For the "dual carbon" (i.e., carbon peak and carbon neutrality) goals and global sustainable development strategies, the construction materials sector rapidly transitions from conventional cement-based materials toward low-carbon, environmentally friendly, and high-performance alternatives. Engineered geopolymer composites (EGC) as a new generation of green building materials exhibit superior mechanical properties, thermal stability, and environmental compatibility. However, the performance of EGC is synergistically governed by multiple factors (i.e., alkali activator concentration, liquid-to-solid ratio, aggregate characteristics, and fiber geometry). The intricate, nonlinear interactions among these parameters present significant challenges for precise mix design and performance optimization. Conventional trial-and-error approaches are prohibitively expensive and time-consuming, rendering them fundamentally incapable of effectively exploring the expansive combinatorial space of EGC mix formulations. Furthermore, EGC mix design often requires the simultaneous satisfaction of multiple, potentially conflicting performance targets, such as high compressive strength and high tensile strain capacity. Therefore, this study was to develop an intelligent design framework for EGC integrating performance prediction and multi-objective optimization. This framework directly addressed the limitations of conventional "black-box" and "experience-driven" approaches, enabling the intelligent and precise design of sustainable cementitious materials.

This study established a closed-loop intelligent design framework for EGC based on machine learning (ML), incorporating "prediction-optimization-feedback". Firstly, a comprehensive EGC database was systematically integrated, comprising 309 sets of experimental data from domestic and international literature. Eleven key mix proportion parameters (i.e., ground granulated blast furnace slag (GGBS), fly ash (FA), silica fume (SF), sand-to-binder ratio (S/B), water-to-binder ratio (W/B), alkali activator modulus (AAM), alkali equivalent (AAD), fiber content (FC), fiber length (FL), fiber length-to-diameter ratio (FLD), and fiber tensile strength (FTS)) were selected as input variables. The 28-d compressive strength, tensile strength, and tensile strain capacity were chosen as target output indicators. Five ML models (i.e., Support Vector Regression (SVR), Gradient Boosting Regression Tree (GBRT), XGBoost, Artificial Neural Network (ANN), and Invertible Neural Network (INN)) were developed to predict the key mechanical properties of EGC from its mix design parameters. Hyperparameter optimization was conducted using grid search and experience-based tuning. The SHapley Additive exPlanations (SHAP) method was employed to quantitatively analyze the influence of each mix parameter on the EGC performance. Finally, the CatBoost model was integrated with the NSGA-III (Non-dominated Sorting Genetic Algorithm III) multi-objective evolutionary algorithm to achieve targeted performance optimization, while automatically searching for optimal balanced solutions under various engineering scenarios.

Among the five ML models evaluated, the INN model exhibits the maximum predictive accuracy, achieving R² values of 0.99, 0.98, and 0.99 for compressive strength, tensile strength, and tensile strain, respectively. The results of SHAP-based global sensitivity analysis reveal that increasing GGBS content significantly enhances compressive strength via promoting secondary hydration reactions. The fiber tensile strength (FTS) is the most influential parameter for tensile strength, and the fiber length (FL) critically determines the tensile strain capacity via improving crack-bridging effects. Alkali equivalent (AAD) and alkali activator modulus (AAM) both affect all three performance indices. Three optimization objectives are implemented by the integration of CatBoost and NSGA-III, i.e., 1) simultaneous maximization of all three target performances, 2) targeted optimization focusing on maximum tensile strain, and 3) precise matching of predefined target performance values. From the set of Pareto-optimal solutions, the optimal mix proportion is definitively selected using the TOPSIS ranking method for subsequent experimental validation. The validation results show that the errors between predicted and measured values for the three selected mix proportions are all within 10%, with the minimum error being only 5.3%, confirming the reliability and engineering accuracy of the proposed intelligent design framework.

This work could establish an intelligent design framework for EGC that integrates INN prediction, SHAP interpretability analysis, and CatBoost-NSGA-III multi-objective optimization, achieving a full-process closed-loop design from performance prediction to targeted optimization. The major findings were 1) The INN model demonstrated a superior predictive accuracy for the three key performance indicators of EGC, providing a robust foundation for forward performance prediction; 2) SHAP analysis quantitatively elucidated the mechanisms of key mix parameters on EGC performance, offering a theoretical support for precise material design; and 3) The CatBoost and NSGA-III integration effectively enablec\d both multi-objective collaborative optimization and single-objective precise targeting, validating the feasibility of the reverse design from desired performance targets to optimized mix proportions through experimental verification. This intelligent design strategy effectively resolved the conventional strength-toughness conflict, thereby transcending the limitations of "black-box" and experience-driven methods and propelling a field toward a paradigm of intelligent, data-driven design for next-generation geopolymer composites.

Under the dual pressures of global extreme climates and the demand for long service lives in major engineering projects, conventional concrete materials increasingly struggle to meet performance requirements for critical infrastructures like cross-sea bridges and nuclear facilities, highlighting a urgent need for advanced construction materials. Ultra-high performance concrete (UHPC) emerges as a transformative solution for modern engineering challenges due to its exceptional compressive strength (>120 MPa), durability, and lightweight design advantages. However, UHPC development faces critical barriers, i.e., the intricate nonlinear relationships among its composition, microstructure, and properties undermine conventional modeling approaches, while the scarcity of high-quality datasets (typically <200 samples) with inconsistent experimental protocols severely limits the reliability of data-driven models. This "algorithm-data mismatch" dilemma necessitates innovative database enhancement strategies.

The existing efforts in transfer learning and data augmentation, including generative adversarial networks (GANs) and Kriging interpolation, show a promise but encounter three fundamental limitations, i.e., physical inconsistency in synthetic data (e.g., illogical porosity-strength correlations), high-dimensionality failures in interpolation methods, and domain-shift distortions during cross-material knowledge transfer. To address these challenges, this study was to propose a dual-driven data enhancement framework integrating conditional GAN (CGAN) and Markov Chain Monte Carlo (MCMC) techniques. The methodology systematically resolved dimensionality issues through preprocessing, generated physically constrained virtual samples via CGAN under constitutive material relationships, and ensures topological consistency using MCMC statistical correction. A Bayesian-optimized XGBoost model could validate the enhanced dataset, with experimental verification from marine floating platform UHPC applications, demonstrating the framework efficacy in overcoming multi-dimensional data generation constraints. This approach was to establish a robust paradigm for intelligent performance prediction under small-sample conditions, advancing digital design capabilities for concrete structures in complex marine environments.

This study developed a CGAN-MCMC model to address small database augmentation and intelligent prediction for UHPC. The process commenced with standardized preprocessing of the original UHPC database, where missing values and outliers were systematically handled through the multiple imputation by chained equation (MICE) method and isolation forest algorithm, respectively, and followed by K-fold cross-validation partitioning. The CGAN-MCMC model was then employed to enhance the UHPC database as distribution comparisons and Kolmogorov-Smirnov (KS) tests conducted to verify the homogeneity between the original and augmented databases. Subsequently, a Bayesian optimization (BO)-tuned XGBoost model was implemented to evaluate performance prediction capabilities by both pre- and post-augmentation datasets. The model validity was further confirmed through validation against an actual UHPC engineering dataset from a floating platform project in Guangdong Province, China, demonstrating its practical applicability and robustness in engineering scenarios.

The data standardization effectively eliminates feature scale discrepancies, mitigating overfitting risks caused by parameter weight bias. The MICE method significantly enhances model noise resistance and prediction stability, establishing a high-reliability data foundation for UHPC performance prediction models. The isolation forest (IF) algorithm effectively removes outliers induced by instrument drift or mixing errors, while improving dataset robustness. The K-fold cross-validation further strengthenes model generalization via comprehensively evaluating performance across diverse data subsets. The optimized dataset demonstrates marked improvements in prediction accuracy and consistency across all performance metrics.

The CGAN-MCMC model is applied to augment the preprocessed UHPC database (N=145), generating an enhanced dataset (N=1,500). A statistical homogeneity between original and augmented data is confirmed through comparative analysis of distribution patterns, Pearson correlation matrices, and Kolmogorov-Smirnov (KS) tests (p-values >0.05). A Bayesian optimization (BO)-tuned XGBoost model reveals substantial improvements in test-set R² values for 3-day hydration heat (62%), median pore size (55%), compressive strength (18%), flexural strength (46%) and workability (35%) when using augmented data, quantitatively validating the CGAN-MCMC enhancement methodology.

The validation with engineering data from a floating platform project in Guangdong Province, China, demonstrates an enhanced model performance (i.e., test-set R² increases from 0.65 to 0.92, with mean absolute error (MAE) reductions of 35% (training) and 50% (testing), alongside root mean squared error (RMSE) decreases of 39% (training) and 48% (testing)). These results empirically confirm an engineering feasibility of the proposed framework. The proposed methodology advances UHPC intelligent design through improved data quality and model generalization, providing an innovative solution for performance optimization.

This study demonstrated a transformative potential of machine learning in overcoming data limitations and enhancing UHPC performance prediction. The study established a paradigm-shifting approach for UHPC design optimization via integrating advanced data augmentation with optimized ML modeling. The CGAN-MCMC framework could resolve critical challenges in material informatics and provide actionable insights for developing sustainable, high-performance concrete systems, accelerating the digital evolution of construction materials engineering.

As a predominant construction material in modern civil infrastructure (i.e., roads, bridges, ports, and airports), concrete is susceptible to adversing actions such as external loading, freeze-thaw cycling, and salt ingress. These factors readily induce surface spalling and scaling, accelerating service-life deterioration and posing significant safety risks. The existing polymer-modified mortars and epoxy-based systems are commonly employed for the repair and strengthening of concrete structures. An organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar is adopted in concrete repair projects due to its rapid setting and early strength. However, the performance requirements differ markedly across construction scenarios in low-flow applications (e.g., vertical or overhead placement), the mortar must maintain a low flowability to prevent sagging/run-off during placement, and mechanical performance depends primarily on bond strength, with relatively relaxed demands on compressive and flexural strengths. By contrast, high-flow applications (e.g., pavement repair or large-area casting) require a high flowability to ensure adequate spreading and filling workability. The compressive strength is a principal mechanical target, while the flexural and bond performance must be also satisfied. These divergent demands make it difficult for a single HB-CSA mixture to meet the performance needs of multiple repair scenarios, underscoring the practical importance of multi-scenario mixture-design optimization for HB-CSA repair mortars.

This study was to investigate an organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar system. Flow spread was employed as a constraint to distinguish high- and low-flow-state scenarios, while 7-d compressive, 7-d flexural, and 7-d bond strengths were taken as the optimization objectives. To address mixture optimization for both flow states, we introduced (i.e., pioneering its use in the cement materials field) a variational-autoencoder-based generative optimization algorithm (VAE-GOA). The method could leverage a generative model to progressively estimate the probability distribution of optimal mixtures over the global design space. In parallel, an adaptive global-exploration strategy prioritized high-potential regions to mitigate premature convergence to local optima, and an iterative optimization scheme further drived the search toward superior solutions. The approach delineated the optimal composition windows and corresponding performance of HB-CSA repair mortars under different flow-state scenarios via visualizing the VAE-GOA-estimated distribution of optimal mixtures. In addition, backscattered electron (BSE) imaging of the interfacial transition zone (ITZ) was also employed to elucidate the mechanisms underlying the improvement in bond performance.

At the outset, the VAE-GOA conducts broad, globally exploratory sampling, while unavoidably covering some low-performing regions, effectively uncovering previously unexplored high-potential areas. As iterations proceed, probability mass progressively concentrates in high-performance regions. The search distribution transitions smoothly from global exploration to local exploitation, reduces attention to low-performing zones, and converges toward a compact subspace containing the best-performing mixtures. These dynamics substantiate the algorithm's intended explore-then-exploit behavior in a multi-constraint design space and effectively mitigate premature convergence to local optima.

Based on two optimization generations and despite strict flowability constraints and limited sampling, the VAE-GOA delivers both macro-level performance gains and a marked expansion of feasible design space. The optimum weighted comprehensive performance is improved by about 20% in the high-flow state group and by >25% in the low-flow state group. The candidates with compressive strength >50 MPa increase from 2 to 6, and those with bond strength >6 MPa increase from 4 to 12, substantially broadening mixture options that meet key targets. For low-flow state scenario, increasing the USCMs is accompanied by a nonlinear decrease in the HPMC, and higher USCMs generally require a lower water-binder ratio (w/b). A practical window of 15%-30%USCMs, 2%-6% Wacker 328, and about 0.1% HPMC at a w/b ratio of 0.22 achieves a low flowability witha high interfacial performance (i.e., mixture L8 attains 48.6 MPa (compressive), 9.2 MPa (flexural), and 6.5 MPa (bond)). For high-flow state scenario, the strength-oriented optimum occurs near about 5% USCMs + 1% Wacker 328 at a w/b ratio of 0.25 (i.e., H7 reaches 63.4 MPa (compressive), 8.3 MPa (flexural), and 4.5 MPa (bond)). For cost-oriented deployment, increasing the USCMs to ～30% with 5%Wacker 328 at a w/b ratio of 0.20 yields a balanced, economical option (i.e., ≈50.7/7.4/6.6 MPa for compressive/flexural/bond). Overall, substituting 15%-30% the USCMs for HB-CSA regulates a flowability and enhances a bond strength as well as reduces production costs by 13%-27%.

Holding the HPMC and w/b ratio roughly fixed while pushing the USCMs beyond 30% (H8 vs. H1) reduces the compressive strength by 14.7 MPa but elevates the bond strength to 6.9 MPa, highlighting a deliberate bond-first Pareto choice. The backscattered electron imaging corroborates this pheoneman. The interfacial transition zone (ITZ) in H8 exhibits a significantly lower porosity and a denser microstructure at the old-new interface, explaining the observed bond enhancement.

In this study, the VAE-GOA could expedite the discovery of high-potential HB-CSA mixtures under strict flow constraints and limited sampling. The comprehensive performance of high-flow state mortars was increased by 20%, and that of low-flow state mortars by >25%. Beyond single best points, the method could yield scenario-specific composition windows and a diverse portfolio choices that practitioners could select from according to the strength, bond, and cost priorities. In particular, low-flow applications were supported by a stable window (e.g., 15%-30% USCMs with 0.1% HPMC and w/b ≈ 0.22), while high-flow placement admited both a strength-oriented option (～5% USCMs + 1% Wacker 328 at w/b = 0.25) and a cost-oriented alternative (30% USCMs + 5% Wacker 328 at w/b = 0.20). The VAE-GOA could be sample-efficient, resilient to local optima, and readily extensible to multi-objective constraints. Looking ahead, coupling the framework with durability targets (e.g., freeze-thaw and chloride ingress), long-term field validation, and uncertainty-aware priors could broaden its applicability to scenario-aware specification and lifecycle-optimized repair design.

The post-cracking seepage behavior of Engineered Cementitious Composites (ECC) is critical to their service security and durability. However, conventional methods are not capable of in-situ seepage assessment due to the difficulty in characterizing internal fissures from observable surface cracks. This study was to develop an intelligent approach for inferring 3D seepage channels from surface cracks and accurately predicting the seepage performance, thereby providing a novel pathway for in-situ durability evaluation of cracked ECC structures.

A computer vision-based approach was employed to achieve a high-precision characterization of surface microcracks and internal 3D fissures. A dual pre-modification deep learning strategy was proposed for semantic segmentation of surface cracks, significantly improving the accuracy to 99.87%. For internal fissure characterization, a transformer-based super-resolution model coupled with a fine segmentation network was developed to enhance computed tomography (CT) voxel resolution by 4×4×4 times, enabling extraction of fine fissures less than 50 μm in width. Also, a novel diffusion-based generative model was designed to intelligently infer 3D internal fissures from 2D surface cracks. The model could integrate optical flow mechanisms to ensure structural coherence and physical plausibility. Furthermore, the Lattice Boltzmann Method (LBM) was utilized to simulate seepage flow within both real and generated fissures, considering complex geometric features and fiber blocking effects, to validate the permeability performance of the inferred fissures.

The proposed diffusion model effectively generates 3D fissures that closely resemble real ones obtained from CT. The key geometric parameters (i.e., tortuosity, roughness, and average width) are differed by less than 5% between generated and real fissures. The results of the LBM simulations further demonstrate that a ratio of hydraulic width to geometric width for generated fissures has an error of less than 9%, compared to real ones. These results indicate that the intelligently inferred fissures can reliably replicate the seepage behavior of actual ECC cracks, enabling to accurate in-situ permeability assessment based solely on surface crack information.

This study presented an integrated framework combining computer vision, diffusion generative modeling, and LBM for intelligent inference and seepage prediction of 3D seepage channels in post-cracked ECC. The key achievements could include, i.e., 1) highly accurate surface crack segmentation; 2) enhanced internal fissure characterization overcoming CT resolution limitations; 3) effective 3D fissure inference from 2D surface cracks with high geometric fidelity; and 4) validated seepage performance of generated fissures via the LBM simulation. The proposed method could offer a promising tool for in-situ durability assessment of ECC structures. A future work could be needed to extend an approach to multiple cracks and validate it under broader experimental conditions.

With increasing global emphasis on sustainable development, the CO₂ emissions from concrete as the most widely used building material have attracted much attention. Statistics show that cement production accounts for approximately 8% of global CO₂ emissions. Low-carbon concrete minimizes lifecycle CO₂ emissions through optimized cementitious materials, use of solid waste, and low-carbon technique (e.g., carbon capture), while still meeting engineering performance standards. However, incorporating recycled aggregates or mineral admixtures, although reducing CO₂ emissions, often leads to a deterioration in mechanical properties and durability of concrete. Therefore, how to effectively balance reduced CO₂ emissions while maintaining the engineering performance of concrete remains a challenge in low-carbon concrete mix designs.

The complex interactions among multiple components of concrete materials make mix proportion design highly complex. Conventional trial-and-error methods show obvious deficiencies in efficiency, cost and precision. Previous research established some performance prediction models based on machine learning methods, which often focused on single performance indicators. There is little systematic research on multi-objective collaborative optimization of mechanical properties. This failure to adequately balance environmental benefits with performance hinders the effective use of AI in designing low-carbon concrete. The result demonstrates that optimizing the proportions of recycled aggregates and supplementary cementitious materials can effectively limit carbon emissions to the range of 240-260 kg CO₂/m³ when the mechanical property requirements (i.e., compressive strength >50 MPa) and chloride ion permeability resistance (i.e., electric flux <500 C) are satisfied. This study was to offer an approach for achieving a balanced design between material performance and CO₂ emissions in low-carbon concrete, and to promote the application of Artificial Intelligence (AI) technology in its mix proportion design.

This study introduced an AI-based intelligent design optimization method for low-carbon concrete. The process started with data preprocessing, including normalization and outlier treatment, to enhance dataset quality. Subsequently, various models, optimized via different hyperparameter tuning methods, were benchmarked for high-precision prediction of key performance indicators. The SHapley Additive Explanations (SHAP) analysis was employed to elucidate the impact of mix proportion parameters on concrete performance. The The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm was then utilized for multi-objective optimization of CO₂ emissions, cost, strength, and chloride ion penetration resistance. Two indices, C_i (Carbon intensity index) and Q_i (Chloride ion penetration carbon efficiency index), were introduced to quantify the balance between environmental impact and concrete performance. These indices were subsequently employed to evaluate the solutions from the multi-objective optimization, guiding the decision-making process towards a comprehensive and synergistic optimization of material properties and low-carbon characteristics.

During data preprocessing, normalization addresses dimensional inconsistencies among feature variables, enhancing their comparability. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm effectively identifies specific outliers (i.e., 43 related to compressive strength and 4 concerning electric charge passed), leading to a marked improvement in overall data quality. Comparison of machine learning models shows that eXtreme Gradient Boosting (XGB) model exhibits a superior performance in all prediction tasks, with Particle Swarm Optimization (PSO) achieving R² of 0.91 for compressive strength and 0.89 for electric charge passed on test sets. Interpretability through SHAP analysis reveals that cement content and water content are the main factors affecting compressive strength, while superplasticizer dosage is a key factor affecting electric charge passed. For carbon efficiency indices, fly ash replacement of cement is an important approach to reduce C_i and Q_i, while the negative impact of recycled aggregates on performance often exceeds their carbon reduction benefits. Multi-objective optimization results indicate significant trade-offs among compressive strength, durability, CO₂ emissions, and cost. Decision schemes based on carbon efficiency indices further confirm that CO₂ emissions can be effectively reduced, while ensuring concrete performance via scientifically proportioning supplementary cementitious materials, and optimizing water-binder ratio and superplasticizer content.

This study highlighted a significant potential of an AI-driven approach for designing low-carbon concrete, effectively balancing engineering performance with environmental impact. This study could provide valuable insights into achieving tailored concrete properties via utilizing predictive modeling, parameter influence analysis with SHAP, and NSGA-III for multi-objective optimization. The proposed intelligent design method incorporating carbon efficiency indices could offer a practical strategy for developing low-carbon concrete, promoting sustainable practices in the construction industry.

Concrete as a typical multiphase composite material exhibits properties closely related to raw material quality, mix proportion design, curing conditions, and types of admixtures. Especially in the field of alkali-activated concrete (AAC), a predominant use of industrial solid wastes as raw materials introduces inherent complexities, including intricate microscopic reaction mechanisms, highly unpredictable performance characteristics, leading to the absence of a widely accepted reliable design standards. However, conventional approaches such as experimental characterization and numerical simulations demand substantial financial and labor resources and yield outcomes constrained due to material variability and methodological assumptions. Machine learning (ML) as a data-driven approach has a potential to promote the implementation of the standard from another perspective (i.e. data aspect) with less cost waste via establishing a predictive model especially a multi-output predictive models with high accuracy and robustness. Therefore, this study was to use machine learning in property prediction of alkali-activated concrete (AAC) via establishing a robust dual-output hybrid BPNN framework, resolving challenges in multi-objective optimization and overfitting through metaheuristic algorithms, and providing mechanistic insights by a feature importance analysis.

This study established two independent databases for model training and generalization capability evaluation. Initially, the datasets were preprocessed and analyzed through data normalization, Pearson correlation coefficient calculation, and descriptive statistical analysis. Subsequently, four metaheuristic algorithms, i.e., Ant Colony Optimization (ACO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO), were employed to optimize the hyperparameters of the Backpropagation Neural Network (BPNN), with a predictive accuracy enhanced via ten-fold cross-validation. The predictive performance of five models (original BPNN and optimized variants) was systematically compared based on four evaluation metrics, i.e., coefficient of determination (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The generalization capability was further validated using a novel independent database, ultimately identifying the optimal model capable of high-precision simultaneous prediction of compressive strength and slump. Finally, the principal component analysis (PCA) was utilized to rank the influence of input variables on the output properties, enabling the selection of optimal mix proportions from the database through comprehensive performance evaluation of dual-output variables.

The Pearson correlation coefficient analysis indicates the rationality of input variable selection via confirming the absence of significant multicollinearity, thereby mitigating its adverse effect on the model predictions. During model training, the metaheuristic optimization algorithms significantly enhances the predictive performance of the Backpropagation Neural Network (BPNN), demonstrating that a hyperparameter optimization effectively improves the model capability. However, the generalization capability evaluation using a novel independent database reveals that three optimized models exhibit an inferior performance, compared to the unoptimized BPNN, contradicting their training-phase results. This discrepancy indicates overfitting in these three models during training, potentially attributable to excessive feature extraction induced by ten-fold cross-validation. In contrast, the Ant Colony Optimization-enhanced BPNN (ACO-BPNN) consistently demonstrates superior predictive accuracy and stability across both training and generalization assessments.

Furthermore, the principal component analysis (PCA)-based feature importance ranking identifies ground granulated blast furnace slag (GGBFS) as the most influential factor for 28-d compressive strength, while the coarse aggregate/fine aggregate ratio (CA/FA) exerts the dominant control over slump variability.

A hybrid-BPNN model capable of predicting both compressive strength and slump was trained and modeled in an entirely new database with appreciable accuracy levels. Five predictive models before and after optimization were established and compared. The ACO-BPNN had the optimum predictive accuracy (based on four evaluation indicators), and the strongest stability and robustness (based on the error bars) in testing process for both two properties. For the assessment of compressive strength prediction in testing process, the evaluation indicators could involve R²=0.932, MAE=2.972, RMSE=4.043, and MAPE=0.083. For slump degree prediction, the evaluation indicators could involve R²=0.929, MAE=12.212, RMSE=19.570, and MAPE=0.223. The WOA-BPNN, GA-BPNN, and GWO-BPNN algorithms exhibited pronounced overfitting tendencies when applied to multi-output variable modeling. This phenomenon could stem from excessive feature space optimization caused by high-frequency data reuse inherent in the ten-fold cross-validation framework. The principal component analysis (PCA) indicated that the relative importance of eleven input variables for compressive strength could be arranged in a descending order as follows: GGBFS; curing time, Na₂SiO₃, total aggregates, CA/FA, superplasticizer, OPC, SF, curing temperature, FA, and NaOH; and those for slump could be arranged in a descending order as follows: CA/FA, curing time, superplasticizer, SF, Na₂SiO₃, GGBFS, OPC, total aggregates, curing temperature, NaOH, and FA.

Concrete as the second-most consumed material globally after water plays a vital role in modern construction and infrastructure development due to its superior mechanical properties, longevity, and versatility. Conventional methodologies for concrete design predominantly rely on empirical approaches, often resulting in extended cycles for material development and suboptimal performance outcomes in practical applications. The advent of the Materials Genome Initiative (MGI), launched in the United States in 2011, highlights a need for data-driven approaches to material design. Concurrently, significant advancements in Artificial Intelligence (AI) have new avenues for enhancing material discovery and characterization. It is thus necessary to establish a robust and comprehensive database capable of accommodating the extensive, heterogeneous, and multi-scale data associated with various concrete properties and performance metrics. This study was to develop an innovative AI-powered intelligent design framework specifically tailored for a concrete material performance gene database. This framework could promise efficient data management and intelligent analytical capabilities and facilitate advanced material development and optimization.

This framework system integrated the information on concrete material composition, mix parameters, environmental factors, etc., and extracted the topological structure and coupling relationships between gene features to establish a concrete material knowledge graph based on conventional relational databases, realizing the visualization of gene feature relationships. This work designed a "gene importance-driven hierarchical density clustering" algorithm for quantitative analysis of concrete material gene features, extracting gene structure with different action relationships, via addressing the complex influencing factors and multi-scale coupling characteristics of concrete material performance. On this basis, this work also designed a closed-loop reasoning path from feature selection, model prediction, and contribution quantification for typical concrete material performance to reduce the cycle of traditional test methods, lower experimental costs, and achieve rapid evaluation and iterative optimization of concrete performance. This could provide a scientific theoretical basis for material selection via the performance prediction model as a core model and combining with multi-objective optimization algorithms. The framework could give a systematic technical framework and theoretical support for intelligent material research via implementing the whole-process design from concrete material data management to intelligent development, promoting the deep integration and innovative development of material science research and civil engineering decision-making.

The implementation of the AI-enabled framework demonstrates substantial enhancements in the accuracy and reliability of concrete performance predictions. The algorithms effectively uncover intricate and complex relationships between various material constituents, and their resultant influence on the critical performance indicators, such as compressive strength, durability, and workability. The unique integration of multi-source data within the database facilitates enhances feature extraction and visual representation of critical metrics, effectively addressing the intricate coupling effects that often complicate conventional testing methodologies. Through rigorous testing, validation, and benchmarking against existing standards, the framework exhibits exceptionally high accuracy and reliability in predicting concrete properties. This robust performance ultimately supports the creation of optimized mix designs, propelling advancements in concrete technology and application.

This study designed the AI-powered Intelligent Design of Concrete Material Gene Database, providing a systematic and technically sound foundation for concrete material research, and significantly enhancing the integration of material science principles and informed engineering decision-making. This study established a high-quality concrete gene database that could facilitate rapid evaluations and predictions via effectively leveraging advanced AI technologies. Furthermore, this study supported innovative advancements in concrete material design, paving a way for more efficient, sustainable practices in construction. Future research endeavors could broaden the system's capabilities, focusing on cross-disciplinary applications and the extension of the database to include low-carbon, environmentally-friendly material options.

Calcium aluminate silicate hydrate (CASH) is a principal binding phase in Al-doped cement systems, with the upper limits of macroscopic cementitious performance ultimately determined by its intrinsic nanoscale mechanical properties. Understanding the multiscale structure-property relationships is essential for guiding the design of improved CASH performance.The unclear submicron structure of CASH as a critical bridge between nanoscale behavior and macroscopic properties hinders the understanding of the evolution of multiscale mechanical properties. This study was to develop a CASH texture model from single crystals to polycrystals and to reveal the impacts of coupled Ca/Si and Al/Si ratios and water states on its physical and mechanical properties.

An upscaling algorithm was developed to remap coarse-grained models to all-atom models, overcoming barriers in constructing submicron textural structures, bridging experimental observations and molecular simulations, and providing some insights into CASH. This approach could enable the exploration of emergent cement phenomena in scales up to 100 nanometers with millions of atoms, offering a realistic representation of CASH pore structure, packing density, water content and state, as well as surface roughness. The resulting submicron-sized CASH was composed of randomly packed nanostructural units, reproducing structural features that were consistent with experimental observations. Molecular dynamics simulations were employed to reveal the multiscale structural evolution, the coupled effects of Ca/Si ratio and Al/Si ratio as well as the influence of multiple water states on the physical and mechanical properties of CASH.

The results indicate that a structural homogenization serves as a fundamental mechanism driving the transition of CASH from nanoscale anisotropy to submicron isotropy, with a disordered packing mitigating the interlayer fragility of single crystal CASH. When the Al/Si ratio increases from 0.05 to 0.10, Al-O bonds facilitate silicate chain polymerization, resulting in an enhancement of 15.40% in tensile strength. The submicron-sized CASH structure achieves an optimal mechanical performance at a Ca/Si ratio of 1.50 and an Al/Si ratio of 0.10. Further increasing the Ca/Si ratio leads to a reduction in strength due to a decrease in bridging Si-O bonds, whereas increasing the Al/Si ratio exerts an limited influence on the chain connectivity and framework stability. The cohesion of CASH is governed by intergranular contacts, and adsorbed water in the grain boundary regions is released as free water under humid conditions, significantly weakening cohesion and reducing strength by 40.80%.

In this study, an upscaling algorithm was developed to overcome barriers in constructing CASH textural models. Molecular dynamics simulations were employed to elucidate the multiscale structural evolution, Ca/Si and Al/Si coupling, and water governing the physical and mechanical properties of CASH. The findings could advance the atomic scale understanding of the submicron polycrystalline structure and mechanical behavior of CASH, paving a way for the design of more sustainable and durable cementitious composites.

Cement production is a highly energy and carbon-intensive process, with the industry contributing about 8% of global anthropogenic CO₂ emissions. Industrial solid wastes such as slag, fly ash, red mud, and coal gangue are produced in a large scale and persist in the environment, making their treatment and valorization a challenge. Replacing part of the clinker with supplementary cementitious materials (SCMs) is a proven strategy for lowering emissions. Nevertheless, many industrial solid wastes, such as red mud and coal gangue, remain underutilized due to their low reactivity and high impurity levels, causing a large variability in physicochemical properties. Such characteristics pose challenges for mix design due to complex mix proportion-performance relationships and restricted experimental trials.

Deep learning-based mix proportion design achieves a notable success due to its universal approximation capability. However, conventional models, such as deep neural networks, typically require large datasets to achieve robust prediction. Obtaining sufficiently large datasets for such models is highly challenging due to limited experimental trials in mix proportion design. Moreover, those methods output only a single optimal mix, failing to account for variability in physicochemical properties of some solid wastes across sources and batches. To address those problems, this study was to apply few-shot learning to develop a highly accurate and generalizable prediction model. In addition, a feasible-region-based design approach was also proposed to tackle physicochemical properties variability. Finally, a multi-objective optimization method was introduced to quantify trade-offs between carbon emission reduction and structural performance.

Furthermore, the proposed few-shot learning-based mix design method could optimize the feasible mix region for red mud-based supersulfated cement. This optimization demonstrated the effectiveness and applicability of the proposed methodology.

This research was to use few-shot deep learning to construct the performance prediction models for novel solid waste cementitious materials. A variational information bottleneck (VIB) neural network was developed, exhibiting superior generalization under small-sample conditions, compared with conventional networks. This model incorporated specialized preprocessing strategies addressing multicollinearity problems. A custom activation function for compressive strength prediction was designed, along with regularization techniques such as the Batch Normalization and Dropout.

To address challenges from variability in physicochemical properties, a feasible-region-based mix design approach was proposed, offering proportion ranges instead of single optimal solutions. This enhanced an adaptability to raw-material variations across batches and sources. Furthermore, a multi-objective optimization method was developed, incorporating carbon-emission factors to obtain mix proportions that could balance compressive strength and environmental benefits. A comprehensive performance metric, the eco-efficiency ratio, defined as 28-d compressive strength divided by total carbon emissions, was introduced, representing the structural performance per unit carbon emission.

In this study, the proposed few-shot deep learning-based mix design method is employed to optimize red mud‑based supersulfated cement. The results indicate that the proposed model outperforms conventional artificial neural networks (multilayer perceptron, MLP). Furthermore, the model maintains consistently small training-testing error gaps, indicating an effective overfitting prevention. Validation using ten verification mix proportions shows that the proposed model achieves lower prediction errors than the MLP for samples with substantially different mix proportions from the training data, confirming a superior generalization across the entire mix‑proportion space.

The feasible‑region‑based mix design approach can identify acceptable proportion ranges from the mix proportion-compressive strength landscape generated by the model. The results indicate that as clinker content increases from 0.1% to 5.0%, acceptable red‑mud content gradually decreases, while greater slag proportions contributed to meeting strength requirements. Also, the optimal 28-d compressive strength typically occurs when slag content ranges from 75% to 85% and gypsum content from 10% to 20%, consistent with supersulfated cement formulations, thereby supporting a reliability of the developed compressive‑strength prediction model.

The acceptable mix‑proportion ranges identified from the mix proportion-relative strength landscape indicate that the relative strength declines with increasing clinker content due to the higher carbon‑emission factor of clinker. Moreover, the results indicate that the mix proportions yielding the maximum relative strength lay outside the feasible region, because the carbon‑emission factor of slag is higher than that of other components, despite its superior reactivity. When producing red mud‑based supersulfated cement, it is necessary to choose a mix proportion based on the reactivity of the red mud to balance product performance and carbon‑reduction benefits.

This research demonstrated a potential of few-shot deep learning-based optimization methods for mix-proportion design of solid-waste cementitious materials. A variational information bottleneck-based modeling method was developed, incorporating a specialized preprocessing strategy for mix-proportion data, a tailored activation function for compressive-strength output, and regularization techniques. A feasible-region-based mix-proportion design method was proposed to address challenges from the variability of physicochemical properties, offering adaptable solutions for different sources and batches of solid waste rather than single optimal designs. The proposed method was applied to optimize a red mud‑based supersulfated cement. The results indicated that at 30% red mud content, the 28-d compressive strength could remain comparable to the control group without red mud. At 40% content, the optimal carbon emission benefit was achieved, while meeting the 28-d compressive strength requirements. This study could validate the effectiveness of the proposed few-shot performance prediction model and the feasible region-based mix proportion optimization method.

Steel corrosion is a critical deterioration phenomenon in concrete structures, particularly under harsh environmental conditions involving carbonation and chloride ingress. Accurate corrosion rate prediction is essential for structural life assessment and maintenance planning. The existing methods include empirical and electrochemical models; they suffer from a limited adaptability or a high computational complexity. Machine learning (ML) approaches show promise but typically focus on point estimates, while neglecting prediction uncertainty, which is crucial for reliable engineering decision-making. This study was to develop a Gaussian Process Regression (GPR) framework that could integrate physical prior knowledge with uncertainty quantification for steel corrosion rate prediction in cementitious materials, addressing two key questions, i.e., 1) how to enhance GPR accuracy and robustness for corrosion prediction, and 2) whether GPR uncertainty outputs could effectively support corrosion risk classification and engineering decisions.

The study utilized 180 experimental datasets from mortar samples embedded steel exposed to 6 relative humidity (RH) conditions after carbonation treatment. Each dataset contained 15 corrosion-related features categorized as mixture parameters (i.e., cement, slag, fly ash, and silica fume proportions, water-to-binder ratio, and chloride content), material properties (i.e., free chloride content, pH value, and porosity), environmental parameters (i.e., RH, saturation, and water content), and electrochemical parameters (i.e., corrosion potential, electrical resistivity, and [Cl^-]/[OH^-]).

The GPR model incorporated physical prior knowledge via setting the mean function as a linear relationship with a logarithmic chloride-to-hydroxide ratio, i.e., m(x) = a · lg([Cl^- ] / [OH^- ]) + b , based on the electrochemical corrosion theory. A Gaussian radial basis function served as a covariance function to capture smooth corrosion behavior variations.

The performance evaluation using 10-fold cross-validation demonstrates that the physical prior GPR consistently outperforms conventional GPR across all the feature combinations. The model shows a superior prediction accuracy (i.e., higher R²), a better uncertainty quantification (i.e., lower negative log-likelihood), and more reliable confidence intervals (i.e., higher prediction interval coverage probability).

The corrosion rates are classified into four levels, i.e., low (<0.1 μA/cm²), low-medium (0.1-0.5 μA/cm²), medium-high (0.5-1.0 μA/cm²), and high (>1.0 μA/cm²). The model achieves a high accuracy in identifying severe corrosion conditions, with most of the 159 high corrosion samples correctly classified, though some confusion exists between intermediate levels.

An uncertainty-driven decision strategy is developed using confidence interval width (threshold: 4 μA/cm²) combined with predicted corrosion levels to generate four response categories, i.e., immediate intervention (i.e., high corrosion + low uncertainty), manual review (i.e., high corrosion + high uncertainty), continuous monitoring (i.e., low corrosion + high uncertainty), and accept current condition (i.e., low corrosion + low uncertainty). Approximately 36% of samples are flagged for review or monitoring due to the high uncertainty, demonstrating a value of uncertainty assessment in risk management.

This work presented a framework combining physical knowledge with probabilistic modeling for trustworthy AI-based corrosion prediction. Key contributions could include 1) effective integration of electrochemical mechanisms into GPR architecture, improving model robustness and interpretability; 2) development of a joint corrosion level and uncertainty classification system; 3) establishment of an uncertainty-based engineering decision framework; and (4) validation that uncertainty quantification effectively could identify potentially problematic predictions requiring additional review.

The framework could advance AI applications in structural health monitoring from simple prediction tools to comprehensive decision support systems, providing a foundation for reliable, interpretable infrastructure maintenance strategies. The approach demonstrated a promising potential for enhancing structural safety through improved corrosion risk assessment and management in challenging service environments.

The global cement industry is a major source of CO₂ emissions, making its green transition an urgent priority. Utilizing industrial solid waste to prepare low calcium carbonatable binder (LCCB) is considered as a promising approach that can simultaneously achieve solid waste valorization and CO₂ sequestration. However, some impurity ions commonly appeared in solid waste, such as Mn ions from manganese slag, can dissolve into the mineral phases of LCCB, affecting its structure and carbonation behavior. The mechanisms underlying these effects remain unclear, hindering the optimization of material performance. This study was to clarify the solid solution behavior of Mn ions in LCCB and their mechanism of action during the carbonation process. The doping behavior of Mn ions in the main mineral phases was analyzed and their influence on the carbonation reactivity and microstructural evolution of the material was discussed via combining density functional theory calculations with experimental validation. This work could provide a theoretical basis for the design of high-performance LCCB materials based on manganese-containing solid waste.

LCCB clinker was synthesized with analytical-grade chemicals. To investigate the doping effect of Mn ions, MnO₂ was incorporated at varying levels (i.e., 0%(in mass), 0.15%, 0.25%, and 0.50%). The raw mixtures were calcined at 1280 ℃ for 120 min, followed by rapid cooling. The resulting clinker was ground and subsequently compacted at 4 MPa to form the specimens, which were then subjected to carbonation curing in 0.3 MPa CO₂ (99.9% purity) for 8 h. The microstructural morphology and elemental distribution were determined by scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectrometry (EDS) and backscattered electron (BSE) detection analysis. The phase composition was characterized by X-ray diffraction (XRD) coupled with Rietveld refinement. The CO₂ uptake was determined by thermogravimetric analysis (TGA), while the carbonation reactivity was assessed by isothermal calorimetry. The pore structure was characterized by low-field nuclear magnetic resonance (LF-NMR). The compressive strength was measured on a WDW-200 testing machine at a constant loading rate of 0.5 mm/min. Furthermore, the crystal models of CS, C₃S₂, and C₂AS were constructed based on the first-principles calculations to investigate the doping behavior of Mn ions.

This study reveals that Mn ion doping significantly enhances the carbonation hardening performance of LCCB via reducing the total bond order density of Ca—O bonds. Mn ions preferentially occupy Ca sites in LCCB, primarily because this substitution pathway has the lowest defect formation energy, and the resulting Mn-O bonds exhibit a chemical environment that is highly similar to the original Ca—O bonds, while maintaining analogous orbital hybridization characteristics. At an MnO₂ doping level of 0.5%, the material properties are improved. After 8-h carbonation, the compressive strength is increased by 80.70%, compared to the blank group, and the porosity is decreased to 20.29%. Mn doping promotes the formation of more CaCO₃, and its content is increased by 47.42% relative to the blank group, significantly increasing the proportion and thermal stability of calcite. The carbonation reaction calorimetry analysis shows that the exothermic peak and cumulative heat release in the doped system both increase, indicating a higher degree of reaction. The aforementioned enhancement effects are mainly attributed to the reduction in the total bond order density of Ca—O bonds due to Mn doping, which weakens the lattice's restraint on Ca²⁺, thereby promoting its dissolution and accelerating the carbonation reaction process. The microstructural analysis demonstrates that Mn doping leads to carbonation products dominated by layered calcite, forming a dense network-like skeleton. Meanwhile, unreacted mineral phases are encapsulated by amorphous phases, and CaCO₃ effectively fills pores, collectively contributing to the improvement in mechanical properties. This research provides a theoretical basis for the high-value utilization of manganese-containing solid waste in low-carbon cementitious materials.

The results indicated that Mn ion doping and its promoting effect on carbonation performance could be highly significant, especially at the optimal doping level (i.e., 0.5%). Under this condition, the carbonated samples exhibited a compressive strength increase of 80.70%, a porosity reduction of 20.29%, and a CO₂ uptake enhancement of 47.42%, compared to the blank group. Mn ions could substitute Ca sites and dissolve into various mineral phases of LCCB, inducing a lattice contraction and a rightward shift in diffraction peaks. This could weaken the total bond order density of Ca—O bonds, facilitating Ca²⁺ dissolution and accelerating the carbonation reaction process. Furthermore, doping could promote the formation of calcite with a higher thermal stability, whose layered stacking structure significantly improved the material densification and mechanical properties. This study could provide a theoretical basis and technical pathway for the resource utilization of manganese-containing solid waste in high-performance, low-carbon cementitious materials, having an important value for promoting the green transition of the cement industry.

Limestone Calcined Clay Cement (LC³) has attracted attention as a promising low-carbon cementitious material due to its reduced CO₂ emissions and satisfactory mechanical performance. However, its properties are strongly affected by raw-material composition, processing conditions, and mixture proportions. In this study, a database encompassing diverse material parameters and preparation processes was developed, and multiple machine-learning models were applied to predict the compressive strength of LC³ systems. The predictive accuracy of these models was further enhanced through the Bayesian optimization and the Boosting-based ensemble strategies. Based on the optimized predictive models, a multi-objective optimization framework was proposed to integrate compressive strength, materials cost, and embodied carbon emissions. A genetic algorithm was subsequently applied to identify optimal mixture proportions for LC³ matrices. The results showed that the proposed approach could achieve a balanced trade-off between mechanical performance and sustainability, thereby improving both the scientific robustness and practical applicability of LC³ mixture design. This work could provide a data-driven pathway for performance-oriented optimization of emerging low-carbon cementitious materials.

A database of 291 LC³ mixtures was compiled, covering a wide range of compositions and curing conditions. Input variables included clinker, calcined clay, limestone, gypsum, water-to-binder ratio, calcination parameters, curing conditions, and curing age. The compressive strength served as an output. Correlation analysis and variance inflation factor (VIF) evaluation were applied to reduce multicollinearity. Four machine learning models (i.e., Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Extreme Gradient Boosting (XGB)) were trained and tuned via the Bayesian optimization. An ensemble framework was then constructed with MLP as the meta-learner and XGB, RF, and GPR as base models within a Boosting strategy to enhance accuracy.

In parallel, a multi-objective optimization model was formulated, targeting compressive strength, material cost, and embodied carbon emissions. Cost and emission factors were drawn from market and life cycle databases. Practical constraints on binder fractions, water-to-binder ratio, and curing requirements ensured a feasibility. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to search for Pareto-optimal mixtures that reflect different trade-offs among strength, cost, and carbon footprint. Results and Discussion The ensemble learning notably improves prediction compared with individual models. The XGB provides the strongest performance among single algorithms, while GPR and RF also yield the good results. The MLP alone is weaker, but the predictive accuracy is further enhanced when integrating as a meta-learner in the Boosting ensemble. This underscores the advantage of combining diverse learners to model a complex LC³ behavior.

The variable importance analysis identifies curing age as a dominant positive factor, followed by calcined clay fineness and curing temperature. In contrast, the excessive replacement of calcined clay and limestone has a negative effect, revealing threshold behaviors. The interactions among clinker, calcined clay, and limestone confirm synergistic benefits in moderate levels, but dilution effects at a higher dosage. These findings clarify the nonlinear mechanisms affecting LC³ strength and validate the interpretability of the model.

The NSGA-II optimization generates a Pareto front of candidate solutions. A strength-prioritized mixture achieves mechanical properties, comparable to OPC concrete with a reduced carbon impact. A carbon-prioritized mixture minimizes emissions, while maintaining an acceptable strength, making it suitable for sustainability-driven projects. A balanced solution achieves compromise among all three objectives, representing a practical option for general engineering applications. The verification against experimental results confirms the reliability of predictions, demonstrating that the proposed framework can simultaneously address the performance, economic, and environmental targets.

This study introduced an integrated framework that could combine ensemble learning with a multi-objective optimization for LC³ mixture design. The main contributions involved, 1) Boosting-based ensemble learning significantly enhanced the accuracy of compressive strength prediction; 2) Curing age, calcined clay fineness, and curing temperature were identified as key positive drivers, while excessive calcined clay and limestone replacement exerted negative influences; 3) NSGA-II effectively produced Pareto-optimal mixtures balancing strength, cost, and carbon emissions, offering flexible solutions under different priorities; and 4) The experimental validation confirmed that optimized LC³ mixtures could maintain competitive strength, while substantially reducing both cost and carbon footprint.

The integration of predictive modeling and optimization provided a powerful pathway for advancing LC³ research and practice. It could contribute a methodological innovation and demonstrate a clear potential for sustainable, design of low-carbon cementitious materials in the construction industry.

Predicting the compressive strength of Steel Fiber Reinforced Concrete (SFRC) is challenging due to the complex, coupled influences of numerous mix design parameters. Traditional experimental methods are often costly and time-consuming for exploring this high-dimensional factor space. While machine learning (ML) models offer a powerful alternative, their practical application is hindered by two major limitations: the scarcity of comprehensive, high-quality experimental data which restricts model performance and generalizability, and the inherent "black-box" nature of advanced ML algorithms which obscures the underlying mechanisms linking mix proportions to mechanical properties. This study aims to address these critical gaps by developing a hybrid framework that integrates ML-based prediction, data augmentation via Generative Adversarial Networks (GAN), and post-hoc model interpretability using SHapley Additive exPlanations (SHAP). The objective is not only to achieve high-precision strength prediction but also to enhance model robustness with limited data and, crucially, to uncover the influential mechanisms of key components on SFRC compressive strength.

A database of 141 mix designs with 11 input features—Cement (C), Fly Ash (FA), Silica Fume (SF), Water (W), Coarse Sand (CS), Water Reducer (WR), Recycled Aggregate (RA), Natural Aggregate (NA), Fiber Length (FL), Fiber Diameter (FD), and Fiber Content Ratio (FCR)—and the corresponding compressive strength was compiled from peer-reviewed literature. Four tree-based ML algorithms (Random Forest, XGBoost, Stacking, and AdaBoost) were employed for regression modeling. To mitigate the data scarcity issue, a GAN architecture was implemented to generate 282 synthetic but physically plausible samples, expanding the training set. The quality of the GAN-generated data was validated using t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization and statistical moment analysis. The model trained on the augmented dataset was evaluated using the coefficient of determination (R²) and Mean Squared Error (MSE). Finally, the optimal model (Random Forest) was interpreted using the SHAP framework to quantify and visualize the contribution and interaction effects of each input feature on the model's predictions.

Using the original dataset, all ML models demonstrated competent predictive capability. The Random Forest model achieved the best performance with an R² of 0.93 and an MSE of 15.6 MPa², followed closely by XGBoost (R²=0.92, MSE=16.6 MPa²). The introduction of GAN-generated data led to a remarkable and consistent improvement in all models' performance. For Random Forest, the R² increased to 0.98 and the MSE decreased by approximately 56% to 6.9 MPa². The Stacking model achieved an R² of 0.99. This significant enhancement confirms that GAN-based data augmentation effectively enriches the feature space and improves the models' ability to generalize from limited experimental data.

The SHAP analysis provided profound insights into the governing factors of compressive strength. The summary plot identified cement, water, and silica fume as the three most influential features. Cement and silica fume exhibited strong positive SHAP values, highlighting their fundamental role in forming a dense cementitious matrix. The impact of water displayed a distinct non-linear relationship: optimal content promoted hydration and strength, while excess water reduced strength, likely by increasing porosity. The interaction SHAP dependence plots revealed nuanced coupling effects. For instance, the positive contribution of a high WR dosage was more pronounced and stable under conditions of lower water content, emphasizing the critical coupling between water and chemical admixtures in determining the water-to-cementitious materials ratio. In contrast, parameters related to fiber geometry (FL, FD) and content (FCR) showed relatively low and concentrated SHAP values near zero, indicating their secondary role in influencing compressive strength, which aligns with their primary function of improving toughness rather than pure compressive capacity. The SHAP force plots and Sankey diagram further illustrated how specific combinations of high cement/silica fume and optimal water content drive predictions toward high strength, whereas high coarse sand content or inappropriate water levels contribute to lower strength outcomes.

This work successfully developed an integrated framework combining ML, GAN, and SHAP for predicting and interpreting the compressive strength of SFRC. The main conclusions are as follows:1) Among the evaluated ML models trained on the original dataset, Random Forest provided the most accurate predictions for SFRC compressive strength.2) GAN-based data augmentation proved highly effective, substantially improving the predictive accuracy and generalization ability of all models, with performance metrics (R² and MSE) showing dramatic improvement.3) SHAP interpretability analysis quantitatively identified cement, silica fume, and water as the most critical factors affecting compressive strength, with water exhibiting a non-linear effect. The analysis also elucidated important feature interactions, such as that between water and water reducer. Fiber parameters were found to have a minor influence on compressive strength prediction.4) The proposed framework offers a powerful, data-driven tool that moves beyond mere "black-box" prediction. It enhances prediction robustness from small datasets and provides actionable, mechanism-based insights for optimizing SFRC mix design, thereby bridging the gap between empirical data mining and fundamental materials science understanding.

Early-age carbon dioxide (CO₂) curing of concrete is an innovative carbon utilization technology, enabling resource-oriented utilization of carbon dioxide that allows carbon neutrality. Thus, it is a promising technology for the concrete industry. Enhancing carbonation reactivity contributes to the promotion of CO₂ curing technology. Among all the influencing factors, previous studies showed that water loss during pretreatment could be one of the most important parameters affecting carbonation degree and mechanical properties of concrete.

In a previous work, water loss was primarily evaluated based on the overall water loss of specimens. Note that water loss varies significantly at different depths due to the complex pore structure and heterogeneity of cement-based materials. This causes depth-dependent carbonation reaction, forming gradients in both water content and carbonation degree. However, the existing studies neglect water loss gradient and its impact on carbonation. This is mainly due to a lack of direct visualization and continuous tracking of moisture distribution and carbonation products inside concrete by conventional testing methods, making it difficult to accurately characterize water loss gradients and carbonation gradients. A more precise and efficient water loss and carbonation characterization method is required.

This study was to apply X-ray imaging technology to non-destructively and quantitatively track the formation of water loss gradients and carbonation gradients in cement-based materials at different pre-drying durations.

This study cast cube specimens with the size of 40 mm for compressive strength tests and specimens with the sizes of 10 mm × 20 mm × 40 mm for X-ray imaging tests. The raw materials consisted of water and P.O.42.5 cement at a water-to-binder ratio of 0.4. X-ray imaging technology was employed to capture projection images of the 20 mm × 40 mm face of the specimens before and after pre-drying and accelerated carbonation.

Image processing for water loss distribution and carbonation distribution included field non-uniformity correction, normalization, image registration, grayscale difference calculation, water loss distribution characterization, attenuation coefficient variation calculation, carbonation-water signal decoupling and carbonation distribution characterization.

Furthermore, a phenolphthalein alcohol solution was used to validate carbonation depths determined by X-ray imaging. The mechanism of different water loss gradients on carbonation behavior was analyzed by thermogravimetric analysis (TGA). The porosity changes of carbonated specimens under different water loss gradients was characterized by low-field nuclear magnetic resonance, thereby elucidating the formation mechanisms of water loss gradients and carbonation gradients. The 3-d compressive strength of the specimens was determined using a universal testing machine.

Based on the X-ray projection images, water loss mapping and water loss-depth curves are obtained, revealing a decrease of water loss with increasing depth. Besides, the overall water loss increases with prolonging pre-drying duration. A substantial water loss difference appears between the surface and internal sample core, forming a distinct water loss gradient. The peak water loss and the water loss gradient both increase with extended drying.

In addition, 2D mapping of attenuation coefficient variation caused by carbonation and the corresponding variation along the depth can be acquired, introducing carbonation gradient and total attenuation coefficient change as indicators for carbonation behavior. Among all the samples, the sample CC18-0 exhibits the shallowest carbonation depth, with the maximum carbonation degree appearing at the surface. Carbonation depths of sample CC18-1.5/3/6 show a slight increase with drying time, while the decline rate within the partially carbonated zone remains consistent. Finally, the sample CC18-24 achieves the maximum carbonation depth of approximately 6.6 mm, while its peak carbonation degree is the lowest. Moreover, prolonged drying time leads to a significant decrease in the carbonation gradient, dropping progressively from 0.0088 mm^-1 for the sample CC18-0 to 0.0061 mm^-1 for the sample CC18-24 (a maximum reduction of 30.7%). In addition, the total attenuation coefficient change within the carbonated zone also increases with drying time (from 0.093 to 0.138).

A comparison of carbonation depths determined by X-ray imaging, thermogravimetric analysis, and the phenolphthalein test indicates that X-ray imaging is a more precise carbonation testing method. Furthermore, the porosity of carbonated specimens decreases as water loss during pre-treatment increases. The sample CC18-24 has the lowest porosity of approximately 25.29%. Eventually, the early-age strength of carbonated cement pastes slightly improve from hydrated samples.

The design of water loss gradients in cement-based materials prior to carbonation can be achieved via controlling the pre-drying duration. In general, short pre-drying period results in a gentle water loss gradient due to the larger pores and lower resistance to moisture migration. The high surface water content impedes CO₂ diffusion, causing the carbonation reaction to be dominated by the transport rate of CO₂. As a result, carbonation mainly occurs near the surface, leading to a high carbonation degree that is limited to a shallow depth. In contrast, a prolonged pre-drying promotes a higher hydration degree and pore refinement, which hinders outward moisture migration. This leads to a sharp increase in water loss at the surface, forming a steep water loss gradient. The low surface water content broadens the pathways for CO₂ diffusion, facilitating a deeper carbonation penetration. Meanwhile, the presence of continuous water films in the internal regions enhances the dissolution efficiency of calcium ions, resulting in a gradual carbonation gradient. The steep water loss gradient drives CO₂ to diffuse inward, thereby increasing the overall carbonation degree, which is evidenced by higher CO₂ uptake and calcium carbonate content. The increased calcium carbonate content further refines the pore structure, significantly reducing the total porosity of the matrix, which in turn enhances the compressive strength.

The samples subjected to shorter drying durations exhibited less and more uniform moisture losses from surface to interior. The relatively high residual moisture at the surface favored in-situ carbonation but limited CO₂ transport inwards, resulting in a higher surface carbonation degree with a shallower carbonation depth. Conversely, the samples dried longer experienced a greater moisture loss and an increased surface drying. The lower surface moisture facilitated CO₂ diffusion and slowed down calcium ion dissolution, leading to a relatively lower surface carbonation degree yet a greater carbonation depth. It was indicated that a steep moisture loss gradient promoted CO₂ diffusion, enhancing the overall carbonation degree of cement pastes. This significantly reduced total porosity, which consequently improved the compressive strength.

Rheological properties of cement paste are critical factors governing the workability of fresh concrete, significantly affecting construction performance, structural safety, and durability. Although rheometers provide direct measurements of parameters such as yield stress and plastic viscosity, their field application is often restricted by equipment costs and operational complexity. Consequently, the mini-slump flow test remains a prevalent method for evaluating workability due to its simplicity. Theoretical frameworks allow for yield stress calculation based on the final spread diameter. However, conventional analysis relies solely on the static final value, neglecting the dynamic flow process. This limitation results in a failure to capture dynamic information related to plastic viscosity, which dictates the spreading rate and flow duration. Recent computer vision technology has demonstrated a broad potential in construction materials research. Some previous studies attempted to record flow spread using cameras, they often suffered from reliance on manual data processing, insufficient precision, or requirements for expensive high-speed imaging equipment. To address these limitations, this paper was to propose a high-precision characterization method for the flow spread process of cement paste based on computer vision. This study could achieve automated, dynamic, and quantitative analysis of paste flow via integrating the You Only Look Once version 11 (YOLOv11) object detection model with the Segment Anything Model 2 (SAM2), and employing a dual geometric correction system, providing a robust data foundation for inverting rheological performance.

P·O 42.5 ordinary Portland cement, fly ash, and slag were utilized to prepare 80 groups of cement pastes with varying water-to-binder ratios (i.e., 0.45, 0.50, and 0.55) and mineral admixture dosages ranging from 0% to 50%. A polycarboxylate superplasticizer was added to regulate fluidity, creating a dataset with diverse rheological properties. The image acquisition setup consisted of a smartphone camera fixed 400 mm vertically above a 10 mm thick tempered glass plate, with an A3 paper sheet placed underneath as a spatial reference, recording videos at 1920 × 1080 resolution. The proposed characterization method followed a sequential pipeline comprising four stages, i.e., perspective correction, automated segmentation, refraction correction, and dynamic parameter calculation. First, to address trapezoidal distortion caused by oblique camera angles, a global homography matrix was calculated via detecting the four corners of the A3 reference paper in the final video frame. This matrix was applied to the entire video sequence to restore an orthogonal top-down view. Subsequently, a combined deep learning approach was employed for segmentation, where YOLOv11 detected the mold position in the initial frame to generate a "point prompt" that initializes SAM2. Leveraging its Video Object Segmentation (VOS) capability and memory mechanism, SAM2 automatically tracks and segments the paste mask throughout the sequence, effectively resolving occlusion issues where the mold or operator hands could block the paste during the initial lifting phase. Following segmentation, a refraction correction model based on Snell's law was applied to eliminate the visual displacement caused by light passing through the glass plate, establishing an accurate physical relationship between pixel width and true scale. Finally, the spread diameter D(t) and instantaneous velocity V(t) curves were extracted to quantify dynamic behavior based on the corrected masks.

The experimental validation confirms the high efficacy of the proposed method in both automated segmentation and measurement accuracy. In terms of segmentation, the prompt-based mode utilizing YOLOv11 and SAM2 proves superior to the automatic mask generation mode. The model effectively propagates mask information from clear final frames back to initial frames via leveraging the VOS function, generating continuous and precise contours even when the paste is partially occluded. Regarding measurement precision, the constructed dual correction system significantly mitigates errors. The experimental results indicate thatan optical distortion causes a maximum relative error of 4.1% without a refraction correction. The application of the refractive correction model reduces this error to within 1.6%. A validation study involving 80 groups of pastes shows a linear correlation between the calculated final diameters and manual measurements, with a coefficient of determination (R²) of 0.999, a Mean Absolute Error (MAE) of 0.87 mm, and a Mean Absolute Percentage Error (MAPE) of merely 0.49%. Based on the accurate extraction of D(t) and V(t) curves, four dynamic parameters were defined, i.e., final spread diameter (D_max), peak velocity (V_max), flow stabilization time (ts_table), and time to reach peak velocity (t_Vmax). Crucially, the analysis demonstrates that distinct pastes can exhibit nearly identical final diameters yet possess significantly different peak velocities and flow time. This discrepancy indicates that static measurements alone fail to capture variations in plastic viscosity and flow dynamics. The proposed dynamic indicators effectively capture these distinctions that conventional static tests fail to distinguish, offering a more comprehensive description of flow behavior.

This study established an automated segmentation method for cement paste via integrating YOLOv11 and SAM2, which could effectively solve the dependency on external prompts and address occlusion problems during the initial flow stage through a memory-based mask tracking mechanism. Furthermore, a dual error correction system incorporating perspective and refraction corrections was constructed to quantitatively rectify geometric and optical distortions, reducing the maximum relative measurement error from 4.1% to less than 1.6% and ensuring high fidelity of physical dimensions. Based on these foundations, a dynamic quantitative analysis method was proposed by defining key parameters such as D_max, V_max, t_stable, and t_Vmax based on the dynamic curves. Compared with conventional methods, this dynamic characterization could offer improved information density and automation, achieving a more comprehensive characterization of cement paste flow behavior through quantitative analysis of the flow process. Ultimately, the method could provide a technical support for the automated and intelligent testing of cement paste workability.

This review concludes that AI makes a great progress in predicting performance and optimizing mixtures, while there are still some challenges before it is widely used in real engineering. The main challenges include the lack of high-quality open datasets, the poor ability of models to work on new materials (generalization), and the need for better trust and interpretability. Future research needs to move from static predictions to autonomous systems.

In the future, an AI-driven autonomous system can be used for sustainable concrete. This involves building a cyber-physical system. In this system, sensors collect real-time data during concrete production. AI models can analyze the data and automatically adjust the mixture proportions to ensure consistent quality. This allows for adaptive manufacturing, reducing waste and ensuring that the concrete meets the design requirements. Furthermore, Digital Twin technology can extend this to the entire life of the structure. A digital twin is a virtual copy of the physical infrastructure. It can update itself using sensor data to monitor the health of the structure and calculate its carbon footprint in real time. This can favor to make better decisions about maintenance and repair, extending the service life of the infrastructure.

Finally, we need to focus on five priorities, i.e., creating open benchmark datasets for fair comparison; developing physics-informed AI that combines data with expert knowledge; advancing multi-objective optimization to balance performance, cost, and carbon; building cyber-physical systems and digital twins; and establishing standards and training programs. AI should not replace human experts. Instead, it should act as an intelligent assistant, helping engineers to design greener and more durable concrete. This human-AI collaboration is a key to achieving a carbon-neutral built environment in the future.

With the increasingly demand of ceramic components in biomedical engineering, microelectronic packaging, and related fields, the requirements for ceramic geometric complexity, accuracy and mechanical properties become popular. Injection molding technology possesses distinct advantages of high dimensional accuracy, superior shape fidelity, and elevated production efficiency for the mass production of geometrically intricate ceramic parts. This study fabricated fine-grained zirconia-toughened alumina (ZTA) ceramics via submicron powder injection molding and hot isostatic pressing (HIP). Furthermore, the rheological behavior of the feedstocks, thermal debinding kinetics, microstructure, and mechanical properties were systematically investigated.

A ZTA powder with an average particle size of 0.281 μm was used. The binders used consisted of polypropylene (PP, 99% purity; same for subsequent materials), polyethylene (PE, 99%), polystyrene (PS, 99%), ethylene-vinyl acetate (EVA, 98%), paraffin wax (PW 99%), stearic acid (SA 99%) and dibutyl phthalate (DBP, 99.5%). The mass ratio of the binder components m(PP/PE/PS) : m(EVA) : m(PW) : m(SA) : m(DBP) in the feedstock was 30 : 30 : 30:5:5. The ceramic powder and organic binders were fully mixed by an internal mixer (Guangdong C Fine Technology Co., Ltd, China) at 180 ℃ for 90 min, then crushed into granules of less than 5 mm. The solid loading of the feedstocks was 54%, 56% and 58%, respectively. Injection molding was performed using a model VE600Ⅲ-80hs commercial injection molding machine (Zhafir Plastics Machinery GmbH., Germany). Thermal debinding was conducted using a hot air circulation furnace (Wanlong Electric Furnace Co., China). The green bodies were pre-sintered in a muffle furnace (Ruibang Intelligent Technology Co., China) and then HIP post-treated using a model AIP10-30H system (AIP Co., USA). The rheological properties of feedstocks were tested at 190 ℃ by a model Rosand RH2000 capillary rheometer (Malvern Co., UK). The thermal decomposition behavior of binders and green bodies within the temperature range of 30-600 ℃ was examined using a model STA-449C simultaneous thermal analyzer (NETZSCH Co., Germany) coupled with a model TENSOR II FT-IR thermogravimetric-infrared spectroscope (BRUKER Co., USA). The microstructures of ZTA samples were analyzed by a model JSM-6700F field emission scanning electron microscope (SEM, JEOL Co., Japan). The hardness of the samples was measured using a model Instron Wilson VH1150 Vickers hardness tester (BRUKER Co., USA). The flexural strength of the samples was evaluated via the three-point bending method using a model PT-1176 universal testing machine (Baoda instrument Co., Ltd., China).

At 190 ℃, the shear viscosity of the raw material decreases with increasing shear rate, demonstrating the pronounced shear-thinning characteristics advantageous for injection molding. The feedstocks utilizing polypropylene as a skeletal binder exhibit a typical shear-thinning rheological behavior and a homogeneous microstructural morphology. At a shear rate of 100 s^-1, the shear viscosities of raw materials with solid contents of 54%, 56%, and 58% are 466.79 Pa·s, 900.90 Pa·s, and 2352.54 Pa·s, respectively. The non-Newtonian index reaches its minimum value (i.e., n=0.47) for the material at 56% solid-content. The TG and TG (DSC)-FTIR analyses reveals that the mixing process of binders does not affect the decomposition products, but affects the degradation process. The mixing of binders leads to an eutectic melting. Furthermore, a winding structure is formed between the straight chain part of DBP and other binder components due to a physical or chemical adsorption of binders on the surfaces of ceramic particles. Therefore, the initial degradation temperature increases. The ZTA ceramics with a high relative density of 98.81% and fine grains can be fabricated via air pre-sintering and HIP post-treatment technology. The ZTA ceramics pre-sintered at 1500 ℃ and HIP-ed at 1450 ℃ have superior comprehensive properties with a hardness of (21.73±0.42) GPa and a flexural strength of (656±24) MPa. Based on the optimized process, the fabricated ceramic capillary bonding tools exhibit excellent dimensional accuracy and superior surface finish.

In this work, fine-grained ZTA ceramics were fabricated via submicron powder injection molding combined with HIP. The feedstocks with polypropylene (PP) as a skeletal binder exhibited characteristic shear-thinning rheological behavior and homogeneous microstructure. At a solid content of 56%, the feedstocks demonstrated the minimum non-Newtonian index (n=0.47). During mixing, degradation products of the binder system remained stable, while preserving the mixing sequence, indicating partial processing compatibility among binder components. The ZTA ceramics pre-sintered at 1500 ℃ and HIP-ed at 1450 ℃ exhibited superior comprehensive properties (i.e., a relative density of 98.81% (4.15 g/cm³), hardness of (21.73±0.42) GPa, and flexural strength of (656±24) MPa). This study could realize the preparation of fine-grained, high-density ZTA ceramic capillary bonding tools, providing an effective pathway for manufacturing precision ceramic components with micro-scale structures.

A²⁺B⁴⁺Nb₂O₈-type ceramics (where A = Co, Ni, Zn and B = Ti, Zr) are critical materials for satellite communication and mobile base station applications due to their moderate relative permittivity (ε_r) and high-quality factor (Q × f). Among these, CoTi_0.2Zr_0.8Nb₂O₈ ceramic demonstrates superior microwave dielectric properties (i.e., ε_r of 26.5, Q × f value of 76921 GHz, and τ_f of-15×10^-6/℃). However, its high sintering temperature (1350 ℃) has a major challenge for practical implementation in low-temperature co-fired ceramic (LTCC) systems, where lower processing temperatures are essential for compatibility with silver electrodes and multilayer integration. This study was to prepare Cu_xCo_1-xTi_0.2Zr_0.8Nb₂O₈ (0.0≤x≤0.3) ceramic system via the partial substitution of Co²⁺ ions with Cu²⁺ ions and LiF flux doping. The optimal Cu²⁺ substitution level was determined via the systematic analysis of the quality factor (Q × f) of ceramics with varying compositions. LiF was introduced as a sintering aid, effectively reducing the sintering temperature of Cu_0.2Co_0.8Ti_0.2Zr_0.8Nb₂O₈ ceramic. The results demonstrated that Cu_0.2Co_0.8Ti_0.2Zr_0.8Nb₂O₈ ceramic with 30% LiF additive, sintered at 950 ℃ exhibited a superior compatibility with silver electrodes and achieved optimal microwave dielectric properties (i.e., a relative permittivity (ε_r) of 30, a high Q × f value of 44,571 GHz, and a temperature coefficient of resonant frequency (τ_f) of -42.0×10^-6/℃).

The raw materials were precisely weighed according to stoichiometric ratios and subjected to ball milling in a horizontal ball mill with zirconia balls with ethanol as a dispersant for 24 h. The resulting slurry was dried at 85 ℃ for 4 h and calcined at 900 ℃ for 3 h. Afterwards, the powders were further ground in the mill under identical conditions for 24 h to ensure the material homogeneity. After subsequent drying at 85 ℃ for 4 h and sieving through a 40-mesh screen, the finer powders were mixed with 5%PVA as a binder in an agate mortar, passed through a 200-mesh sieve, and uniaxially pressed into green pellets with 10 mm diameter× 5 mm height. These pellets were first debindered at 600 ℃ for 2 h before being sintered between 1200-1300 ℃ for 4 h. For low-temperature sintering optimization, LiF at different concentrations (i.e., 0-40.0%, 99.9% purity, Macklin Biochemical Co.) was introduced during secondary milling, and the modified samples were prepared under the same processing condictions but sintered at a reduced temperature of 950 ℃ for 4 h.

This study obtains a low-temperature sintering and performance optimization of Cu_xCo_1-xTi_0.2Zr_0.8Nb₂O₈ (0.0≤x≤0.3) ceramics via a strategy of partial Cu²⁺ substitution for Co²⁺ and LiF flux doping. The XRD patterns confirm the effective solid solution of Cu²⁺ without secondary phase formation, though the lattice contraction appears due to the smaller ionic radius of Cu²⁺ (i.e., 0.73 Å), compared to Co²⁺ (i.e., 0.74 Å). The sample with x of 0.2 demonstrates an optimal comprehensive performance.

For the introduction of LiF as a flux agent, the sintering temperature of Cu_0.2Co_0.8Ti_0.2Zr_0.8Nb₂O₈ can be reduced from 1200 ℃ to 950 ℃. The Rietveld refinement indicates the formation of a (Li, Cu)NbO₃ secondary phase, and the content increases at a higher LiF doping concentration (i.e., 10%-40%). The SEM images show that the 30% LiF-doped sample has the most homogeneous microstructure (grain size: 0.52 ± 0.12 μm) and a high relative density, leading to an optimal microwave property (i.e., ε_r = 30, Q × f=44,571 GHz, and τ_f = -42.0×10^-6/℃). The results of Co-firing experiments confirm an excellent chemical compatibility between the 30% LiF-doped ceramic and silver electrodes at 950 ℃ without any interfacial reactions.

This study prepared Cu_xCo_1-xTi_0.2Zr_0.8Nb₂O₈ (0.0≤x≤0.3) ceramic system via the partial substitution of Co²⁺ ions with Cu²⁺ ions and LiF flux doping, enabling dense sintering at 950 ℃. The optimal 30% LiF-doped Cu_0.2Co_0.8Ti_0.2Zr_0.8Nb₂O₈ ceramic sintered at 950 ℃ for 4 h achieved an exceptional performance (i.e., ε_r = 30, Q × f = 44,571 GHz, and τ_f = -42.0×10^-6/℃) with an excellent Ag electrode compatibility, demonstrating a promising potential for microwave dielectric applications.

Zirconia is a preferred material for thermal barrier coatings (TBCs) on the surface of super alloys in gas turbines due to its high melting point, low thermal conductivity and suitable thermal expansion coefficient. However, the tetragonal to monoclinic phase transition of pure zirconia in a service temperature range causes volume expansion and coating spalling failure. Y doping (YSZ) is commonly used to suppress this phase transition. With the development of gas turbines, higher requirements put forward for the service temperature and lifetime for TBCs. Sintering densification under long-term application of YSZ can increase the thermal conductivity and elastic modulus, which impairs its thermal insulation and damage tolerance. To address this issue, multi-element co-doping method was used to further improve the performance of YSZ. A doping material selection theory needs to be proposed from the point view of suppressing the tetragonal to monoclinic phase transition due to the large number of candidate dopant elements and complexity of doping composition ratios. Since the thermodynamic driving force for the tetragonal to monoclinic phase transition during cooling always exists, a key to stabilizing the tetragonal phase to room temperature increases the phase transition kinetic resistance. It is thus necessary to analyze the phase transition kinetic process to reveal the critical atomic relaxation mechanism related to high kinetic resistance, and propose theoretical strategies for stabilizing the tetragonal phase of zirconia.

In this study, a software package named VASP was employed for the relevant calculations of density functional theory (DFT). The electron-ion interaction was described by the projected augmented wave (PAW) potential function. The electron exchange-correlation energy was treated by the generalized gradient approximation (GGA). For elements Zr and O, their valence electron configurations were set as 4s², 4p⁶, 5s¹, 4d³ and 2s², 2p⁴, respectively. The cutoff energy of the plane wave basis set was set at 600 eV, and the k-point grid density based on the Monkhorst-Pack method was 4 × 4 × 4. During the structure optimization process, the energy convergence criterion was 1×10^-6 eV, and the force convergence criterion was 1×10^-4 eV/A.

The transition state search for the phase transformation from the tetragonal phase to the monoclinic phase was conducted by the solid-state nudged elastic band (G-SSNEB) algorithm in the VTST toolkit. During the transition state search, 12 images were inserted along the transition path, the maximum force threshold for the structural optimization of each image in the elastic band was set at 0.03 eV/A, and the energy convergence criterion was 1×10^-7 eV. The VTST toolkit was also used to analyze the Bader charge of different structures during the phase transition process. We employed a software package named LOBSTER to perform orbital decomposition of the electronic structures of tetragonal and monoclinic zirconia phases, and analyzed the chemical bond properties within the crystal via calculating the crystal orbital Hamilton population (COHP).

The average coordination numbers in a monoclinic phase are lower than those in a tetragonal phase. In the monoclinic phase, Zr atoms exhibit a 7-coordinate environment, whereas O atoms display a mixed coordination of 4-coordinated and 3-coordinated states in equal proportions. Conversely, in the tetragonal phase, Zr and O atoms are exclusively 8-coordinated and 4-coordinated, respectively. The crystal structure analysis and COHP characteristics reveal that the Zr—O bonds in the monoclinic phase exhibit shorter average lengths and higher bond strengths, compared to those in the tetragonal phase. In addition, the [OZr] polyhedral in the monoclinic phase displays lower distortion degrees. These factors constitute the thermodynamic driving force for the tetragonal-to-monoclinic (t-m) phase transformation of zirconia during the cooling process. This indicates two pathways for the t-m phase transition via performing the transition state search. The energy barrier in path 1 is only 2.4 meV/f.u.. There is a meta stable orthorhombic phase in path 2, and the energy barriers for tetragonal-to-orthorhombic (t-o) and orthorhombic-to-monoclinic (o-m) phase transitions are 6.33 meV/f.u. and 27.03 meV/f.u., respectively. In path 1, t-ZrO₂ directly transforms into m-ZrO₂ via the sliding of the (010) Zr atomic layer, which is indicative of a martensitic phase transformation. The transition resistance in path 1 is attributed to the breaking of 1/8 Zr—O weak bonds. In the first step of path 2, the phase transition resistance is similar to that in path 1. Specifically, the 1/8 Zr—O weak bonds are broken via increasing the distance between (100) Zr atomic planes without any sliding, leading to an atomic coordination environment in o-ZrO₂ that closely resembles that of m-ZrO₂. In the second step in path 2 (o-m), the sliding of Zr atomic plane (100) leads to the alternative stacking of 3 and 4 coordinated O atoms along the (100) direction, accompanied by a high energy barrier. The high transition resistance is related to the broken of weak Zr—O bonds and the stretch of strong Zr—O bonds and increase of distortion degree of [OZr] polyhedral. It is thus plausible to infer that stabilizing the metastable phase with a coordination environment analogous to that of the monoclinic phase can significantly enhance the kinetic resistance to phase transition. This stabilization can result in the phase transformation necessitating the redistribution of 3- and 4-coordinated O atoms.

The transition state search indicated two t-m phase transition paths. Path 1 involved a one-step direct phase transition with an energy barrier of 2.4 meV/f.u., characterized by the sliding of Zr atomic plane (010), consistent with the shear-dominant characteristic of martensitic phase transitions. The low phase transition resistance could be attributed to the breaking of 1/8 Zr—O weak bonds. Path 2 proceeded in two steps via a metastable orthorhombic phase, with respective energy barriers of 6.33 meV/f.u. and 27.03 meV/f.u.. The high phase transition resistance of path 2 could be attributed to the redistribution of O atoms with different coordination numbers in the second step, which was related to multiple structure changes, including weak bond breaking, strong bond stretching and local distortion degree increasing. Enhancing the stability of low-coordination metastable structures and introducing spatial distribution reconstructions of O atoms with different coordination numbers could serve as a theoretical mechanism for suppressing the tetragonal to monoclinic phase transition.

Silicon heterojunction (SHJ) solar cells have a high conversion efficiency, which drives the development of the photovoltaic industry. The transparent conductive oxide (TCO) films with a high electron mobility are needed to improve the conversion efficiency of SHJ solar cells. The mobility of the conventional 10% (in mass) SnO₂ doped In₂O₃ films is 30 cm²/(V·s), which cannot meet the demand of high mobility. Some reports indicate that the Ce, Ti, Zr, W, Hf, Mo, and Ta dopants with a low doping content can improve the mobility. A high density ceramic target is a key material for depositing TCO films. However, a low doping content is unconducive to sintering densification of ceramic target. Therefore, multiple oxide dopants with harmless sintering additive are used to enhance sintering densification and decrease resistivity. In this paper, an In₂O₃-based ceramic target doped with ZrO₂, CeO₂, Ta₂O₅, and TiO₂ was designed, and CeO₂ and TiO₂ were acted as sintering additives. In addition, the phase structure, microstructure, density, and resistivity of targets sintered at different sintering temperatures and times were also investigated.

High purity (99.99%) In₂O₃, CeO₂, Ta₂O₅, ZrO₂, and TiO₂ nanopowders were used as raw materials. The doping contents of CeO₂, Ta₂O₅, ZrO₂, and TiO₂ powders were 0.3%, 0.3%, 0.3% and 0.1%, respectively. Anhydrous ethanol was used as a dispersant, and the weight ratio of zirconia balls to powders was 3:1. The slurry was ground in a planetary ball mill at 360 r/min for 6 h, and then dried at 80 ℃ for 24 h. The dried powders were added with polyvinyl alcohol (PVA) at 5% aqueous solution, and then granulated through an 80-mesh sieve. The granulated powders were pressed into green bodies with a relative density of approximately 58%, a diameter of 13 mm, and a thickness of 1.5 mm. The green bodies were dewaxed at 600 ℃ for 3 h and then sintered in an oxygen atmosphere at a flow rate of 5 L/min. The targets were sintered at 1400, 1450, 1500, 1500 ℃ and 1600 ℃ for 4 h and 6 h, respectively.

The phase structure was analyzed by X-ray diffraction (XRD). The microstructure and elemental distribution were characterized by field-emission scanning electron microscopy (FE-SEM). The grain size and distribution were analyzed and statistically calculated from FE-SEM images using a software named Nano measure. The density was determined based on the Archimedes principle. The resistivity was evaluated using a four-point probe. The sintering densification behavior during the heating process was investigated by a dilatometric analyzer.

The densification and shrinkage of target during heating process can be divided into three stages. The target exhibits a rapid shrinkage after 1324 ℃, and its shrinkage rate at 1500 ℃ is 10.7%. This confirms the sintering additive function of dopants. The XRD patterns show the formation of a cubic bixbyite phase, indicating that Ce⁴⁺, Zr⁴⁺, Ta⁵⁺ and Ti⁴⁺ are dissolved in the lattice of In₂O₃. The grains are densely packed together, and the grain size gradually increases with increasing sintering temperature and holding time. All the targets show a dense internal microstructure and transgranular fracture behavior. The average grain size of targets sintered for 4 h increases from 1.44 μm at 1400 ℃ to 5.31 μm at 1600 ℃. A more substantial increase also occurs from 1.95 μm at 1400 ℃ to 10.41 μm at 1600 ℃ in the targets sintered for 6 h. Elements Ce, Ta, Zr and Ti exhibit a uniform distribution in In₂O₃ matrix, which helps to suppress an abnormal discharge during magnetron sputtering.

The density of targets sintered for 6 h is higher than that of targets sintered for 4 h. The density firstly increases and then decreases as the sintering temperature increases from 1400 ℃ to 1600 ℃. The optimal density of targets sintered for 4 h and 6 h is 99.05% and 99.39%, respectively. The resistivity of targets sharply decreases when the sintering temperature increases from 1400 ℃ to 1500 ℃, and then slightly increases with further increasing sintering temperature to 1600 ℃. The optimal resistivity of targets sintered at 1500 ℃ for 4 h and 6 h is 1.214×10^-3 Ω·cm and 1.181×10^-3 Ω·cm, respectively. The resistivity increase of target sintered at 1600 ℃ is due to the volatilization of In₂O₃ and density decrease of target at a high sintering temperature.

This work investigated the effects of sintering temperature and time on the microstructure and properties of In₂O₃-based targets doped with CeO₂, Ta₂O₅, ZrO₂, and TiO₂. The results showed that all the targets have single cubic bixbyite phase, dense microstructure. The transgranular fracture characteristics and uniform grains with sizes of <10 μm could be obtained. The doped multiple oxides exhibited a sintering additive function, promoting sintering densification. The sintering densification process could be accelerated after 1324 ℃. Raising sintering temperature and prolonging holding time could effectively improve the density and reduce the resistivity. The target sintered at 1550 ℃ for 6 h had a relative density of over 99% and a resistivity of less than 1.181×10^-3 Ω·cm. Therefore, multiple oxides doped In₂O₃-based targets with a low doping content could achieve high density and conductivity.

Perovskite solar cells (PSCs) as a representative of third-generation photovoltaic technology have achieved remarkable progress. Their power conversion efficiency (PCE) is significantly enhanced from the initial 3.8% reported in 2009 to the 27% in 2025. Compared to conventional designs that employ expensive hole transport layer (HTL) materials and noble metal electrodes, carbon is considered as an ideal substitute for noble metal electrodes due to its unique structural diversity, chemical stability, and rich surface chemistry. Also, a similarity between the Fermi level of carbon materials and that of metals gives carbon electrodes an advantageous edge in practical applications. HTL-Free carbon-based perovskite solar cells (C-PSCs) typically adopt an HTL-free structure, which simplifies the fabrication process and positions them as promising candidates for single-junction solar cells. However, a direct contact between carbon electrodes and perovskite surfaces inevitably exacerbates a non-radiative recombination that may occur during thin-film preparation. This study was thus to introduce guanidinium iodide (GAI) into perovskite films based on the FAMACsPbI₃ system. GAI could effectively passivate iodine defects and promotes crystal growth and enhance crystal stability through a robust hydrogen-bond network, thereby significantly improving the efficiency and stability of C-PSCs.

Perovskite precursor solutions were prepared via dissolving lead iodide (PbI₂), formamidinium iodide (FAI), methylammonium chloride (MACl), cesium iodide (CsI), and methylammonium iodide (MAI) in a mixed solvent of N, N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO), with different concentrations of guanidinium iodide (GAI) (i.e., 0, 1, 2 mg·mL^-1 and 3 mg·mL^-1) as dopants. The perovskite layer was then deposited on a substrate pre-coated with a TiO₂ layer by a one-step spin-coating method, followed by annealing on a hotplate at 150 ℃ for 13 min. A conductive carbon paste was uniformly blade-coated onto the surface of the perovskite active layer and annealed at 100 ℃ for 10 min to complete the fabrication of the perovskite solar cell.

The crystal phase and morphology of the films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The surface composition, chemical states, and elemental information of the perovskite films were determined by X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR). The optical absorption properties and energy band variations were analyzed by ultraviolet-visible (UV-vis) spectroscopy and steady-state/time-resolved photoluminescence (PL) measurements. The carrier separation/transport dynamics and defect state density in the devices were evaluated by electrochemical impedance spectroscopy (EIS), Mott-Schottky (M-S) analysis, space-charge-limited current (SCLC) measurements, and dark current-voltage (I-V) curves. The performance enhancement of GAI-doped perovskite devices was further investigated via measuring current density-voltage (J-V) characteristics under simulated sunlight.

The XRD patterns of samples doped with varying concentrations of GAI (i.e., 0, 1, 2 mg·mL^-1 and 3 mg·mL^-1) reveal that the perovskite lattice expands with increasing GAI concentration, as evidenced by a gradual shift of diffraction peaks toward lower angles. At a doping concentration of 2 mg·mL^-1, the perovskite exhibits an optimal crystallinity. The AFM and SEM characterization further demonstrates a reduced surface roughness, an enlarged grain size, and an improved film smoothness. The PL measurements show a significantly enhanced peak intensity at the GAI doping level of 2 mg·mL^-1, corroborating a positive role of GAI in promoting perovskite crystallization. The XPS and FTIR analysis elucidates the impact of GAI doping on the internal lattice structure, confirming the presence of hydrogen bonding (N—H...I) and clarifying the mechanistic role of GAI during perovskite crystallization. The effect of GAI doping on the carrier separation and transport is systematically investigated through EIS, Mott-Schottky (MS), SCLC, and dark current measurements. The results indicate that GAI incorporation increases a recombination resistance, suppresses a non-radiative recombination, facilitates a photogenerated carrier separation, and reduces a defect state density from 1.58×10¹⁶ cm^-3 to 1.17×10¹⁶ cm^-3. Consequently, the GAI-doped device achieves a champion power conversion efficiency (PCE) of 17.33%, outperforming the control device (15.7%). In addition, the results of stability tests demonstrate a remarkable improvement (i.e., unencapsulated devices retain approximately 95% of their initial efficiency after 600 h of ambient storage, whereas the control group degrades to below 90%).

This study proposed a low-concentration GAI-doped bulk passivation technique for perovskite films. The research demonstrated that moderate GAI doping could promote a perovskite crystal growth and significantly mitigate both bulk and interfacial defects. Consequently, GAI-doped perovskite films could effectively suppress a non-radiative recombination, enhance carrier transport, and substantially improve the efficiency and stability of HTL-free carbon-based perovskite solar cells (C-PSCs). A high power conversion efficiency of 17.33% was achieved through this strategy. The unencapsulated devices retained approximately 95% of their initial efficiency after 600 hours of exposure to ambient air, exceeding the performance of the control group. These findings could provide a novel research direction for developing highly efficient and stable C-PSCs.

Based on the comprehensive research, the FPC process demonstrates a remarkable potential in the preparation of high-performance ceramics, showcasing unique advantages in various ceramic systems such as alumina, silicon nitride, and zirconia. This process achieves microstructural and property optimization through multi-dimensional parameter regulation. A precise control of slurry solid content and particle zeta potential is critical for the stability and uniformity of foam structures. The pretreatment temperature alters the precursor's architecture, with optimal temperatures modulating the sintering activity of foam precursors and grain morphology to lay a solid foundation for subsequent sintering. Hot-pressing temperature and pressure are key parameters determining the final densification and microstructure of ceramics, where appropriate hot-pressing regimes effectively eliminate internal pores and enhance the overall performance of ceramics. The ceramic microstructures (i.e., phase morphology and distribution, as well as grain size, shape, and orientation) can be effectively regulated via finely tuning these process parameters, thereby achieving an in-depth optimization of the ceramic mechanical properties. In terms of process advantages, the foam precursor method enables a fine structural design via leveraging the ultra-high porosity of foam and single-grain-layer-thick pore walls. This significantly improves performance, notably enhancing the mechanical properties across multiple ceramic systems and reducing the dielectric loss of alumina ceramics. The process is relatively simple, requiring no complex equipment or workflows, and is environmentally friendly, lowering production costs and technical barriers to align with sustainable development needs. Moreover, the method offers a high flexibility, allowingthe adjustment of process parameters according to different requirements and integration with other processes such as spray drying and impregnation coating to prepare diverse high-performance ceramic materials. Its toughening and strengthening mechanisms are diverse, primarily including crack deflection and bridging caused by the lamination effect, toughening by intragranular defects (i.e., dislocations and twins), grain refinement and uniform distribution, toughening by anisotropic grain growth, and improved grain boundary bonding strength to alter the material's fracture mode. These results show some novel ideas and methods for the preparation of high-performance ceramics, greatly promoting the development of the ceramic materials field toward higher performance and broader applications.

Future research directions include the following aspects: In process optimization, the existing research on the synergistic mechanisms among various process parameters remains insufficient. In material system expansion, the process could be extended to conventional silicate ceramics, high-entropy ceramics, and other fields. In performance characterization, most of the existing studies focus on room-temperature mechanical properties, with less research on high-temperature mechanical behavior, thermophysical properties, and dielectric properties. From an application perspective, as science and technology advance, demand for high-performance ceramics in aerospace, electronics, energy, and other fields is growing, necessitating an accelerated resolution of engineering bottlenecks in the foam precursor method. In summary, the future development of the foam precursor method should focus on full-chain innovation spanning materials, processes, performance, and applications. It is essential to fully unleash the technology potential in structural design flexibility, green manufacturing compatibility, and multi-dimensional performance regulation to facilitate the large-scale application of ceramic materials in emerging fields.

BaTiO₃ is widely investigated as a piezoelectric catalyst due to its high safety, low preparation cost, stability, and environmental friendliness. This review provides a comprehensive review of the catalytic mechanisms of BaTiO₃-based piezoelectric materials, their energy sources, and recent advancements in the field of piezoelectric catalysis. The existing research into the applications of BaTiO₃-based materials primarily focuses on organic pollutant degradation, hydrogen production via water splitting, hydrogen peroxide synthesis, and biomedical uses. However, most studies remain in lab-scale and do not deal with industrial applications. The practical applications of BaTiO₃-based materials face some challenges despite having superior piezoelectric catalytic performance in various fields. Firstly, in practical applications, BaTiO₃ suffers from a low carrier concentration and a rapid recombination rate, restricting its catalytic efficiency. Secondly, there is a lack of comprehensive understanding of the piezoelectric catalytic mechanisms of BaTiO₃. The band theory and shielding charge effects are used to explain these mechanisms, but they do not fully elucidate all catalytic phenomena. The existing research shows that the piezoelectric effect of BaTiO₃ can enhance reactant conversion. However, the specific reaction pathways and intermediate formation mechanisms require a further investigation. In addition, the stability and reusability of BaTiO₃-based piezoelectric catalytic materials in practical applications need a urgent attention. To address these challenges, future research should focus on the following aspects:

1) Development of new material systems and optimization of modification strategies: Moving beyond conventional modification frameworks and exploring composition design methods utilizing machine learning can develop composite structures with gradient piezoelectric characteristics. A photopiezoelectric dual-drive catalytic system can be created to achieve a synergistic enhancement in carrier generation and separation via coupling plasmon resonance effects with piezoelectric responses. Interface engineering of two-dimensional materials, such as MXene, can offer new insights for constructing efficient charge transfer channels.

2) In-depth study of catalytic mechanisms: A multi-scale characterization platform is established via integrating in-situ transmission electron microscopy with ultrafast spectroscopy to monitor interfacial charge dynamics induced by piezoelectric potentials in real-time. Dynamic density functional theory (DFT) calculation methods are developed to elucidate the nonlinear coupling mechanisms among stress, potential, and catalytic activity, especially focusing on the formation and conversion pathways of transient intermediate states.

3) Expansion and optimization of practical applications: BaTiO₃-based piezoelectric catalysts in more complex real-world environmental systems, such as industrial wastewater treatment, are implemented to assess their performance and limitations, thereby promoting the industrial application of piezoelectric catalysis technology. In addition, material designs with self-healing capabilities are also explored to enhance long-term stability and recyclability, preventing secondary pollution.

This review systematically analyzes the theoretical basis, evaluation methods, and improvement methods of fracture strength reliability of advanced structural ceramic materials, which has a certain guiding significance for the study of fracture strength reliability of ceramic material systems exhibiting brittle characteristics. The Weibull theory is widely applied in the statistical field of strength discreteness of brittle materials such as ceramics, and has an irreplaceable engineering value in characterizing the reliability of fracture strength of ceramic materials. The core of improving the reliability of fracture strength of advanced structural ceramic materials lies in enhancing their ability to resist crack propagation and precise control of defects. The reliability of fracture strength can be improved via controlling the uniformity of grain size and microstructure distribution, adding second phases to construct toughening microstructures, optimizing molding processes to reduce the occurrence of defects in the preparation process, and repairing surface defects through secondary treatment. Future reliability research will develop intelligent design and preparation, high purity and uniformity control, biomimetic composite toughening, as well as fine processing and in-situ self-healing.

Nano-porous alumina thermal insulation materials have a significant application value in cutting-edge fields such as aerospace due to their lightweight properties, low thermal conductivity, and excellent high-temperature stability. This review primarily represents research progress on the preparation techniques and performance enhancement of nanoporous alumina thermal insulation materials for the past five years. Regarding alumina aerogel insulation materials, several challenges remain although extensive studies are conducted on precursor selection, sol-gel process refinement, drying method optimisation, and reinforcement composite development. These primarily include complex fabrication processes, high costs, safety concerns (associated with reactor usage), lengthy preparation cycles, and the inability to achieve continuous production. To address these issues, a subsequent research should focus on process optimisation and functional design. This includes developing novel precursors and refining drying techniques.

1) Enhancing sol-gel processes can strengthen wet gel structures, thereby reducing replacement and ageing times;

2) Exploring low-cost aluminium salts as precursors;

3) Eliminating high-pressure autoclaves for drying to lower equipment costs and energy consumption.

The existing literature on nano-alumina pressed thermal insulation materials remains relatively scarce. Their forming methods primarily comprise dry forming and wet forming. Dry forming relies on mechanical bonding forces between alumina powder particles, achieving a physical bonding through compression to connect particles into composite materials. However, this method exhibits low material reliability and inadequate mechanical properties, rendering it unsuitable for practical applications. In contrast, wet forming uses physical or chemical means to regulate particle distribution and bonding patterns. It utilizes liquid-phase media to disperse, align, and structurally stabilise nano-alumina particles, ultimately forming green bodies or materials with specific morphologies. Although this method yields aluminium oxide thermal insulation composites with a favorable formability, it still has some challenges like high density, complex processing, and susceptibility to cracking during liquid-phase media removal. Although post-processing techniques (such as sol-impregnation or high-temperature heat treatment) can be applied to nano-alumina pressed insulation green bodies, the thermal conductivity of the treated insulation materials increases significantly, making them unsuitable for many applications. To achieve both enhanced mechanical properties and low thermal conductivity in insulation composites, a future research needs to focus on curing the interfaces between nanoparticles to improve the material mechanical performance.

1) α-MnO₂ has superior stability and specific capacity. It is attributed to the double-chain structure of α-MnO₂, with a larger average cross-sectional area of tunneling, which is favorable for ion storage. The lamellar structure of δ-MnO₂ is prone to structural deterioration during cycling, leading to the irreversible growth of new phases, and the crystal structure of γ-MnO₂ consists of a random arrangement of 1 × 1 and 1 × 2 tunneling structures, which is unstable and prone to the transformation of the crystalline structure, restricting the insertion/extraction of Zn²⁺.

2) The in-situ-constructed α-MnO₂/rGO belongs to the optimum cathode materials for zinc ion batteries. The in-situ growth method enables the α-MnO₂ arrays to be anchored in the rGO surface layer (110). The Mn atoms on the crystalline surface and the rGO substrate are bonded by Mn-O-C covalent bonds, and the interface is in atomic-level contact, which reduces the charge transfer resistance, promotes the coordination adsorption of Zn²⁺, decreases the activation energy of extraction and blunts the active sites on the surface of rGO, and inhibits the occurrence of side reaction such as a hydrogen precipitation reaction.

3) The energy storage mechanism of MnO₂/rGO cathode material is mainly based on H⁺/Zn²⁺ insertion/extraction, supplemented by other energy storage mechanisms. In the process of H⁺ extraction, the exposure of surface active sites and short diffusion paths are crucial, so the specific surface area and interfacial proton affinity of rGO need to be optimized as a priority. Zn²⁺ extraction is limited by the ionic size and volumetric strain, so it is necessary to strengthen the conductive network and the structural stability and to design a suitable pore structure.

To further enhance the electrochemical performance of zinc ion batteries, the MnO₂/rGO composites still need to be further investigated from the following two aspects in the future:

1) Although the rGO composite strategy improves the performance of MnO₂ to a certain extent, the electrochemical performance of MnO₂/rGO composites can be further enhanced to meet the demands of high-efficiency and long-life zinc ion batteries if combined with a variety of strategies, such as pre-insertion of guest ions, defect engineering, and nanomodulation.

2) MnO₂/rGO cathode materials still face a problem of MnO₂ dissolution, leading to the loss of active material and reducing the battery capacity. Common solutions are to add Mn²⁺ salts to the electrolyte or to use solid-state electrolytes, which are costly and unfavorable for mass production applications. Therefore, exploring low-cost MnO₂ dissolution inhibition strategies will become an important aspect in zinc ion battery research, while the development of flexible, wearable, and high-security electronic devices will be the future development of zinc ion batteries.