexural strength, indicating that NS could effectively promote the hydration reaction and structural densification. The results of the ASCB test indicated that the bonding strength of the NS-MPC/PCC interface decreased as a/R increased. The interface fracture process showed a significant nonlinear characteristic. The pure mode I fracture was the most unfavorable. The mixed fracture analysis revealed the nonlinear relationship between the effective fracture toughness and the mixity of fracture. The SEM images and EDS spectra further confirmed that NS enhanced the chemical bonding and crack resistance at the interface via promoting the formation and filling of Si-P-Mg composite hydration products in the interface.

To accelerate the progress of pozzolanic reaction of the mineral admixtures, such as fly ash (FA) and metakaolin (MK), dispersed nano-sized hydrated calcium silicate-polycarboxylate superplasticizer (C-S-H-PCE) were employed to regulate their early-age pozzolanic reactivity in saturated Ca(OH)₂ solution. The ion dissolution process of the mineral admixtures was investigated through conductivity determination, inductively coupled plasma optical emission spectroscopy (ICP), and pH value measurement. The pozzolanic reaction products of the mineral admixtures were studied by chemically bound water content analysis, Fourier transform infrared spectroscopy (FTIR), and Brunner-Emmett-Teller (BET) based on nitrogen adsorption. The enhancement effect of nano-sized C-S-H-PCE on the cement-mineral mortar was analyzed by compressive strength, and the regulatory mechanism underlying the pozzolanic reactivity was discussed.

As a kind of commercial product, nano-sized C-S-H-PCE was provided by Jiangsu Sobute New Materials Co., Ltd., China. The synthetic C-S-H was dispersed via the adsorption of the comb-like PCE on its surface, and then the final C-S-H-PCE with the particle sizes of 50-100 nm was obtained via ultrasonic treatment. The mass concentration of nano-sized C-S-H-PCE was 12%. The conductivity, inductively coupled plasma optical emission spectroscopy (ICP), pH value measurement, chemical bound water analysis, Fourier transform infrared spectroscopy (FTIR) and N₂ adsorption were applied to characterize the samples in this investigation.

As the dosage of nano-sized C-S-H-PCE increasing, the conductivity of MK and FA systems decreased, indicating that the concentration of free ions in the system gradually decreased, and the conductivity of MK system decreased more significantly in relative to the FA system with the addition of nano-sized C-S-H-PCE. The pH values of both MK and FA systems rapidly decrease with time, and the corresponding OH^- consumption increases with the addition of nano-sized C-S-H-PCE after 24 h reaction. The results of conductivity and pH value indicated that nano-sized C-S-H-PCE promoted ion dissolution from mineral admixtures and thus enhancing the pozzolanic activity.

As the dosage of nano C-S-H-PCE increases, the chemically bound water content corresponding to both MK and FA systems gradually increases, and the chemically bound water content corresponding to MK system was significantly higher than that of corresponding to FA system. The FTIR spectra indicated that the polymerization degree of silicon oxygen tetrahedra in mineral admixtures MK and FA in saturated Ca (OH)₂ solution continuously increased with the addition of nano-sized C-S-H-PCE, and the corresponding Si—O bond stretching vibration peak number and integral area of MK system were higher. The results of nitrogen adsorption showed that the nitrogen adsorption capacity and specific surface area of MK and FA samples increase with the increase of nano-sized C-S-H-PCE content, promoting the progress of pozzolanic reaction.

The mechanism of nano C-S-H-PCE regulating the pozzolanic reactivity of mineral admixtures was that nano-sized C-S-H-PCE reduced the free energy ∆G required for the nucleation barrier and nucleation of C-S-H gel, that is, the ions dissolved from mineral admixtures can nucleate and crystallize on the surface of nano-sized C-S-H-PCE at a lower saturation concentration, thus quickly forming hydration products. The formation of hydration products on the surface of nano-sized C-S-H-PCE reduced the ion concentration in the solution, leading to an increase in ion unsaturation and further promoting the ion dissolution from mineral admixtures, thereby accelerating the pozzolanic reactivity of mineral admixtures.

The results showed that the conductivity and pH values of the MK and FA systems both could decrease as the amount of nano-sized C-S-H-PCE increased, accompanying with an accelerated breakage of Si—O and Al—O bonds. After 24 h reaction, the chemically bound water content gradually increased, while the polymerization degree of silicate tetrahedrons continuously increased. In addition, the nitrogen adsorption capacity and specific surface area of the samples gradually expanded. The aforementioned results indicated that nano-sized C-S-H-PCE accelerated the ion dissolution from mineral admixtures and the formation of pozzolanic reaction products. Nano-sized C-S-H-PCE reduced the nucleation energy barrier of C-S-H gel, facilitating the nucleation and crystallization of ions dissolved from mineral admixtures on the surface of nano-sized C-S-H-PCE, thereby accelerating the formation of hydration products. This process could promote the dissolution of ions from mineral admixtures, ultimately enhancing their pozzolanic reactivity.

Foamed lightweight soil (FLS) is a cementitious geotechnical material prepared via mixing cement paste with preformed foam and sometimes incorporating additional aggregates. Its density and mechanical strength can be tailored via adjusting the foam content. FLS is widely employed in embankment construction and backfill applications due to its capacity to substantially reduce fill load and settlement. The strength of FLS generally exhibits an exponential relationship with density, which is affected by its internal pore structure. During casting, unstable bubbles can lead to non-uniform pore distribution. Enhancing foam stability is therefore crucial for pore structure optimization. Additives such as stabilizers and viscosifiers are commonly used to improve foam stability, but they can concurrently reduce foaming capacity. SiO₂ nanoparticles have a potential in modifying interface properties and inhibiting foam decay. However, its influence on the pore structure of FLS and a systematic comparison with conventional FLS remain to be insufficiently investigated. This study was to examine how SiO₂ nanoparticles could affect foam stability and enhance the mechanical properties and pore structure of FLS. The effects of morphology and content of SiO₂ nanoparticles were analyzed through bubble density measurements, foam bleeding tests, and optical microscopy. The mechanical performance and pore structure characteristics were evaluated by uniaxial compression tests and X-ray computed tomography, providing theoretical and practical insights for the application of nanomaterials in FLS.

FLS specimens were prepared with P·C 42.5 composite Portland cement, plant-protein-based foaming agent, nanoparticles of SiO₂, and tap water. SiO₂ nanoparticles were incorporated at different mass ratios (i.e., 1‰, 3‰, and 5‰) by cement mass. Three target wet density groups were designed at 1.5, 1.1 g/cm³, and 0.6 g/cm³. The mixtures were cast in cubic molds with the size of 70.7 mm, demolded after 24 h, and cured under standard conditions (i.e., (20 ± 2) ℃, RH > 95%) for 7, 28 d, and 60 d, respectively. The foam bleeding rate was determined via measuring the volume of liquid expelled from freshly prepared foam standing in air at specified intervals, calculated as a ratio of expelled liquid to initial foam volume. The bubble morphology and foam decay were analyzed by optical microscopy. The mechanical properties of hardened specimens were evaluated by uniaxial compression tests, while the pore structure was characterized using X-ray computed tomography (X-CT). All the experiments were repeated at least three times to ensure accuracy and reproducibility.

The results of foam stability test demonstrate that the incorporation of SiO₂ nanoparticles significantly reduces the foam bleeding rate and prolongs the bubble dissipation duration in FLS, while simultaneously promoting more uniform bubble size distribution and regular morphological characteristics. These results confirm the effective enhancement of bubble stability through the modification of SiO₂ nanoparticles. Note that FLS specimens incorporating irregularly-shaped SiO₂ nanoparticles exhibit superior comprehensive performance due to the larger contact area between irregular particles and bubble interfaces, compared to their spherical counterparts. For FLS specimens with irregular SiO₂ nanoparticles, the optimal incorporation ratio is 3‰ (in mass), achieving a 1 h bleeding rate of 23% in the foam system, representing a 70% reduction, compared to the control group without SiO₂ nanoparticles. This enhancement mechanism involves the adsorption of surfactant molecules onto the surfaces of the nanoparticles, where appropriate concentrations typically form monolayer adsorption films that convert the surface of originally hydrophilic to hydrophobic. However, the excessive surfactant adsorption leads to a bilayer formation, reverting the nanoparticle surface to hydrophilic and consequently diminishing a stabilization effectiveness. The results of uniaxial compression tests reveal that the peak strength of FLS in different density groups generally follows an initial increase and a subsequent decrease trend with increasing the content of SiO₂ nanoparticles, reaching the maximum values at an incorporation ratio of 3‰. The results of X-CT scanning further indicate that the incorporation of SiO₂ nanoparticles generally reduces the internal pore volume of FLS, while demonstrating that the pore volume has a decreasing-then-increasing pattern as the content of SiO₂ nanoparticles increases. Moreover, the addition of SiO₂ nanoparticles contributes to more regular pore morphology and more uniform pore spacing distribution in FLS. The specimens incorporating the SiO₂ nanoparticles of 3‰ and 5‰ exhibit more regular internal pore structures, compared to that with SiO₂ nanoparticles of 1‰.

The incorporation of SiO₂ nanoparticles enhanced foam density, while reducing foaming volume and bleeding rate. The microscopic analysis revealed that SiO₂ nanoparticles could thicken the liquid film, increase foam sphericity, and reduce the connectivity of Plateau borders, thereby inhibiting gas escape. The 3‰ content of irregular SiO₂ nanoparticles demonstrated an optimal foam quality. At identical curing ages, the uniaxial compressive strength of FLS in different density groups exhibited a characteristic trend of initial increase and subsequent decrease as the content of SiO₂ nanoparticles increased, obtaining the maximum value at the content of 3‰. Compared to the specimens without SiO₂ nanoparticles, the 28 d and 60 d compressive strengths in three density groups with SiO₂ nanoparticles could increase from 7% to 51%.For the specimens without SiO₂ nanoparticles, the 28 d FLS specimens with SiO₂ nanoparticles displayed reduced individual pore volumes in all density groups, and the average pore volume was decreased by 28%-46%. Concurrently, the pore sphericity increased and accompanied with a narrow size distribution, and the pore spacing became more uniform. The modification of SiO₂ nanoparticles could prove particularly effective in optimizing the pore structure of low-density FLS.

Subsea shield tunnels serve as critical passages connecting two sides of sea areas and play a vital role in supporting the development of a maritime nation and the coordinated integration of land and marine resources. In recent years, the construction of subsea shield tunnels has been rapidly developed, with representative projects including the Lion Rock Subsea Tunnel of the Guangzhou-Shenzhen-Hong Kong High-Speed Railway, the Qingdao Jiaozhou Bay Second Subsea Tunnel, and the Jintang Subsea Tunnel of the Ningbo-Zhoushan Railway, China. However, during both the construction and service phases of shield tunnels, some factors such as construction practices, aggressive environmental conditions, and uneven settlement can lead to cracking, water leakage, and even corrosion of reinforcement in segment concrete. Insufficient toughness and crack resistance of shield segment concrete can compromise the long-term operational safety of shield tunnels. During service, shield segments are subjected to high water pressure and strongly corrosive ionic environments. To enhance the durability of concrete, erosion-inhibiting materials like coatings, migrating corrosion inhibitors, and internally incorporated nanomaterials are often used during prefabrication. These materials significantly improve the durability of segment concrete, and their effect on crack resistance and toughness remains insufficiently explored.

This study was to investigate the enhancement of fracture toughness in shield segment concrete through the use of erosion-inhibiting materials, including silica nanoparticles, migrating corrosion inhibitors, and nano-sized precursors. In addition, the mechanism by which these materials could affect the microstructure of concrete was also analyzed from the perspective of interfacial transition zone optimization.

In this study, the fracture behavior of concrete was investigated by a three-point bending test method. During the loading process, the displacement of the specimens was monitored using digital image correlation (DIC). Also, the interfacial transition zone of the concrete was analyzed via backscattered electron imaging analysis. The fracture toughness of the concrete microstructure was characterized by nano-indentation scratching technique.

Based on the implementation of erosion inhibition measures, the fracture toughness of concrete is notably enhanced. In concrete specimens treated with silica nanoparticles, migrating corrosion inhibitors, and nano-precursors at various dosages, the crack initiation toughness is increased by 22.6%-48.9%, the instability toughness is improved by 18.8%-49.8%, and the fracture energy is elevated by 0.50-1.59 times. The improvement in fracture toughness is closely associated with the physical and chemical interactions of these materials within the concrete. The silica nanoparicles coating penetrates into the cement matrix, promotes a further hydration, and provides additional nucleation sites. Its reaction products effectively fill pores and reduce the number of initial defects and microcracks, resulting in a denser cement matrix with improved strength and deformation properties. The active molecules in the migrating corrosion inhibitor form coordination bonds with calcium ions in cement hydration products, anchoring themselves within the hydration phases. The main components migrate to the steel-concrete interface, forming a protective adsorbed layer that delays reinforcement corrosion. The nano-precursor material undergoes an ion-exchange reaction during cement hydration, in-situ generating calcium carboxylate compounds. These hydrophobic substances are uniformly dispersed in nanoscale within the C-S-H gel network, significantly enhancing the interfacial wetting behavior via reducing the surface energy of pore channels. Furthermore, the nanoparticles released from the precursor decomposition fill internal pores, block connected voids, optimize pore size distribution, and increase matrix density.

The application of erosion-inhibiting materials significantly enhanced the fracture toughness of the concrete. In shield segment concrete treated with nano-silica coating, migrating corrosion inhibitor, and nano-precursors at varying dosages, the peak load was increased by 16.8%-37.1%, crack initiation toughness was improved by 22.6%-48.9%, unstable fracture toughness was increased by 18.8%-49.8%, and fracture energy was enhanced by 0.50-1.59 times, compared to the reference group. Before peak load, the migrating corrosion inhibitor and nano-silica coating both contributed to a reduction in the Crack Tip Opening Displacement (CTOD), while the nano-precursor was effective in lowering CTOD values at all loading stages. During the post-peak phase (specifically at 30% beyond peak stress), the CTOD was decreased by 31.8%-47.2% with nano-precursor incorporation. The erosion-inhibiting materials also improved the properties of the Interfacial Transition Zone (ITZ). The ITZ thickness of the materials treated with silica nanoparticles coating and migrating corrosion inhibitor could be reduced by 7.1% and 6.5%, respectively.

C-S-H/PCE nanocomposites represent a significant advancement over conventional accelerators, offering a synergistic approach to enhance early-age strength and durability in cement-based materials. This review systematically discusses the fundamental understanding of their synthesis, the complex interplay between C-S-H and PCE, their unique nucleation seeding mechanism, and the factors governing their performance. The development of novel metal-doped nanocomposites and the exploration of synergistic combinations with other admixtures expand their potential and effectiveness across diverse cementitious systems. These materials demonstrate clear advantages in enabling rapid strength development, improving microstructure, and facilitating sustainable construction practices with reduced clinker content. Despite these advances, several critical gaps remain. A deeper understanding of the dynamic evolution, migration, and in-situ behavior of these nanocomposites within the complex chemical environment of hydrating cement paste is urgently needed. Future research should focus on imparting multifunctionality to these materials, such as self-healing or responsive properties, to create smarter and more durable concrete. The development of cost-effective, scalable, and sustainable synthesis methods, potentially utilizing industrial by-products, is essential for widespread adoption. Leveraging advanced computational tools like molecular dynamics simulations and machine learning can be instrumental in accelerating the rational design and optimization of the next generation of these high-performance nanomaterials.

Despite significant achievements, several challenges remain in understanding and optimizing nanofiller-modified interfaces. First, the existing characterization techniques are limited in their ability to capture the dynamic evolution of interfacial structures at high spatial and temporal resolution. Most methods provide only static, two-dimensional information, making it difficult to fully understand multi-scale and multi-physics processes. Second, the inherent heterogeneity and discontinuity of interfaces complicate quantitative modeling. The existing models often describe average behavior, but fail to capture local weak zones or nanoscale force transfer mechanisms. Third, the introduction of multifunctional nanofillers increases the complexity of multi-field coupling (i.e., mechanical, thermal, chemical, and electrical), posing challenges for unified theoretical frameworks. Finally, the large-scale engineering application of nanomodified interfaces is hindered by some issues such as nanofiller dispersion, cost, and performance stability under complex service conditions.

Future research should focus on several key directions. Advanced characterization techniques with a high spatiotemporal resolution, such as synchrotron radiation-based imaging and X-ray computed tomography, should be developed to enable real-time tracking of interfacial evolution. The integration of artificial intelligence and machine learning can facilitate high-throughput data analysis and predictive modeling, enabling a quantitative design of interfacial properties. Multi-scale theoretical frameworks that link material composition, microstructure, and macroscopic performance are needed to achieve accurate prediction and optimization. Also, efforts should be made to translate laboratory findings into practical engineering applications, particularly in emerging areas such as 3D-printed concrete, recycled concrete, concrete repair, and self-healing cement-based materials.

In summary, nanomodification is demonstrated to effectively tailor interfaces in cement-based materials and their structures, while the enrichment effect and nano-core effect is proposed to clarify the underlying mechanisms. These advances provide a foundation for mitigating the inherent weakness of interfaces. Despite these advances, some challenges remain in terms of characterization, quantitative modeling, and large-scale application. Nevertheless, continued progress in nanotechnology, multi-scale analysis, and intelligent design is expected to enable the rational design and engineering of high-performance, durable, and multifunctional cement-based materials and their structures.

The western region of China is characterized by high altitude, significant diurnal temperature variations, and extensive saline soil distribution. Under the coupled effect of freeze-thaw cycles and salt attack, the service life of local concrete structures decreases significantly, resulting in substantial economic losses. Although steel fiber-reinforced ultra-high performance concrete (UHPC) exhibits an enhanced freeze-thaw resistance, it faces a challenge of underutilization of fiber strength due to the limitations imposed by fiber-matrix interface degradation and inherent matrix risks. In this study, ethylenediaminetetraacetic acid (EDTA) treated steel fibers replaced conventional steel fibers to enhance the mechanical properties and freeze-thaw resistance of UHPC via improving the fiber-concrete matrix interface performance.

In this study, steel fibers were modified using the EDTA method. First, 0.05 mol/L CaCl₂ and 0.05 mol/L Na₂CO₃ solutions were prepared separately, and 1% EDTA was added to Na₂CO₃ solution. Subsequently, the steel fibers were immersed in a mixed solution of CaCl₂, Na₂CO₃, and EDTA for 6 h. Finally, the fibers were washed with distilled water. Based on the measured concentrations of SO₄^2- and Cl- in saline soil from Golmud, this study employed a 133.6 g/L NaCl and 19.5 g/L MgSO₄ solutions to simulate the corrosive environment for freeze-thaw tests. This study investigated the effect of combined salt attack-freeze-thaw cycles on the fiber-matrix interfacial bond strength, pull-out energy, relative dynamic elastic modulus, mass loss ratio, compressive strength, and flexural strength of UHPC. The phase composition and microstructure were characterized by X-ray diffraction (XRD), mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM), revealing the microscopic mechanisms of performance degradation. In addition, a freeze-thaw damage model for UHPC was also established based on the two-parameter Weibull distribution function.

The EDTA modification induces a longitudinal exfoliation on steel fiber surfaces, resulting in a significantly increased roughness. The interfacial bond strength and pull-out energy are enhanced by 22.2% and 66.7%, respectively. Correspondingly, the compressive strength and flexural strength of UHPC are improved by 2.5% and 12%. The enhanced fiber-matrix interfacial bond strength and pull-out energy increase the ultimate tensile stress required for unstable propagation of microcracks in UHPC, effectively inhibiting the initiation and development of microcracks. This mechanism thereby enhances its freeze-thaw resistance. After 500 cycles of water-freezing and salt solution freezing tests, the EDTA-treated steel fiber-reinforced UHPC demonstrates significantly superior performance, compared to conventional steel fiber UHPC in all critical metrics i.e., interfacial bond strength, pull-out energy, compressive strength, flexural strength, relative dynamic elastic modulus, mass loss ratio, and porosity. After 500 freeze-thaw cycles in water, the bond strength of plain steel fiber UHPC and modified steel fiber UHPC are decreased by 40.4% and 27.8%, respectively. Their pullout energy are declined by 29.7% and 50.6%, respectively. The relative dynamic elastic modulus are decreased by 5.26% and 4.57%, respectively. The masses are reduced by 0.46% and 0.45%, respectively. The compressive strengths are decreased by 16.6% and 16%, respectively, and the flexural strengths are decreased by 26.1% and 20.6%, respectively. After 500 salt solution freeze-thaw cycles, the bond strength of plain steel fiber UHPC and modified steel fiber UHPC are decreased by 41.3% and 39.3%, respectively. The pullout energies are declined by 40.5% and 54.2%, respectively. The relative dynamic elastic modulus are dropped by 5.44% and 5.25%. The masses are reduced by 0.5% and 0.48%. The compressive strengths are decreased by 21.4% and 20.3%, respectively, and the flexural strengths are decreased by 29.3% and 25.2%, respectively. The XRD patterns reveal that after 500 salt solution freeze-thaw cycles, some expansive products such as ettringite and gypsum are formed within the UHPC. The expansive stresses generated by these products induce crack initiation and propagation, thereby degrading the performance of the UHPC. MIP indicates that freeze-thaw cycles result in an increased porosity in UHPC. In both water-freezing and salt solution freeze-thaw environments, the porosity of modified steel fiber UHPC is lower than that of plain steel fiber UHPC. SEM images reveal that during debonding of modified steel fibers from the matrix, a greater amount of weak matrix material adhering to the fiber surfaces is carried away. This phenomenon demonstrates that modified steel fibers establish a stronger interfacial bonding with the cementitious matrix. Based on the predictions by the Weibull model, the critical freeze-thaw cycles for EDTA-treated and conventional steel fiber-reinforced UHPC are predicted to be 1610 and 1270 cycles under water-freezing exposure, and 1220 and 1150 cycles under salt solution-freezing exposure, respectively. The results indicate that in the both environments, fiber modification treatment can effectively enhance the freeze-thaw resistance of UHPC and significantly improve its durability.

The results demonstrated that EDTA modification treatment enhanced the strength and fiber-matrix interfacial bonding performance of UHPC, inhibited micro-crack propagation, thereby slowing the deterioration rate of properties such as relative dynamic elastic modulus and strength during freeze-thaw cycles, reduced mass loss, and improved the freeze-thaw resistance of UHPC. The freeze-thaw damage model predicted that EDTA-modified steel fiber UHPC could have a 26.8% increase in freeze-thaw cycle resistance under water-freezing conditions and a 6.1% improvement under salt solution-freezing environments, compared to conventional steel fiber UHPC. This study could offer a reference for the durability de-sign of UHPC structures in cold saline soil regions of western China.

Stray current leakage poses a significant threat to the service life of underground electrified engineering structures. Although ultra-high-performance concrete (UHPC) exhibits superior durability, compared to ordinary concrete, the corrosion mechanism of internal steel fibers induced by stray current remains unclear, thus restricting its broader application in underground environments. Under the influence of stray current, chloride ion transport in UHPC is governed by the combined effects of free diffusion, electromigration, and changes in pore structure caused by steel fiber corrosion. The mechanisms of free diffusion and electromigration are extensively studied, but the influence of steel fiber corrosion on chloride ion transport is not thoroughly investigated. In particular, the impact of corrosion-induced microstructural changes on chloride migration is not quantitatively assessed, and the micro-morphological characteristics of steel fiber corrosion under stray current are not fully revealed.

The materials used included Portland cement (P·II 52.5), silica fume (particle size: 0.1-0.2 μm), fly ash (Type F), quartz powder (particle size: 20-100 μm), quartz sand (particle size: 450-900 μm), a superplasticizer, and steel fibers. The steel fibers were added at a volume fraction of 2%, with a length of 8 mm and a diameter of 0.12 mm. The microstructural characteristics of steel fiber corrosion and the chloride ion transport mechanism in UHPC under the influence of stray current were determined by rapid chloride migration test (RCM), water absorption test, atomic force microscopy (AFM), and X-ray computed tomography (X-CT), respectively. The electrochemical corrosion of UHPC was simulated via the RCM test, and the changes in effective porosity after corrosion were determined via the water absorption test. The micro-morphological features of corroded steel fibers were characterized by AFM, and the corrosion-induced microcrack propagation and changes in porosity within the UHPC matrix were analyzed by X-CT.

The corrosion pits on the surface of the steel fibers are primarily distributed in the range of -250 to 200 nm at an external voltage of 30 V. As the voltage increases to 50 V, the pit depth expands to -430 to 700 nm, and it further extends to-580 to 770 nm at 70 V. Correspondingly, the effective porosity of UHPC increases from 0.50% to 0.58% at 30 V, from 0.60% to 1.31% at 50 V, and from 0.58% to 1.88% at 70 V.

The expansion of corroded steel fibers induces the formation and propagation of microcracks within the UHPC matrix, leading to an increase in apparent porosity. At different applied voltages of 30 V, 50 V, and 70 V, the apparent porosity increases to 1.49-1.95%, 1.50-2.43%, and 1.47-2.96%, respectively. The effective chloride diffusion coefficient measured is 3.68×10^-12 m²/s at 30 V, increases to 9.11×10^-12 m²/s at 50 V, and further increases to 28.3×10^-12 m²/s at 70 V. These results demonstrate that stray current significantly accelerates the chloride ion transport in UHPC. Neglecting the effect of porosity variation can result in a significant overestimation of the service performance of UHPC structures within their intended design life.

Stray current induced changes in the micro-morphology of steel fibers, with maximum pit depths concentrated in the range of -580 nm to 770 nm. The corrosion became more severe closer to the exposed surface, potentially causing a fracture of the steel fibers and a complete loss of their load-bearing capacity. The results of water absorption tests indicated that stray current increased the effective porosity of UHPC. This increase could be primarily attributed to the expansion of corroded steel fibers, leading to microcrack propagation within the UHPC matrix. The apparent porosity caused by steel fiber corrosion was quantified. In specimens unaffected by stray current, the apparent porosity ranged from 1.47% to 1.56%. A higher applied voltage led to more significant changes in apparent porosity.

Sulphosilicate clinker is considered as a low-carbon cementitious material due to its high sulfur/silicon contents, low sintering temperature and low carbon emissions. Despite its promising performance and environmental advantages, the feasibility and mechanism of incorporating supplementary cementitious materials such as slag powder into sulphosilicate cement remain unclear. This study was to investigate the effects of slag powder and quicklime into sulphosilicate cement on its hydration behavior and mechanical performance.

The sulphosilicate clinker primarily comprised Belite (C₂S), Yeelimite (C₄A₃$) and Ternesite (C₅S₂$), with minor amounts of calcium sulfate and ferrite phases. Four types of cement were prepared, i.e., the reference (i.e., sample A-1), 30% slag powder (i.e., sample A-2), 30% slag powder with 1.5% quicklime (i.e., sample A-3) and 30% slag powder with 3% quicklime (i.e., sample A-4). The cement paste was analyzed for pH value, setting time, strength and hydration production at different ages of 3, 7 d and 28 d. The hydration products, microstructure evolution and thermodynamic behavior were determined by X-ray diffraction (XRD), thermogravimetric and differential thermal analysis (TG-DTG), scanning electron microscopy (SEM) and isothermal calorimetry, respectively.

The results indicate that the addition of 30% slag powder (sample A-2) extends the initial setting time by 12 min and improves workability (9.5% increase in flow), but significantly reduces early compressive strength due to the dilution effect thus diminishing active mineral content and delaying hydration product formation. The sample A-2 exhibits a 40% and 38% reduction in compressive strength at 3 d and 28 d, respectively, compared to the reference. The incorporation of quicklime significantly changes pH value in early hydration stages and promotes a pozzolanic reactivity of the slag. Compared to the sample A-2, the sample A-3 achieves a 35% increase in 28 d compressive strength (i.e., 46.3 MPa) meets the 42.5-grade requirement. However, the sample A-4 exhibits less compact microstructure and diminishes strength development, compared to the sample A-3, indicating that excessive quicklime can hinder mineral reactivity and hydration kinetics.

The analysis of isothermal calorimetry shows distinct hydration profiles among the cement systems. The samples A-1 and A-2 display two distinct peaks (initial wetting and C₄A₃$ hydration), while the samples A-3 and A-4 show only one sharp early peak due to the exothermic quicklime hydration, with later stages subdued due to an inhibited mineral reactivity. The XRD patterns reveal that the ettringite content in the sample A-3 increases from 32% at 3 d to 51% at 28 d, which is substantially higher than that of the sample A-2 (i.e., from 26% to 42%). In contrast, the sample A-4 plateaues 33% ettringite at 28 d. The TG-DTG analysis and bound water quantification reinforce these findings, showing that the sample A-3 has the maximum cumulative increase in hydration products over time among the blended systems. The SEM images confirm that the early formation of ettringite is suppressed, and the resulting ettringite transforms from dense columnar crystals in the sample A-1 to finer needle-like structures in the sample A-2, indicating a weaker skeletal framework and less compact microstructure. The sample A-3 develops a denser network of interlocked hydration products, while the sample A-4 shows persistent voids and unreacted particles, illustrating a delicate balance required for optimal alkaline activation.

This study demonstrated that the combination of slag powder and quicklime content could synergistically improve the mechanical performance and sustainability of sulphosilicate cement. At a 30% replacement level, slag powder significantly extended the initial setting time. However, the dilution effect led to a reduction in active mineral content, causing the amount of ettringite at 3 d and 28 d to decrease by 23% and 17%, respectively. This corresponded to a decline in compressive strength by 40% at 3 d and 38% at 28 d. The microstructural analysis revealed that the addition of slag powder could cause the morphology of ettringite to transform from columnar to needle-like/rod-like (with an aspect ratio > 10), and a higher residue of unhydrated slag particles led to a more porous microstructure. An appropriate amount of quicklime enhanced the alkalinity and properties of sulphosilicate cement. At a dosage of 1.5%, quicklime promoted slag hydration and increased the strength of the samples. The ettringite content in these samples was increased by 6% at 3 d and 10% at 28 d, while the chemically bound water content was increased by 6.1% and 4.5%, respectively. Consequently, the 28-d compressive strength was increased by 35%. In addition, distinct etching marks appeared on the slag surfaces along with the formation of a small amount of needle-like ettringite in 7 d samples with 1.5% quicklime, enhancing the density of the composite system. Conversely, a 3% quicklime dosage significantly inhibited the hydration of C₄A₃$ and C₅S₂$. The resulting excessive Ca(OH)₂ delayed the second hydration heat release peak to 40 h and reduced the total cumulative heat to 116.76 J/g.

The combined use of slag powder and quicklime profoundly could affect sulphosilicate cement performance. Incorporating 1.5% quicklime, 30% slag powder and an appropriate amount of anhydrite into the sulphosilicate clinker could allow for the preparation of cement that meeted the 42.5 strength grade. However, when slag was used alone, its activity was difficult to be activated due to the insufficient alkalinity of the cement liquid phase. Thus, it was not recommended as a standalone mineral admixture for this type of cement. Quicklime could significantly improve alkalinity, the performance of the cement was highly sensitive to its dosage. The optimal quicklime content could be determined based on the specific mineral composition of the clinker or the final cement blend.

The cement industry contributes approximately 8% of global CO₂ emissions, with each ton of clinker production for releasing nearly one ton of CO₂. Mineral admixtures such as slag, fly ash, and silica fume become essential for developing sustainable concrete, enabling a partial replacement of cement (i.e., 15%-45%, mass fraction), while enhancing durability through pozzolanic reactions. However, high-volume substitutions of exceeding 30% critically depress a pore solution alkalinity from pH value 13.3 to 12.5-12.8, destabilizing passive films on conventional carbon steel (LC) and elevating corrosion risks by 3-5 times. This alkalinity reduction has severe threats to structural integrity in marine environments and deicing salt exposure, driving global infrastructure repair costs beyond $2.5 trillion annually. A Cr10Mo1 alloy corrosion-resistant steel (LA) was developed, having a chloride threshold 10 times greater than that of LC in simulated pore solutions. However, its passivation behavior in realistic cement-mineral admixture systems with synergistic interactions of Ca²⁺, SO₄^2-, and Al³⁺ ions remains unexplored. In this study, the passivation mechanisms of the steel in complex paste extracts were investigated to provide foundational data for designing durable and sustainable infrastructure.

The passivation kinetics were analyzed by electrochemical techniques like linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS), and Mott-Schottky analysis. The LA and LC electrodes (φ10 mm×10 mm) were polished to a 2000-grit finish and immersed for 10 d in extracts derived from cement pastes containing different mineral admixtures of 0%, 15%, 30%, and 45% (slag/fly ash = 1∶1), designated as 0SF, 15SF, 30SF, and 45SF. The paste extracts were prepared with P·O 42.5 cement at a water-to-binder ratio of 1∶1, cured at 25 ℃ for 14 d, filtered through 0.45 μm membranes, and stored in nitrogen to prevent carbonation. The measured pH values reduced progressively from 13.4 (0SF) to 12.5 (45SF). Electrochemical testing utilized a three-electrode cell with a platinum counter electrode and a saturated calomel reference electrode (SCE). LPR scans (±20 mV vs. open-circuit potential at 0.166 7 mV/s) and EIS measurements (10 mV amplitude, 0.01-1.00×10⁴ Hz frequency range) were conducted after passivation. The Mott-Schottky analysis was performed at 1000 Hz with potential sweeps from -1.5 V to +0.25 V vs. SCE (50 mV/s). Equivalent circuit modeling (R_s-[CPE_dl-R_ct]-[CPE_f-R_f]) was used to fit the EIS data to resolve solution resistance (R_s), passive film properties (CPE₁, R₁), and charge transfer resistance (CPE₂, R₂). X-ray photoelectron spectroscopy (XPS) was used to characterize compositional gradients by an Axis Ultra DLD spectrometer with Al K_α radiation (1486.6 eV) and Ar⁺ sputtering (0.02 nm/s). The spectra of Fe 2p₃/₂ and Cr 2p₃/₂ were deconvoluted via the Gaussian-Lorentzian peak fitting.

The steels of LA and LC both can be effectively passivated in all extracts, while demonstrating fundamentally divergent responses to increasing mineral admixture content. The LA displays significantly an enhanced passivation, evidenced by an 81% increase in polarization resistance (i.e., R_p: 348 kΩ·cm² in 0SF to 630 kΩ·cm² in 45SF) and a positive shift in corrosion potential (i.e., E_corr: -157 mV to -128 mV). Conversely, the LC suffers a degraded passivity with a 54% reduction in R_p (i.e., 348 kΩ·cm² to 160 kΩ·cm²) and a negative E_corr shift to -208 mV. The Nyquist plots corroborate these results, revealing expanded capacitive arcs for the LA and contracted arcs for the LC in low-pH value extracts. The charge transfer resistance (R_ct) for the LA increases from 5.4×10⁵ Ω·cm² (0SF) to 6.4×10⁵ Ω·cm² (45SF), which is decreased by 44% (i.e., from 5.9×10⁵ Ω·cm² in 0SF to 3.3×10⁵ Ω·cm² in 45SF) for the LC. The Mott-Schottky analysis further distinguishes their electronic properties. The LA exhibits a n-p heterojunction behavior characterized by positive slopes in the -1.00 V to +0.25 V region and negative slopes between -1.5 V and -1.0 V, with decreasing total carrier density (N_total) from 4.96×10²⁰ cm^-3 (0SF) to 1.93×10²⁰ cm^-3 (45SF). In contrast, the LC maintains a n-type semi-conductivity with increasing N_total from 4.93×10²⁰ cm^-3 to 7.09×10²⁰ cm^-3.

XPS depth profile shows critical differences in passive film architecture. The LA forms a bilayer structure comprising an outer layer (0-5 nm) of FeOOH/Fe(OH)₃ (712.2 eV) and Fe₂O₃ (710.7 eV), and an inner layer (5-15 nm) enriched with Cr₂O₃ (576.3 eV), Cr(OH)₃ (577.1 eV), and FeO (709.5 eV). Reduced alkalinity triggers an increase of 32% in Cr-oxide content (i.e., Cr_ox+hy/Cr_metal=2.8 in 45SF vs. 2.1 in 0SF), forming a dense chromium-rich barrier. The LC develops a monolayer Fe-oxide film (0-10 nm) containing Fe₂O₃, FeOOH, and FeO, with decreasing FeO content by 67% (i.e., Fe²⁺/Fe³⁺=0.18 in 45SF vs. 0.55 in 0SF). Mechanistically, a low alkalinity elevates the oxidation potential of chromium in the LA, promoting Cr³⁺ incorporation into the inner film. The resultant Cr₂O₃ (p-type semiconductor) establishes a high-energy barrier (i.e., >1.2 eV) against anion penetration, while coexisting FeO (n-type) stabilized cation transport. For the LC, FeO decomposition (Fe²⁺→Fe³⁺) at a low pH value increases oxygen vacancy density, accelerating ionic transport through the film and diminishing chloride adsorption capacity resulted from loss of [Fe²⁺-Cl^-] coordination sites.

Mineral admixtures content variation at 15%-45% could affect the passivation behavior of Cr10Mo1 alloy corrosion-resistant steel (LA) and ordinary carbon steel (LC). The two steels exhibited a good passivation in the paste extract solutions, but had different performances. As the amount of mineral admixture increased, the passivation of the LA enhanced, while the passivation effect of the LC gradually weakened. The passivation film of the LA was a bilayer structure, with the outer layer primarily composed of FeOOH/Fe(OH)₃ and Fe₂O₃, and the inner layer mainly consisting of Cr₂O₃, Cr(OH)₃, and FeO. The passivation film of LC was a monolayer structure, mainly containing Fe₂O₃, FeOOH/Fe(OH)₃, and FeO. The alkalinity of the paste extract solution decreased, and more Cr elements involved in the formation of the passivation film of the LA with the increase in mineral admixture content, enhancing its passivation. Meanwhile, for the LC, Fe oxides in the passivation film gradually decomposed, and the content of FeO that provided a decreased protection, leading to a weakened passivation. This study indicated that the introduction of mineral admixture could not hinder the passivation effect of the LA, but weaken the passivation strength of the LC.

With the increasing depth of oil and gas exploration, high-temperature and high-pressure conditions become common in deep and ultra-deep wells. Under such harsh environments, the long-term integrity of wellbore cement is frequently compromised due to the strength retrogression, posing serious risks to zonal isolation and production safety. Although silica sand is widely used to mitigate strength degradation via modifying the Ca/Si ratio of hydration products, recent studies show that significant strength loss still occurs at ultra-high temperatures.

Previous studies focused on curing regimes involving low-temperature pre-curing followed by high-temperature exposure, which mainly represent upper wellbore conditions. However, in deeper sections of high-temperature wells, cement typically sets and cures directly at elevated temperatures, resulting in more severe degradation. In addition, low-density cement is widely applied in cementing operations, its long-term performance under such conditions remains insufficiently understood, particularly beyond short-term curing periods. This study was to investigate the long-term performance evolution and underlying mechanisms of low-density sand-added oil well cement under different high-temperature conditions, providing a guidance for the design of durable cement systems for deep well applications.

Six low-density cement slurry formulations with a target density of 1.6 g/cm³ were designed, incorporating silica sand, silica fume, and hollow glass microspheres. Two curing regimes were adopted to simulate different downhole environments, i.e., (1) direct curing at 180 ℃ to represent high-temperature deep well conditions, and (2) curing at 80 ℃ for 2 d and then long-term curing at 180 ℃ to simulate upper wellbore conditions. The long-term performance of cement samples was evaluated after curing at 180 ℃ for 2, 30 d, and 90 d, respectively. The compressive strength, Young's modulus, and permeability were measured to assess macroscopic properties. The microstructural and mineralogical evolution was characterized by mercury intrusion porosimetry (MIP), X-ray diffraction (XRD), thermogravimetric analysis (TG), and scanning electron microscopy (SEM), enabling a comprehensive analysis of pore structure development and hydration product transformation.

The results indicate that curing conditions have a decisive influence on the long-term performance of low-density cement systems. Under direct high-temperature curing at 180 ℃, all formulations exhibit severe strength retrogression. The compressive strength of silica sand-based systems remains approximately 28 MPa within 30 d, but decreased to below 10 MPa after 90 d. The incorporation of hollow glass microspheres significantly enhances the early strength (i.e., up to 45 MPa at 2 d). However, these systems experience a rapid deterioration, with strength nearly disappearing after 30 d. In contrast, the samples subjected to low-temperature pre-curing and followed by high-temperature exposure show significantly improved stability. For formulations without hollow glass microspheres, the compressive strength remains stable or slightly increases for 90 d. However, the systems containing hollow glass microspheres still exhibit a noticeable strength reduction, indicating their limited suitability for high-temperature applications. The evolution of Young's modulus and permeability is consistent with the strength results. Under direct high-temperature curing, Young's modulus decreases markedly, while permeability increases in orders of magnitude, indicating severe degradation of structural integrity. Conversely, under low-temperature pre-curing conditions, the parameters both remain relatively stable, reflecting an improved durability. The microstructural analysis reveals that pore structure coarsening plays a key role in the performance degradation. Under direct high-temperature curing, the median pore size increases significantly from tens to hundreds of nanometers, accompanied by a transformation from harmless pores to harmful large pores. In contrast, the samples subjected to low-temperature pre-curing exhibit slower pore coarsening and more stable pore size distributions. The mineralogical evolution further explains the observed macroscopic behavior. Under direct high-temperature curing, beneficial amorphous hydration products and tobermorite gradually transform into more crystalline phases such as xonotlite, leading to the strength loss. The thermogravimetric analysis confirms significant changes in decomposition peaks, indicating alterations in hydration product composition. In contrast, under low-temperature pre-curing conditions, the content of amorphous phases and tobermorite remains relatively stable, contributing to sustained mechanical performance. The SEM images show that hollow glass microspheres progressively react and disappear under prolonged high-temperature exposure, leaving large voids that significantly deteriorate the microstructure. Although these microspheres enhance the early strength, their long-term effect is detrimental due to the pore enlargement and structural weakening.

This study demonstrated that curing conditions critically affected the long-term performance of low-density sand-added oil well cement. Direct high-temperature curing led to severe strength retrogression, pore structure coarsening, and unfavorable phase transformations, whereas low-temperature pre-curing significantly improved long-term stability. Hollow glass microspheres were effective in enhancing early strength but are not suitable for high-temperature applications due to their negative impact on long-term durability. The stability of hydration products, particularly amorphous phases and tobermorite, could be identified as a key factor governing strength retention. These findings could provide important insights into the design of durable cement systems for high-temperature oil and gas wells and highlight the necessity of considering both curing conditions and material composition in practical applications.

Determining the glass content in fly ash is crucial for characterizing its compositional properties (i.e., glass content, crystal types and quantities, and glass phase composition) and serves as a fundamental parameter for regulating and optimizing its engineering performance (i.e., fresh workability, volume stability, hydration process and products, anti-permeability, and mechanical performance). The glass content of four low-calcium and four high-calcium fly ash samples can be quantitatively analyzed by three methods, i.e., the alkaline-acid two-step dissolution method (NaOH followed by HNO₃), the acid dissolution method (HF), and quantitative X-ray diffraction (QXRD) method. A comparison of the three methods demonstrates that QXRD offers superior testing precision and characterizes the full components of fly ash, unlike the two dissolution methods. Although the two dissolution methods can provide a rough estimation of glass content, their precision is relatively poor. This low precision is attributed to the dissolution of crystalline phases (like quartz and mullite), the formation of new crystalline phases (such as hydromagnesite and tobermorite) during the dissolution process, and the residual undissolved glass phases. Consequently, QXRD can be recommended for the quantitative analysis of fly ash glass content.

The glass content of eight different fly ash samples was measured by three characterization methods, i.e., the alkaline-acid two-step dissolution method (NaOH followed by HNO₃), the acid dissolution method (HF), and quantitative X-ray diffraction (QXRD). A comparison of the results revealed a significant discrepancy between these three testing methods, highlighting a need for further research. To investigate the dissolution power of the NaOH and HNO₃ solutions, two additional methods (i.e., the alkaline dissolution method (NaOH) and the acid dissolution method (HNO₃) were adopted. The respective dissolving capabilities toward both the crystalline and glassy phases of the eight fly ash samples were ultimately disclosed via using QXRD to analyze the dissolving effect of these four dissolution methods.

The dissolving characteristics of the four dissolution methods (i.e., alkaline, HF acid, HNO₃ acid, and two-step) on the crystalline and glass phases of fly ash are analyzed by linear regression and the Pearson correlation method, based on the measured glass and crystal contents remaining after each treatment. The linear regression between the crystal content of the raw fly ash and the residual crystal content after dissolution demonstrates that the crystalline phases of fly ash are dissolvable by both alkaline and/or acid solutions. Furthermore, the results confirm that glass phase residue remains after all alkaline and/or acid treatments, indicating that none of the dissolution methods can achieve a precise selective dissolution of the fly ash's glassy phase. The Pearson correlation method is employed to investigate the dissolving tendency of the crystalline and glass phases in the different solutions. The resulting fly ash component-Pearson correlation coefficient diagram effectively reveals the dissolution characteristics of fly ash components in various alkaline and/or acid solution, thereby clearly explaining the underlying dissolution mechanism and demonstrating the infeasibility of using these alkaline and/or acid dissolution methods for accurately determining the fly ash's glass content.

The quantitative analysis of the glass content in four low-calcium and four high-calcium fly ash samples was conducted by three methods, i.e., the alkaline-acid two-step dissolution method (NaOH followed by HNO₃), the acid dissolution method (HF), and quantitative X-ray diffraction (QXRD). For the three methods, QXRD demonstrated superior precision and comprehensiveness, determining the glass content and the types and quantities of crystalline phases, whereas the two dissolution methods showed a lower precision. The QXRD analysis of the dissolution residues revealed that using alkaline (NaOH) and/or acid (HNO₃, HF) solutions failed to achieve an ideal selective dissolution of the fly ash's glass and crystalline phases. Furthermore, new crystalline phases were sometimes generated during the dissolution process, fundamentally limiting the accuracy of using these dissolution methods for quantitative analysis of glass content. Based on the results of QXRD tests on both the raw fly ash and the alkaline/acid dissolution residues, the error sources were associated with using alkaline and/or acid dissolution methods for determining fly ash glass content. The application of the Pearson correlation method and the resulting interpretation of the reaction mechanisms and products between fly ash components and the NaOH, HNO₃, and HF solutions mechanistically revealed the dissolution process and mechanism of fly ash, thereby fundamentally explaining the infeasibility of accurately quantifying fly ash glass content by alkaline and/or acid dissolution methods.

The chloride (Cl^-) binding capability of calcium silicate hydrate (C-S-H) plays an important role in the chloride resistance and, consequently, the service life of concrete used in marine and deicing environments. The synergistic regulation between geometric confinement and electrostatic interactions and its dependence on the Ca/Si ratio remain insufficiently resolved although the existing studies examine either pore structure (accessibility and transport) or interfacial electrostatics (electrical double layer, EDL). This gap is addressed via establishing a coupled pore structure-EDL framework calibrated against a controlled C-S-H series at different calcium-silicon (Ca/Si) ratios, thereby transforming qualitative descriptions into the mechanism-based quantification of chloride adsorption. In this study, systematic measurements and mathematical descriptions were performed to clarify the C-S-H pore structure, surface potential distribution, and chloride binding capacity. Effects of the Ca/Si and chloride concentrations were investigated. The dynamic competition mechanism between pore-scale accommodation and EDL-mediated electrostatic partitioning was quantitatively characterized.

C-S-H gels with different targeted Ca/Si ratios (i.e., 0.6, 1.0, 1.4, and 1.8) were prepared via chemical precipitation with sodium silicate and calcium nitrate in deionized water. After homogenization, the suspensions were adjusted to pH value of ≥ 12 by using 5 mol/L NaOH solution and then transferred to a polytetrafluoroethylene (PTFE)-lined autoclave and crystallized isothermally

at (60 ± 1) ℃ for 14 d. The product was recovered by vacuum filtration using a Büchner funnel, washed for three times with deionized water, and dried in a vacuum oven at 50 ℃ for 72 h. The dried solid was then ground in a ball mill, passed through a 200-mesh sieve, and stored in a desiccator.

The total pore volume and pore-size distribution (PSD) of the C-S-H gels with varying Ca/Si ratios were determined by nitrogen sorption. The characteristic pore diameter and the gel pores below 10 nm, which were most relevant for transport and EDL overlap in C-S-H clusters, were measured. The Zeta potential (ζ) was measured in alkaline media (pH value of 12) over ionic strengths representative of pore solutions (e.g., 0.1-0.5 mol·L^-1 NaCl). The batch adsorption was conducted via equilibrating C-S-H powders in NaCl solutions at (20.0±0.1) ℃ for 14 d. Supernatants were filtered and analyzed by ion chromatography to determine equilibrium Cl^- concentrations, and adsorption capacities were calculated by mass balance and normalized to solid mass. The Gouy-Chapman-Stern (GCS) framework was implemented in cylindrical pores to couple measured ζ at different pore sizes, explicitly accounting for the Stern layer, diffuse layer, the Debye length, and the EDL overlap in nanoscale. The steady-state Poisson-Boltzmann/Nernst-Planck system was solved to obtain spatial electrostatic potentials and local Cl^- concentrations. The total adsorption was computed via integrating the EDL-related concentration profiles at different pore sizes. The model predictions were compared and validated by the experiments.

The C-S-H specimens with varying Ca/Si ratios normally exhibit unimodal gel-type porosity with dominant mesopores of 3-5 nm, accompanied by a tail extending into larger sizes. Increasing Ca/Si leads to densification, and the cumulative volume of gel pores below 10 nm decreases from approximately 0.11 cm³·g^-1 to 0.04 cm³·g^-1 when Ca/Si increases from 0.6 to 1.8. The zeta potential becomes less negative with increasing Ca/Si ratio, changing from -17 mV (at Ca/Si ratio of 0.6) to -10 mV (at Ca/Si ratio of 1.8) in the case of 0.1 mol·L^-1 NaCl. As electrolyte concentration increases from 0.1 mol·L^-1 to 0.5 mol·L^-1, and |ζ|decreases due to the classical EDL compression. The Debye lengths in the 0.3-1.0 nm imply a substantial EDL overlap within the characteristic gel pores (3-5 nm), eliminating the notion of an electroneutral pore core and promoting field-driven partitioning.

The role of Ca/Si ratio in chloride binding behavior reveals a composition-dependent balance. At a low Ca/Si ratio (i.e., 0.6), the large pore volume favors chloride accommodation, while the highly negative surface potential causes a great electrostatic exclusion of Cl^- near the C-S-H walls. Within confined gel pores, overlapping EDLs shift Cl^- towards the pore center and reduce near-surface concentrations, limiting net chloride uptake despite abundant void space. At an intermediate Ca/Si ratio (i.e., 1.0), the pore volume remains high, while |ζ| is moderated, yielding the most favorable balance between geometric access and electrostatic capture. The modeled profiles and measurements both indicate the maximum adsorption capacity at this intermediate Ca/Si composition, consistent with a synergy between transport accommodation and weakened repulsion. At a high Ca/Si ratio (i.e., 1.8), weaker repulsion can, in principle, increase the interfacial adsorption efficiency per unit area, but the gel densification causes a sharp loss of accessible pore volume (i.e., 0.04 cm³·g^-1), becoming a dominant limiting factor for the reduced overall Cl^- uptake.

A model-experiment agreement is established, demonstrating the non-linear Freundlich-like increase of chloride adsorption with solution concentration. The measured ζ and PSD provide a physically interpretable parameter set for quantitative prediction of Cl^- binding capacity of C-S-H. The densification (at a high Ca/Si ratio) or electrostatic exclusion (a low Ca/Si ratio) can dominate Cl^- adsorption, depending on the composition-controlled balance of the effects. The results identify an intermediate composition window that maximizes Cl^- binding via leveraging moderate surface charge and mesoporous access.

Chloride uptake by C-S-H could be controlled by a dual mechanism, i.e., with gel pore geometry dictating accommodation and transport, while EDL electrostatics governing near-surface adsorption. A low Ca/Si ratio (i.e., 0.6) provided large gel pore volumes but suffered great electrostatic exclusion. A high Ca/Si ratio (i.e., 1.8) weakened a repulsion but substantially densified the gel, resulting in a limited adsorption. An intermediate Ca/Si ratio (i.e., ≈1.0) achieved the optimum synergy between pore accessibility and moderated EDL effects, resulting in a peak Cl^- adsorption. The pore structure-EDL coupling framework proposed in this work could quantitatively link pore sizes and surface potential distribution to chloride adsorption capacity and provide actionable targets for binder design to enhance chloride resistance in service. Cementitious binders that preserved a mesopore network enriched in 3-5 nm pores and moderated surface charge exhibited a superior chloride binding capacity.

Montmorillonite (Mt) has been extensively applied in drug delivery systems owing to its high cation exchange capacity, large specific surface area, and biocompatibility. However, its strong hydrophilicity and interlayer water molecules significantly restrict the efficient loading of hydrophobic drugs (e.g., ibuprofen (IBU)). Organic modification using surfactants can enhance Mt's hydrophobicity, where cationic types dominate as mainstream modifiers due to their efficient ion-exchange characteristics, capable of expanding interlayer spacing and optimizing drug compatibility. Nevertheless, anionic surfactants rely on anion-dipole interaction adsorption, while nonionic surfactants, despite advantages in low toxicity and biodegradability, exhibit inferior binding efficiency with limited research on related drug carriers. Recently, green nonionic alkyl polyglucosides have attracted considerable attention for their environmental friendliness, superior interfacial activity, and biosafety. This study employed dodecyl glucoside (DPG) to fabricate DPG-Mt composite carriers through wet ball-milling intercalation modification. It systematically investigated the intercalation mechanisms between nonionic surfactants and Mt, along with drug-loading performance and release behaviors, aiming to overcome limitations of conventional ionic modifiers and elucidate the efficiency-enhancing principles of nonionic modification in drug delivery systems. This research not only provides novel strategies for developing safe and stable Mt-based drug delivery systems but also advances technological innovation in green pharmaceutics, demonstrating significant theoretical and practical implications.

This study employed green and safe nonionic DPG to prepare a safe and biocompatible DPG-Mt based pharmaceutical formulation through intercalation modification of natural Mt using a wet ball-milling technique. The synthesized DPG-Mt was systematically characterized for its morphology, crystal structure, functional groups, thermal stability, and colloidal properties using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy (RS), thermogravimetric analysis (TGA), BET surface area analysis, and Malvern laser particle size analysis. By investigating the effects of various DPG content on the structure and properties of DPG-Mt, the mechanistic role of DPG in modifying Mt was elucidated, and the adsorption capacity of DPG-Mt for the anti-inflammatory drug, IBU was evaluated. Finally, simulated drug release experiments were conducted to examine the IBU release behavior from drug-loaded DPG-Mt, while cytotoxicity assays were performed to assess its biocompatibility, thereby validating its potential for biomedical applications.

The characterization results of DPG-Mt revealed that the wet ball-milling process did not disrupt or exfoliate the layered structure of Mt. Instead, under the compressive, impact, and frictional forces of mechanical ball-milling, the particle size of Mt was significantly reduced. DPG overcame the weak electrostatic forces, van der Waals interactions, and hydrogen bonding between stacked Mt layers, adsorbing and intercalating into its interlayer spaces, thereby expanding the interlayer spacing of DPG-Mt. Specifically, the interlayer spacings of 1.0DPG-Mt, 2.0DPG-Mt, and 3.0DPG-Mt increased to 1.47 nm (2θ = 6.0°), 1.55 nm (2θ = 5.7°), and 1.52 nm (2θ = 5.8°), respectively. Raman spectroscopy and BET surface area analysis demonstrated that nonionic DPG did not form chemical bonds with DPG-Mt but was physically intercalated into the interlayers via hydrogen bonding and electrostatic interactions. The BET surface area of Na-Mt increased from 62.6 m²/g to 72.0 m²/g under mechanical ball-milling, likely due to the fragmentation of large Mt particles during the modification process. However, the adsorption and intercalation of DPG into DPG-Mt partially obstructed pore channels, leading to a reduction in pore volume and average pore size from 0.337 mL/g and 4.61 nm to 0.245 mL/g and 2.15 nm, respectively.

Concurrently, after organic modification, the particle size of DPG-Mt decreased from 984.0 nm (PDI = 0.34) to 605.0 nm (PDI=0.31), while its zeta potential increased from -39.7 mV to -22.5 mV. These results indicated that the adsorption and intercalation of DPG not only inhibited particle aggregation but also reduced surface charge density. Furthermore, the interfacial tension (IFT) of the DPG-Mt aqueous suspension significantly decreased from 72.75 × 10^-3 N·m^-1 to 41.12 × 10^-3 N·m^-1 after modification. This phenomenon can be attributed to the hydrophobic dodecyl chains of intercalated DPG, which weakened the hydrophilicity of DPG-Mt and enhanced its interfacial activity. Adsorption experiments to IBU demonstrated that organic modification increased the saturated adsorption capacity (Q_m) of Mt for IBU from 35.336 mg/g to 46.083 mg/g, with IBU adsorption on DPG-Mt favoring a multilayer mechanism. In vitro drug release and cytotoxicity analyses revealed that both Mt and DPG-Mt exhibited sustained release of IBU over 480 min. Notably, the biocompatible DPG-modified Mt (DPG-Mt) displayed superior biocompatibility, underscoring the synergistic benefits of organic modification in optimizing drug delivery performance while maintaining biosafety.

The wet ball-milling process exerted compressive, impact, and frictional forces on Mt, leading to reduced particle size, increased specific surface area, and enhanced colloidal interfacial activity of DPG-Mt. The organic modification of Mt by DPG occurred via physical intercalation, where DPG was physically embedded into the interlayer spaces of Mt under mechanical forces. This not only expanded the interlayer spacing and improved the thermal stability of the intercalated DPG but also effectively altered the strong polarity of Mt, thereby enhancing its affinity for the hydrophobic IBU. Consequently, the saturated adsorption capacity of Mt for IBU increased from 35.336 mg/g to 46.083 mg/g. Owing to the superior adsorption performance of DPG-Mt to IBU, DPG-Mt exhibited sustained drug release over 480 min, demonstrating favorable sustained-release performance, with the released IBU amounting to 76% of the total dosage. Cytotoxicity tests further confirmed that the organic modification of Mt with DPG conferred excellent cytocompatibility to DPG-Mt. Given its high drug adsorption capacity, excellent sustained-release properties, and outstanding cytocompatibility, DPG-Mt demonstrates significant potential as a hydrophobic anti-inflammatory drug formulation for biomedical applications.

Low-density polyethylene (LDPE) is limited in its high-end applications due to insufficient mechanical and thermal properties. Using silicate minerals as fillers is an effective strategy, as they possess high modulus, thermal stability, and abundance. Anorthite (An), as a framework silicate mineral, is a highly promising candidate material. However, its hydrophilic surface is incompatible with the hydrophobic LDPE, thus hindering the performance. Silane coupling agent (i.e., KH570) can improve interfacial bonding, but its role in the An/LDPE system remains unclear. In this study, the effect of KH570-modified An on the LDPE performance was investigated, offering some insights into the development of high-performance LDPE composites and the efficient utilization of An resources.

The silane coupling agent KH570 was used to modify An. KH570 was hydrolyzed in an ethanol-water solution (7∶3 v/v) and reacted with An at 80 ℃ for 6 h. After centrifugation, washing, and drying, modified anorthite (K-An) was obtained. K-An/LDPE composites (in a mass ratio of 95 ∶ 5) were fabricated via melt blending with a twin-screw extruder (130-180 ℃) followed by hot pressing. The modification mechanism was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS). The morphology and dispersion were characterized by scanning electron microscopy (SEM). The surface wettability was determined by contact angle measurement. The mechanical properties were determined by tensile testing (ISO 527-1), and the thermal stability was determined by thermogravimetric analysis/derivative thermogravimetry (TG/DTG).

KH570 effectively modifies the chemical properties of the surface of An without altering its crystal structure or morphology. The FT-IR and XPS spectra confirm the formation of covalent bonds, enhancing the hydrophobicity of the An surface and improving its dispersibility in LDPE. The mesoporous structure remains stable, and the distribution of small pores is improved, indicating a regulatory effect on the pore characteristics. The K-An/LDPE composite material exhibits excellent tensile strength, elongation, and thermal stability due to the rigidity of An, intense interfacial bonding, and thermal barrier effects, highlighting its potential as an LDPE reinforcing filler.

KH570 could react with -OH groups on the surface of An to form stable bonds, reducing surface polarity, while preserving its crystal morphology, thereby lowering the interfacial energy difference with LDPE and enhancing compatibility. As a result, K-An/LDPE composites modified with KH570 showed enhanced tensile strength, elongation at break, tensile modulus and thermal residue, which could be increased by 38.01%, 104.39%, 48.32%, and 15.05%, respectively. This was attributed to the rigidity and thermal stability of the An framework, intense interfacial bonding, and a uniform thermal barrier network.

Low-temperature co-fired ceramic (LTCC) technology becomes a cornerstone in the fabrication of high-frequency, high-density microelectronic devices. However, a critical challenge lies in a thermal expansion mismatch between conventional LTCC substrates (typically <7 × 10^-6 ℃^-1) and printed circuit boards (PCBs, 12-16 × 10^-6 ℃^-1), leading to interfacial delamination and compromised reliability. To address this issue, developing LTCC materials with high and linear thermal expansion coefficients is essential. Among some potential candidates, BaO-B₂O₃-SiO₂ glass-ceramics stand out due to their tunable properties and relatively high thermal expansion. Nevertheless, achieving dense sintering at < 900 ℃, while maintaining a high thermal expansion coefficient (>11 × 10^-6 ℃^-1), remains a key obstacle. This study was to investigate the synergistic effects of incorporating CaO and SrO into BaO-B₂O₃-SiO₂ glass-ceramics on the sintering behavior, crystallization characteristics, and overall performance for LTCC applications.

Four glass compositions were designed based on the BaO-B₂O₃-SiO₂ system at different additions of CaO and SrO (in mole fraction), i.e., BBS1 (baseline), BBS2 (with 10% CaO), BBS3 (with 10% SrO), and BBS4 (with 5% CaO and 5% SrO). Raw materials including BaCO₃, CaCO₃, SrCO₃, H₃BO₃, and SiO₂ were melted at 1400 ℃ for 2 h, quenched, and ground into powders with a consistent particle size (D₅₀ ≈ 5 μm). The powders were pressed into pellets and bars, then sintered at 800 ℃ for 20 min. The sintering behavior was analyzed by high-temperature microscopy and dilatometry. The glass transition (T_g) and crystallization (T_c) temperatures were determined by differential scanning calorimetry (DSC). The phase identification and crystallinity were evaluated by X-ray diffraction (XRD), and the microstructural features were characterized by scanning electron microscopy (SEM) and Raman spectroscopy. The thermal expansion, density, dielectric properties, flexural strength, and hardness were measured to assess the performance of the sintered glass-ceramics.

The introduction of CaO and SrO significantly lowers the onset (T_fs) and maximum shrinkage (T_ms) temperatures, with SrO having a more pronounced effect. For instance, T_fs decreases from 720 ℃ (BBS1) to 654 ℃ (BBS3), and T_ms decreases from 794 ℃ to 761 ℃. This indicates enhanced sintering kinetics due to the larger ionic radius and higher polarizability of Sr²⁺, which facilitates atomic mobility and reduces activation energy. The results of DSC reveal that the addition of CaO and SrO also decreases T_g and T_c, with SrO again exhibiting a greater influence. The ΔT (T_c-T_g) value, which reflects the processing window for sintering, is increased in SrO-containing samples, indicating an improved sinter-ability.

The XRD patterns show that the baseline sample (BBS1) primarily crystallizes into Ba₂Si₄O₁₀, while CaO addition (BBS2) promotes the formation of Ba₅(Si₈O₂₁), and SrO addition (BBS3) and co-addition (BBS4) favor forming Ba₂Si₃O₈. The crystallinity increases due to CaO and SrO incorporation, reaching a maximum of 77.76% in BBS2, indicating that CaO is more effective in promoting crystallization due to its smaller ionic radius and higher field strength, which enhances network disruption and nucleation. The Raman spectra confirm a structural transformation from [BO₃] to [BO₄] units, indicating a more connected and dense glass network.

The microstructural analysiss reveals that the baseline sample has a porous structure with dispersed spherical crystals. In contrast, CaO and SrO additions lead to denser microstructures with interlocked crystal morphologies. Particularly, BBS4 exhibits a uniform distribution of plate-like Ba₂Si₃O₈ crystals and reduced porosity, contributing to its superior mechanical properties.

Thermal expansion coefficients (30-300 ℃) range from 11.52 × 10^-6 ℃^-1 to 12.43 × 10^-6 ℃^-1, with BBS4 showing a balanced value of 11.94 × 10^-6 ℃^-1. The slight decrease compared to the baseline is attributed to the increased [BO₄] content and denser structure. The dielectric constants at 1 MHz vary between 6.51 and 7.82, with BBS3 showing the maximum value due to its high-polarizability Sr²⁺ and Ba₂Si₃O₈ phase. The flexural strength and hardness are significantly improved in CaO- and SrO-modified samples, with BBS4 achieving the maximum values (i.e., 124.36 MPa and 953.88 HV). These enhancements are attributed to increased crystallinity, reduced porosity, and optimized crystal interlocking.

This study demonstrated that the co-incorporation of CaO and SrO into BaO-B₂O₃-SiO₂ lass-ceramics effectively modulated their sintering and crystallization behaviors, enabling dense sintering at 800 ℃. SrO played a dominant role in lowering sintering temperatures, while CaO was more effective in promoting crystallization. The synergistic effect of both oxides led to a glass-ceramic with a high thermal expansion coefficient (i.e., 11.94 × 10^-6 ℃^-1), excellent mechanical strength (i.e., 124.36 MPa), and favorable dielectric properties (i.e., 8.71 at 1 MHz), making it highly suitable for LTCC applications. The findings could provide a promising pathway for developing high-performance, low-temperature sintering glass-ceramics with tailored thermal and mechanical properties.

This review summarizes the latest research progress on the performance changes and modulation of early-age concrete under vehicle-bridge coupled vibration, focusing on the analysis of vibration characteristics, concrete performance changes, and the underlying mechanisms, and discussing the existing modulation measures. Future research should develop lab-scale simulation methods that more closely reflect actual vibration characteristics, considering the coupling effects of temperature, humidity, load, and corrosion, to comprehensively explore the evolution of concrete performance under practical conditions. In addition, in-situ characterization systems for vibration loading should be also developed, using techniques such as 3D CT, NMR, DIC, and acoustic emission to capture the evolution of the concrete microstructure and crack dynamics, thus providing a basis for the design and life prediction of vibration-resistant concrete.

Rare-earth ion-doped materials demonstrate outstanding advantages in solar-blind photodetection due to their unique energy level structure, efficient energy transfer, and controllable luminescence properties. Remarkable progress is made in material design, energy transfer mechanism clarification, matrix modification, defect engineering, nanostructure construction, and device performance optimization. The optimized devices exhibit enhanced responsivity, widened spectral response, faster response speed, lower dark current, and improved stability, showing broad application prospects in military, civil, environmental, and biomedical fields. Nevertheless, several critical scientific and technical challenges still hinder their practical deployment. For instance, the trade-off between high energy transfer efficiency and long-term structural stability remains difficult to balance. The excessive rare-earth doping often causes concentration quenching and luminescence attenuation. A precise control over microstructures, interfaces, and defect states remains challenging, and the dynamic photophysical evolution under operating conditions requires a further in-depth verification via in-situ characterization techniques.

Future research directions of rare-earth-based solar-blind photodetection will focus on the development of novel composite material systems via integrating rare-earth dopants with two-dimensional materials, perovskites, and other emerging functional media to construct high-efficiency energy transfer channels. In the device level, developing miniaturized, integrated, and intelligent micro-/nano-solar-blind photodetectors combined with MEMS technology becomes a major trend. Meanwhile, theoretical simulations based on first-principles calculations, high-throughput material screening, and low-cost scalable fabrication processes should be strengthened to accelerate the industrial translation of laboratory achievements. This review provides a systematic and valuable reference for the rational design, performance optimization, and practical innovation of high-efficiency rare-earth-doped solar-blind photodetection materials and devices.