Glass composition affects properties through structure, and this composition→structure→property relationship is one of the core topics in material science. Glass property prediction and composition design based on the simulation models become an important research interest in glass development. Glass structure gene modeling (GSgM) is a practical method to introduce the measured glass structure into simulation in a real sense. This paper was to deal with the theoretical background, model establishment, recommendation for glass design method, structural data collection as well as the limitations of GSgM. The application of GSgM in property prediction, such as physical, chemical, thermal and spectroscopic properties, and glass composition design were demonstrated in the study of laser glass and solidification glass for simulated radioactive nuclear waste.

The theoretical basis of GSgM was firstly introduced, and then the effective glass design method for the collection of simulation dataset was recommended in detail. In addition, the composition-structure (C-S), structure-property (S-P) models and the bi-directional modeling of C↔S↔P in laser glass and radioactive waste solidification glass as well as the error correction function of this method were also established.

GSgM can ignore the complexity of the glass system, and all the measured glass structure data can be used for modeling. If the fine glass structure analysis (i.e., NMR or synchrotron radiation) is not available, the Raman and IR spectra are the most convenient and universal methods for glass structure characterization, in which the integrated area from the Gaussian peak fitting can be used as the structure information in the modeling. The peak-fitting is recommended to follow the characteristic vibration bands reported in references in order to decrease the relative error of the structure information. In the study of simulated radioactive waste solidification glass, The S-P modeling of chemical stability, thermal property, liquidus temperature and Mo-yellow phase models based on limited experimental data are performed to exhibit a high simulation accuracy of glass property prediction when the structure is used to do the simulation, and the model validation proves the reliability of the method. In Nd:phosphate laser glass, although the spectroscopic properties perform highly a non-linear relationship to glass composition, they can be still designed in a high accuracy via bi-directional simulation of C→S→P and P→S→C. There is another function of GSgM on error correction of experimental data. In the modeling of T_g and T_L, the results show that samples with a large property measurement error can be corrected by GSgM simulation.

Glass structure gene modeling (GSgM) was a simulation method in which glass structure was used as a "bridge" to thread composition and property by C-S and S-P modeling. It could provide an effective prediction and optimization method for glass simulation. The high accuracy of GSgM could come from the transformation of the nonlinear relationship of C-P to two linear relationships of C-S and S-P. The GSgM approach was demonstrated as an alternative, powerful simulation method for glass modeling with limited data, and the relationship between glass composition, structure and properties could be further understood so as to achieve the more efficient simulation of glass design and glass property.

With the advancement of the Materials Genome Initiative and the establishment and improvement of the glass database, data-driven machine-learning methods penetrate the field of glass materials, and remarkable progress is made in the modeling of the physical, mechanical, and optical properties of glass materials. In recent years, oxide glasses have found increasingly widespread applications in the field of electronic applications, and their electrical properties as critical indicators have attracted much attention. However, modeling on the electrical properties of oxide glasses remains a challenge, and insufficient data suppress the model's performance. To address the issue of limited data availability on the electrical properties of oxide glasses, this work was to propose a data augmentation framework named WGAN-GP-CP tailored for glass materials.

Based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) model, an additional composition penalty term (CP) was added to the generator's loss function, and a method for evaluating sample quality was proposed, which could assess the quality of the generated samples from three aspects, i.e., diversity, accuracy, and uniqueness. Moreover, the XGBoost algorithm was employed for model training, and the model was interpreted through SHAP single-feature analysis and feature interaction analysis.

The trained generators can generate high-quality synthetic samples, expanding the existing datasets effectively. The results of comparative studies demonstrate that the data-augmented models exhibit mitigated overfitting and significantly enhanced generalization capability. The generalization performances of the room-temperature bulk resistivity, relative dielectric constant, and dielectric loss models increase from 0.784 to 0.838, 0.862 to 0.897, and 0.801 to 0.861, respectively. The results obtained from SHAP analysis are generally consistent with the classical physicochemical understanding of the electrical properties of oxide glasses. In addition, some interesting mixing effects and interactive relationships are also revealed.

The data augmentation framework proposed in this work offered a strategy for developing high-performance data-driven models under data scarcity conditions. The insights from SHAP single-feature analysis and feature interaction analysis could provide an important guidance for a deeper understanding of the relationship between the composition and electrical properties of oxide glasses. This research shifted the optimization of oxide glass electrical properties from empirically driven trial-and-error methods to data-driven scientific design frameworks, laying a foundation for the development of high-performance glass materials in the field of electronic applications.

As phase-engineered composites integrating amorphous and crystalline domains, glass-ceramics synergistically retain a high transparency of vitreous materials, while achieving ceramic-like mechanical robustness. Transparent glass-ceramics include systems such as ZnO-MgO-Al₂O₃-SiO₂, Li₂O-Al₂O₃-SiO₂, etc., in which the precipitation of spinel crystalline phase can be controlled in ZnO-MgO-Al₂O₃-SiO₂ (ZMAS) system. The spinel crystal phase has a high Mohs hardness as well as a high modulus of elasticity, so the ZMAS system glass-ceramics with a spinel crystal phase has superior mechanical properties. The introduction of Li₂O into ZMAS glass-ceramics can refine the grain size and also promote the precipitation of crystal phases such as β-quartz solid solution with a low coefficient of thermal expansion, which can adjust the overall coefficient of thermal expansion of the glass-ceramics and improve the thermal shock resistance of the glass-ceramics. β-quartz solid solution crystals are close to the refractive index of the glass, which can significantly reduce the scattering of light and improve the transparency of the glass-ceramics. In this work, Li₂O-ZnO-MgO-Al₂O₃-SiO₂ glass system was selected as a research subject to investigate the effect of Li₂O content on the crystallization behavior and properties of the glass. The study revealed that the introduction of Li₂O exerted a modulating effect on the crystallization behavior, optical properties, and mechanical performance of the glass-ceramics.

The parent glasses were prepared by a melting method, and the components were designed as (54-x) SiO₂-17 Al₂O₃-x Li₂O-3 Na₂O-14 ZnO-7 MgO-3 ZrO₂-2 SnO₂ (%, x=0, 2, 4, 6). The glass was melted at 1600 ℃ for 3 h. The glass solution was poured into a copper abrasive tool that was preheated at 590 ℃ for moulding. The formed glass blocks were annealed in an annealing furnace at 590 ℃ for 5 h to remove the internal stresses in the glass, and the glass was cut into blocks with the thickness of 1.5 mm for subsequent testing. The chemical state of the elements in the base glass was characterized by a model ESCALAB250Xi X-ray photoelectron spectrometer (XPS, Thermo Fisher Co., USA). A glass powder was used for testing, corrected using the electron binding energy of C1s. The DSC curve of glass was determined by differential scanning calorimetry (DSC).. The crystalline phase of the prepared samples was examined by an X-ray diffractometer. The base glass and microcrystalline glass microstructures were analyzed by a model Ultra Plus field emission scanning electron microscope (FESEM, Zeiss Co., Germany) with an additional Ultim Max 65 energy spectrometer (JSM-IT800). The optical properties of the glass and microcrystalline glass samples after double-sided optical grade polishing were characterized by a model UV3600 UV-visible spectrophotometer (UV-vis, Shimadzu Co., Japan). The Vickers hardness of the polished glass samples was measured by a model HV-1000A microhardness tester (Huayin Co., China) with a load of 1.96 N and a dwell time of 10 s. Each glass specimen underwent five replicate measurements to minimize experimental errors. The coefficient of thermal expansion (CTE) was determined by a model DIL 402C thermal dilatometer (Netzsch Co., Germany). The glass was cut into some rectangular bars with the sizes of 4 mm × 4 mm × 25 mm for dilatometric analysis.

The free oxygen content in the glass network increases with the increase of Li₂O content from 0 to 6% (in mole), leading to a reduction in the degree of network polymerization. This structural change can reduce the glass transition temperature from 745 ℃ (i.e., at 0 mol% Li₂O) to 664 ℃ (i.e., at 6% Li₂O). The β-quartz solid solutions form as the Li₂O content increases to 2%. The temperature required for the formation of β-spodumene crystals decreases as the Li₂O content further increases, leading to an increase in the β-spodumene crystalline phase content and suppression of spinel crystal precipitation. The transmittance also undergoes a sharp decline due to the influence of large-sized β-spodumene crystals. The glass-ceramics with 2% Li₂O content, after crystallization at 850 ℃, exhibit a superior performance (i.e., a Vickers hardness of 8.62 GPa, a transmittance of 84%, and a flexural strength of 199 MPa).

Increasing Li₂O content from 0 to 6% could promote the depolymerization of the glass network, leading to a reduction in the glass transition temperature. At 2% Li₂O, this composition effectively suppressed grain agglomeration and promoted the formation of β-quartz solid solution crystalline phases, thereby enhancing the transparency of the glass-ceramics. The coexistence of spinel phases and β-quartz solid solution improved the comprehensive properties of the glass-ceramics (i.e., a Vickers hardness of~8.62 GPa and a flexural strength of~199 MPa).

Nd³⁺-doped phosphate glasses, utilizing the ⁴F_3/2 → ⁴I_11/2 energy-level transition of Nd³⁺ ions, achieve 1.06 μm luminescence and have critical applications in optical communications, biomedical engineering, and defense technologies. However, the development of laser glasses depends on empirical trial-and-error approaches, requiring extensive experimentation to generate limited data. This paradigm suffers from inefficiency, prolonged cycles, high costs, and a lack of theoretical guidance. Moreover, the luminescent properties of rare-earth-doped glasses are closely tied to the local structure of the rare-earth ions. The intricate relationship between structure and performance renders most computational models for glass properties ineffective, severely hindering the rapid advancement of laser glasses. It is thus necessary to develop cross-scale computational methods that combine physical interpretability and extrapolation capabilities to enable accurate prediction and inverse design of luminescent properties for broad-composition Nd³⁺-doped phosphate glasses.

In this study, Nd³⁺-doped Li₂O-MgO-Al₂O₃-P₂O₅ glasses were prepared. The local microstructure of Nd³⁺ in the glass as a statistical ensemble of its local environments in neighboring glassy compounds (NGCs) was described. Based on this theoretical framework, an NGCs model was proposed to quantitatively predict the luminescent properties of Nd³⁺-doped phosphate laser glasses, positing that the glass's luminescent properties could be equal to the statistical average of the corresponding properties of its NGCs. This work could provide a theoretical foundation for quantitatively calculating the local structure and luminescent properties of rare-earth-doped laser glasses.

In rare-earth-doped laser glasses, the luminescent properties were determined via the analysis of the local structure of the rare-earth ions. The local structure of rare-earth ions in multicomponent laser glasses as a statistical ensemble of their configurations in NGCs was simulated based on the broken ergodicity theory. The NGCs in the compositional space (i.e., Li₂O·Al₂O₃·4P₂O₅, 9Li₂O·3Al₂O₃·8P₂O₅, P₂O₅, Li₂O·P₂O₅, Al₂O₃·3P₂O₅, Li₂O·2MgO·P₂O₅, MgO·P₂O₅, Li₂O·6MgO·3P₂O₅, Al₂O₃·P₂O₅, MgO·2P₂O₅ and 2MgO·Al₂O_3·P₂O₅) were identified via querying the Materials Project database for the quaternary phosphate system (i.e., Li₂O-MgO-Al₂O₃-P₂O₅). The local microstructure and luminescent properties of all glass compositions in this system were derived from these 11 NGCs by the NGCs model.

The molecular dynamics (MD) simulations were performed by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). The Morse potential, suitable for modeling short-range interactions in rare-earth-doped glasses, was employed. The long-range interactions were treated by the Ewald summation method (cutoff radius: 12.0 Å), while the short-range interactions were truncated at 8.0 Å. The glass quenching process was simulated at a fixed timestep of 2 fs. The initial structure was thermalized at 5000 K for 1.0 ns to eliminate memory effects. The system was then cooled to 3000 K in the canonical (NVT) ensemble at 5 K/ps. To simulate the experimental conditions, the system was equilibrated in the isothermal-isobaric (NPT) ensemble at 3000 K for 1.0 ns, allowing simultaneous relaxation of the simulation box dimensions and atomic positions. The further cooling to 300 K was conducted in the NPT ensemble at 0.5 K/ps, with intermediate equilibration steps at 2500, 2000, 1500, 1000 K, and 300 K (1 ns each), respectively. Finally, a 1 ns equilibration in the microcanonical (NVE) ensemble was performed, with atomic coordinates recorded every 5000 steps during the last 500 ps.

The radial distribution function g(r) and structure factor S(q), representing short- and medium-range structural orders, were calculated by both the NGCs model and MD simulations. The results from these methods have remarkable consistency, indicating the NGCs model's capability to quantitatively determine the local structural features of Nd³⁺ in Li₂O-MgO-Al₂O₃-P₂O₅ glasses. Since the luminescent properties of rare-earth-doped glasses are governed by the local coordination environment, this agreement validates the NGCs model for predicting luminescent performance.

The NGCs model is further applied to calculate key luminescent properties, i.e., the emission cross-section (σ_e) and fluorescence lifetime (τ) of the ⁴F_3/2→⁴I_11/2 transition. The σ_e values increase monotonically with Li₂O content, showing a maximum relative deviation of 1.72% from experimental data. For τ that is influenced by non-structural factors such as hydroxyl quenching and Nd³⁺ clustering, the maximum relative error is 6.19%. These results underscore the model's accuracy in predicting luminescent properties. A robust composition-property mapping is established via leveraging this model, enabling efficient screening of high-performance Nd³⁺-doped phosphate glass compositions.

A comprehensive analysis of 11,355 valid glass compositions within the glass-forming region reveals an inverse correlation between σ_e and τ, i.e., compositions with a higher σ_e typically exhibit a shorter τ. However, the model identifies a subset of optimized compositions with both high σ_e and extended τ, corresponding to Al₂O₃ (0-7%), MgO (10%-18%), and Li₂O (1%-7%).

This study applied the NGCs model to analyze the local structure and luminescent properties of Nd³⁺-doped Li₂O-MgO-Al₂O₃-P₂O₅ glasses. The calculated g_Re (r) and S_Re(q), reflecting short- and medium-range orders, aligned closely with the MD simulation results, indicating the model's precision in characterizing Nd³⁺ local structures. The model achieved exceptional accuracy in predicting luminescent properties, with maximum relative errors of 1.72% for σ_e and 6.19% for τ, compared to the experimental data. The systematic screening of the glass-forming region identified optimal compositional ranges (i.e., Al₂O₃: 0-7%, MgO: 10%-18%, Li₂O: 1%-7%) that could simultaneously maximize σ_e and τ.

The NGCs model could provide a groundbreaking approach for designing advanced Nd³⁺-doped phosphate laser glasses via establishing a quantitative link between atomic-scale structures and macroscopic performance. This methodology could accelerate material discovery and reduce reliance on costly experimental iterations, paving an efficient way for the development of next-generation laser materials.

In recent years, glass-ceramics have become a preferred material for new-generation glass covers due to their superior mechanical properties, excellent optical characteristics, and stable chemical performance. However, their inherent brittleness makes them prone to catastrophic fracture during crack propagation. To solve this problem, ion exchange technology as a strengthening method designed for aluminosilicate glass systems is widely adopted. This process significantly enhances mechanical properties such as hardness and flexural strength, while maintaining the substrate optical transparency. However, this process introduces a large amount of sodium and potassium ions onto the glass surface, which simultaneously instigates the degradation of chemical stability. This degradation primarily arises from water-induced corrosion, which occurs due to two sequential stages, i.e., leaching, where alkali metal ions (e.g., K⁺, Na⁺) undergo ion exchange with H₃O⁺/H⁺ in aqueous environments, generating an alkaline surface; and network dissolution, where hydroxyl ions attack and depolymerize the silicate network. Crucially, condensation droplets concentrate OH^- ions while atmospheric CO₂ forms highly corrosive mixed alkali carbonates under humid conditions, dramatically accelerating degradation. This fundamentally restricts the applicability of ion-exchanged glass-ceramics as an advanced display material, which a sustained surface integrity under environmental exposure is essential. It is thus necessary to improve the surface chemical stability of chemically strengthened microcrystalline glass in industry. To address this critical industrial challenge, this study was to introduce an immersion treatment using zinc chloride (ZnCl₂) solution designed to enhance chemical stability and impact resistance in lithium aluminosilicate (LAS) glass-ceramics simultaneously.

LAS glass-ceramic specimens (composition: 69.43% SiO₂, 5.62% Al₂O₃, 12.13% Li₂O, 6.21% ZrO₂, 5.27% P₂O₅, 1.34% Na₂O; primary crystalline phases: lithium disilicate and β-spodumene) were processed through sequential cutting, grinding, and polishing procedures to obtain glass samples with the sizes of 147.0 mm×72.0 mm×0.6 mm. The specimens underwent sequential chemical strengthening, i.e., immersion in molten NaNO₃ at 440 ℃ for 4 h, followed by molten KNO₃ at 410 ℃ for 0.5 h. Afterwards, an immersion treatment was performed in both distilled water and ZnCl₂ solutions at different contents (i.e., 0.05, 0.10, 0.20 mol/L) at 98 ℃ for 3.5 h, respectively. The treated and untreated specimens underwent accelerated weathering at 85 ℃/85% RH for 1, 4, and 6 d. The tests evaluated post-treatment efficacy for enhancing surface chemical stability and analyzed the underlying mechanisms. The elemental distributions (i.e., Na, K) on the surfaces and cross-sections of samples before and after treatment under different conditions were characterized by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). The surface roughness of samples was characterized by atomic force microscopy (AFM) across 30 μm×30 μm scan areas. The impact resistance of samples before and after treatment under different conditions was evaluated by drop-ball testing (132 g steel ball, maximum drop height of 1700 mm), and the results represented the average values from 10 samples per group.

Immersion in ZnCl₂ solutions significantly modifies the surface composition and properties of the chemically strengthened glass-ceramic samples. The SEM-EDS analyses indicates that this immersion process reduces the concentration of K⁺ on the surface, while leading to an increase in Na⁺ content. Particularly, ZnCl₂ solutions with higher concentrations enhance the outward migration of K⁺ from the subsurface region. The reason is since a weak acidity of ZnCl₂ solution promotes H⁺/OH^- neutralization, thereby enhancing the ion exchange process. In parallel, Na⁺ migration appears in the samples during immersed progress, particularly in distilled water. This is due to the reduced cross-linking density is caused by the formation of Si-OH and the fracture of Si-O-Si bonds after K⁺ ions on the surface are displaced. This induces a relaxation in the surface network structure, forming diffusion channels. Consequently, the weakly bound Na⁺ migrates outward under the synergistic effects of concentration gradients and structural defects. However, Na⁺ migrates in the samples treated with ZnCl₂ solutions, as the smaller, higher-charge Zn²⁺ ions fill in the surface pores.

For immersion, the surface roughness of the samples increases. The most severe damage, measured at 3.2 nm occus in samples treated with distilled water due to the sustained corrosion of the glass network. In contrast, ZnCl₂ solutions cause less severe, and concentration-dependence increases in the roughness (i.e., 1.5-2.2 nm). This is because H⁺ neutralization reduces the availability of alkaline media, thereby inhibiting silicate regeneration and limiting network degradation.

The results of accelerated weathering tests conducted at 85 ℃ and 85% relative humidity (RH) for 1, 4, and 6 ddemonstrate an enhancement in the chemical stability of samples treated with ZnCl₂. The untreated samples exhibit dense white spots after just 1 d and develop severe haze at 6 d. The samples immersed in distilled water show minor spots rich in Na⁺, while those treated with ZnCl₂ exhibit a minimal visible degradation. This improvement is attributed to two mechanisms, i.e., 1) a reduction in surface K⁺ concentration, which suppresses ion exchange driven by leaching, and 2) the diffusion of Zn²⁺, which blocks further alkali ion leaching and H⁺ adsorption.

The SEM-EDS analysis shows some distinct corrosion patterns. The untreated samples display severe dendritic K₂CO₃ aggregates and needle-like corrosion zones. The samples treated with distilled water show fine Na⁺-rich surface spots with limited corrosion. The samples treated with 0.05 mol/L ZnCl₂ have sparse deposits without significant elemental anomalies, indicating the blocking effect of Zn²⁺ against alkali migration.

The samples treated with 0.05 mol/L ZnCl₂ exhibit a maximum average impact height of approximately 1600 mm, which is 1.5 times greater than that of the untreated samples. This impact resistance improvement is attributed to the blunting of microcracks on the glass surface during mild corrosion.

This study investigated the effect of ZnCl₂ immersion on the surface chemical stability of chemically strengthened glass-ceramics. The results revealed that ZnCl₂ immersion significantly reduced surface alkali metal ion concentrations and suppressed their outward diffusion, substantially improving the chemical stability. The treated specimens exhibited superior corrosion resistance and surface integrity under accelerated aging (i.e., 85 ℃/85% RH). Also, ZnCl₂ treatment simultaneously enhanced the impact resistance. The samples immersed in 0.05 mol/L ZnCl₂ at 98 ℃ for 3.5 h achieved a maximum average impact height of 1600 mm (i.e., 132 g steel ball), which was 1.5 times greater than that of the untreated samples. These results indicated that ZnCl₂ immersion treatment could improve surface chemical stability and mechanical durability, having an effective surface-modification technology for glass-ceramics.

Silicate glass is a versatile material widely used in architectural windows, electronics, optics, and nuclear waste storage. As science and technology advance, demand grows for high-performance glass materials. The mechanical properties, which directly impact service life, safety, and functionality, have attracted much attention. Conventional glass development relies on empirical experience and trial-and-error methods, which are slow and limited in scope. In addition, the amorphous nature and broad compositional range of glass materials make designing tailored materials challenging, requiring composition optimization, cost reduction, and process assurance. Recent advancements on computational modeling, machine learning (ML), and materials informatics offer promising solutions. ML models can predict glass properties and accelerate the discovery of high-performance compositions via leveraging high-dimensional compositional data and integrating computational and experimental insights. However, ML effectiveness depends on dataset quality—availability, completeness, consistency, accuracy, and representativeness. In this work, three ML-based models were constructed from three distinct sources, and datasets were sourced from SciGlass (density), molecular dynamics simulations (Young's modulus), and a QSPR model (hardness). This approach could provide an innovative dataset construction strategy for advancing glass composition design.

In this study, ML models were developed to predict the density, Young's modulus, and hardness of silicate glasses. The dataset comprised 425 density values and 43 hardness values selected from the SciGlass database, 425 Young's modulus values obtained through MD simulations, and 50 hardness values predicted using a QSPR model. In the MD simulations, the Teter potential was employed, while the QSPR modeling incorporated descriptors was related to electronegativity. This work focused on six-component borosilicate glasses composed of SiO₂-B₂O₃-Al₂O₃-MgO-CaO-Na₂O. For initial data collection, a data preprocessing step was conducted, which included outlier removal, elimination of duplicate entries, and normalization of the compositional variables. A multilayer perceptron (MLP) neural network was used to model the relationship between glass composition and properties. The neural network architecture consisted of an input layer, several hidden layers, and an output layer, with weighted connections between neurons. The input features were the molar percentages of the oxide components, while the outputs were the corresponding physical properties. Non-linearity was introduced through the use of the rectified linear unit (ReLU) activation function in the hidden layers, enabling the model to capture complex nonlinear relationships. The output layer used a linear activation function to provide direct predictions of glass properties. To train and validate the models, the dataset was split into training and test sets in a ratio of 9∶1. This partitioning ensured sufficient training data, while reserving a subset for independent evaluation of model generalization. Model performance was assessed using mean squared error (MSE) and the coefficient of determination (R²). The trained models were subsequently used to predict the density, Young's modulus, and hardness of silicate glasses. Finally, nine formulations and corresponding properties were predicted from the models and experimentally verified.

The compositions of the dataset are widely distributed. The density of the glass varies between 2.3 g/cm³ and 2.8 g/cm³, Young's modulus is primarily distributed between 82 GPa and 115 GPa, and hardness values range from 5.0 GPa to 7.0 GPa. The QSPR model related to hardness demonstrates a good linear correlation between hardness and the F_net descriptor, with the R² value of greater than 0.80, indicating good model fitting and predictive performance. In this study, three ML models are developed to predict the density, Young's modulus, and hardness of silicate glasses. The R² values of the models show clear differences. The density-related model shows a relatively low predictive accuracy, with an R² of only 0.30 on the test set, the model for Young's modulus achieves an R² of approximately 0.70 and the hardness-related model exhibits the maximum performance, with R² of > 0.95. However, the predictive accuracy of all three models was satisfactory. The maximum relative error for density prediction is only 2.67%. The predictions of Young's modulus are overestimated by approximately 10 GPa, mainly due to the limitations associated with empirical interatomic potentials. The prediction error for hardness ranges from approximately 0.05 GPa to 0.40 GPa. The SHAP analysis is further used to investigate the contributions of individual oxide components to the properties. The results reveal that SiO₂ and Al₂O₃ contents positively affect both Young's modulus and hardness, while Na₂O negatively affects the glass density. As a result, the use of MD simulations and QSPR method represents a promising alternative in the absence of abundant high-quality experimental data for model training.

This study presented a comprehensive, multi-source modeling framework for predicting the mechanical properties of silicate glasses by integrating experimental data, MD simulations, and QSPR-based data augmentation. A total of 425 experimental density values, 425 MD-derived Young's modulus values, and 93 hardness values (43 experimental, 50 QSPR-predicted) were used to train three distinct ML models. Nine formulations were predicted and experimentally verified for three properties (i.e., density, Young's modulus and hardness) based on the three models above. The experimental results exhibited small relative errors, indicating a reasonable agreement with model predictions. The results of Young's modulus took into account the effect of errors introduced by simulation. The data quality could become a critical factor affecting the accuracy of predictive models as using ML method. This work incorporated homologous glass property data obtained from MD simulations and employed QSPR method for rapid data augmentation based on experimentally acquired data. This strategy could address the limitations commonly encountered in conventional database construction, including inconsistent data quality and slow acquisition of experimental measurements. It could provide a novel approach toward establishing a data-driven paradigm for glass materials research.

In recent years, microwave-absorbing composites that are both cost-effective and high-performance are developed to address the electromagnetic radiation issues like interference, pollution, and health risks. Spinel ferrite glass-ceramics as all-inorganic composites have attracted wide attention. They combine the magnetic properties of ferrite crystals with the advantages of glass, i.e., high temperature resistance, oxidation resistance, ease of formability, cost-effectiveness, and adjustable dielectric constant. Fe₃O₄ is widely used as an electromagnetic wave absorber due to its high saturation magnetization, strong spin-polarization, half-metallic properties, and low cost. Fe₃O₄ can also utilize industrial solid wastes like iron tailings as raw materials to achieve resource recycling. In this paper, silicate glass-ceramics containing flower-like Fe₃O₄ nanocrystals were prepared based on SiO₂-Al₂O₃-K₂O-Na₂O-P₂O₅-B₂O₃-Fe₂O₃ system via appropriate heat treatment. The special structure could enable the glass-ceramics to exhibit a higher reflection loss. A thorough examination of the mechanism of reflection loss was conducted, elucidating the key factors affecting dielectric loss and impedance matching on the microwave-absorbing performance of Fe₃O₄ glass-ceramics.

Raw materials (Sinopharm Chemical Reagent Co., Ltd. China) were weighed in a nominal composition of the glasses with approximately 30 g of each. Then, they were thoroughly mixed to ensure uniformity, and melted in a corundum crucible in an electric furnace at 1 580 ℃ for 2 h. Afterwards, the melt was poured into a mold for shaping. The as-quenched glasses were annealed at 500 ℃ for 1 h. Finally, the glasses were heat-treated at 620-740 ℃ with a heating rate of 10 ℃/min for 2 h. The samples were groundinto powders or cut and polished into small pieces for subsequent characterizations.

The crystalline phases of the samples were determined by X-ray diffraction (XRD). The crystallinity was determined via calculating the I_a/I_b value, where I_a is the crystalline phase intensity and I_b is the total intensity of the sample. The microstructures of the glass-ceramics were characterized by scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HR-TEM) with energy dispersive spectroscopy (EDS). The coercive field (H_c), initial magnetic permeability (µ_i), and magnetic saturation (M_s) of the samples at room temperature were determined by vibrating sample magnetometry (VSM). The glass powder was heated and thoroughly mixed with paraffin at a ratio of 4∶1, then pressed into a ring-shape with an outer diameter of 7 mm, an inner diameter of 3.04 mm, and a thickness of 2 mm in a mold. In the frequency range of 2-18 GHz, the electromagnetic parameters were measured by a vector network analyzer (VNA), and the reflection loss values of the samples were obtained based on the transmission line theory.

The results of scanning electron microscopy (SEM), vibrating sample magnetometry (VSM), and vector network analysis (VNA) reveal that an increase in temperature results in a gradual increase in the number of Fe₃O₄ grains, while the size of the flower-like structures formed by their aggregation initially increases and subsequently decreases. Also, the saturation magnetization (M_s) increases from 5.2 to 9.5 emu·g^-1 although the reflection loss exhibits an initial increase and a subsequent decline. When heat-treated at 700 ℃, the samples exhibit the most significant enhancement in Fe₃O₄ flower-like structures and the most optimal microwave absorption performance. The interfaces of the polyphase structure in the glass-ceramics generate polarization and relaxation, which enhance the dielectric loss. The mutual migration of electrons between Fe²⁺ and Fe³⁺ ions at octahedral sites induces a loss of conductivity. Meanwhile, electron hopping triggers a resonance, leading to an enhanced dielectric loss. The enhancement of crystallinity in the glass matrix promotes the magnetism, thereby leading to an improvement in magnetic loss. As the value of dielectric loss is close to that of the magnetic loss, the glass attains an effective impedance matching, thereby enhancing its reflection loss. This allows for enhanced penetration of electromagnetic waves into the material, leading to optimal reflection loss and effective absorption bandwidth at a thickness of 7 mm.

Silicate glass-ceramics containing flower-like Fe₃O₄ were prepared. The size of the flower-like structure was adjusted via heat-treatment temperature. At 700 ℃, the size of the flower-like structure was the largest. For a thickness of 7 mm, the reflectivity reached -18 dB, and the effective absorption bandwidth was approximately 2 GHz. Based on the analysis of the Debye equation and Cole-Cole plots, the larger flower-like nanostructures enhanced the reflection and scattering of electromagnetic waves during their transmission in the glass-ceramics. This led to an increase in dielectric loss and the formation of better impedance matching, revealing a multiple conduction-reflection-electromagnetic wave absorption mechanism.

Quartz glass is a type of inorganic amorphous material prepared by ultra-clean high-temperature melting process with high-purity natural quartz or synthetic silicon compounds as raw materials, having an outstanding physical and chemical property. This material possesses superior optical performance, extreme thermal stability, ultra-low thermal conductivity, and superior dielectric properties. In the development of optoelectronic technology industry in China, quartz glass with its unique synergy of physical and chemical parameters becomes a strategic basic material in the field of optoelectronic devices. The feature size of devices is in a hundred-micron scale based on the iterative evolution of microelectromechanical systems and wafer-level packaging technologies, further demanding a deeper analysis of the surface structure of quartz glass.

The glass was firstly prepared in a deposition furnace, and then heated in a high-temperature homogenization furnace at 1800 ℃ for 2 h. Afterwards, the glass was cooled to room temperature and discharged. The glass before and after homogenization was cut and polished to obtain glass samples with the sizes of 200 mm×200 mm×5 mm. Starting from the first point position in the upper left corner, some points were taken at intervals of 20 mm in all the directions (i.e., up, down, left, and right) for the analysis of Raman spectroscopy. In addition, the PCA algorithm was utilized for data dimensionality reduction and visualization, and the results of the changes in peak center and half-peak width were also analyzed.

Before homogenization, the standard deviations of LO_FWHM and D2_FWHM are 15.43 cm^-1 and 11.73 cm^-1 respectively, which are relatively larger than those of other indicators. The fluctuation range of the two sets of data is also larger, with the mean values of 114.13 cm^-1 and 47.30 cm^-1, respectively. The standard deviation of D1_center is only 0.18 cm^-1, indicating that the data for this indicator are concentrated, with small differences between each data point. The difference between the maximum and minimum values is also similar. For instance, the maximum value of LO_FWHM is 178.79 cm^-1 and the minimum value is 100.28 cm^-1; the maximum value of D2_FWHM is 115.58 cm^-1 and the minimum value is 42.77 cm^-1. This further indicates that the dispersion before homogenization of the two sets of data is large.

After homogenization, the standard deviation of D2_center is 0.39 cm^-1, which is decreased by 79%, compared to that before homogenization. The standard deviation of D2_FWHM is 0.82 cm^-1, which is decreased by 93%, compared to that before homogenization. The standard deviation of LO_FWHM is 8.03 cm^-1, which is decreased by 48%, compared to that before homogenization. The data fluctuations between different positions significantly reduce, and the standard deviation statistics for each indicator also show a certain degree of reduction. Among them, the influence of D2 peak is dominant, and the influence of D1 peak is subordinate. The D1 peak and D2 peak are respectively related to the tetra-siloxane ring and tri-siloxane ring in the structure. This indicates that on the same sample surface, the homogenization process has a dominant impact on the tri-siloxane ring in the silicon-oxygen structure. The tri-siloxane ring is expected in quartz glass because their Si—O—Si angle is less than the lowest energy (most likely) angle in the glass. The estimation of the energy required to generate such rings is more likely to reach the distribution of the D2 tri-siloxane ring. Also, the silicon—oxygen bond angles of all rings tend to be averaged, and the overall is more normalized.

The Raman spectroscopy could precisely characterize the microscopic structural differences of quartz glass before and after homogenization via detecting the vibration modes of atoms in the silicon-oxygen network (i.e., Si—O—Si bond angles and ring structure defects). For the analysis of structural uniformity, some parameters such as peak center dispersion and half-width were used to quantify the structural uniformity of the glass surface and bulk phase, and to identify high-strain areas (i.e., the regions with concentrated trimer ring defects). In the study of defect mechanisms, the D1 peak (tetramer ring) and D2 peak (trimer ring) defect characteristic peaks were utilized to reveal the migration and recombination laws of defects during homogenization, and to clarify the influence of thermal stress release on structural relaxation. Finally, combined with the data mining technology of the PCA algorithm, a correlation between the Raman parameters and homogenization processes was established, thus providing a theoretical support for precisely regulating the microscopic structure of quartz glass.

Low-temperature co-fired ceramic (LTCC) serves as a critical material for high-performance electronic packaging. Reducing the dielectric loss of LTCC materials can minimize signal attenuation in high-frequency transmission and enhance circuit integration. Based on material composition, LTCC materials can be classified into three categories, i.e., glass-ceramic systems, ceramic systems, and glass/ceramic composite systems. Glass-ceramic systems typically exhibit a low dielectric loss but suffer from inferior mechanical properties and corrosion resistance. Ceramic systems are often employed in medium- and high-dielectric applications, such as dielectric resonators. Reducing the sintering temperature is a key challenge for this system. Glass/ceramic composite systems offer a balanced performance. Their properties can be tailored via adjusting the ratio of the two phases. The La₂O₃-B₂O₃ glass/Al₂O₃ ceramic system serves as one of the representative systems. The sintering and crystallization behavior can be controlled via modifying the glass composition. The La₂O₃-B₂O₃-CaO-P₂O₅ (LBCP) glass/Al₂O₃ composite system demonstrates a significant application potential due to its ability to crystallize low-loss phases such as LaBO₃, LaPO₄, and CaAl₂(BO₃)₂O during sintering. However, the dielectric loss (tanδ > 1×10^-3) of the existing LBCP/Al₂O₃ composites requires a further improvement. In this paper, alkali metal oxides (i.e., Na₂O and Li₂O) were introduced to control the glass crystallization behavior, and the sintering process was refined to enhance the formation of beneficial crystalline phases (i.e., CaAl₂(BO₃)₂O) to develop low-loss LTCC substrates.

La₂O₃-B₂O₃-CaO-P₂O₅-R₂O glasses were prepared by a high-temperature melting method. Analytical-grade raw materials were weighed according to the designed composition and thoroughly mixed. The mixture was melted at 1400 ℃ for 1 h, followed by rapid quenching in deionized water to obtain crushed glass. The crushed glass was ground in a ball mill to obtain glass powders with a median particle size (D₅₀) of 2-3 μm. The glass powder was mixed with solvent and binder to make the slurry, and then the slurry was casted into green tapes. The glass powder was mixed with Al₂O₃ powder in a mass ratio of 45∶55 and casted into LTCC green tapes. Glass and LTCC green tapes both were cut, stacked, and laminated to form green blocks, which were subsequently sintered into dense ceramics.

The structure of the base glass was characterized by solid-state nuclear magnetic resonance spectroscopy (NMR) and Raman spectroscopy. The thermal behavior of the samples was detremined by differential scanning calorimetry (DSC). The phase composition of the sintered samples was identified by X-ray diffraction (XRD). The microstructure of the samples was determined by scanning electron microscopy (SEM). The porosity was quantified by processing the microstructural images via a software named ImageJ. The dielectric constant and dielectric loss of the samples at 5 GHz were measured by a vector network analyzer.

The LBCP glass network consists of low-polymerization borate and phosphate groups. The introduction of alkali metal oxides further disrupts the glass network, increasing the proportion of small depolymerized borate and phosphate groups, while reducing the overall network polymerization degree. The LBCP glass exhibits a great crystallization due to the weakened integrity of the glass network and the enhanced short-range ordering induced by high-field-strength La³⁺ ions. In sintering at 850 ℃, the LBCP glass crystallizes abundant crystalline phases LaBO₃, CaB₂O₄, and LaPO₄. The introduction of alkali metal oxides further enhances crystallization, reducing the activation energy for crystallization and lowering both crystallization and polymorphic transition temperatures, without altering the types of crystalline phases. Note that Li₂O demonstrates a greater effect on promoting glass crystallization, compared to Na₂O. This great crystallization promotes more complete growth of crystals LaBO₃ and LaPO₄, and the dielectric constant and dielectric loss decrease. The great crystallization behavior results in an increased porosity in glass sintering.

The LBCP glass/Al₂O₃ composite precipitates multiple crystalline phases LaBO₃, LaPO₄, and CaAl₂(BO₃)₂O after sintering. During sintering, CaB₂O₄ reacts with Al₂O₃ to form CaAl₂(BO₃)₂O, resulting in the disappearance of CaB₂O₄. The Al₂O₃ particles increase the resistance to densification shrinkage, leading to a higher porosity. The sintered composite samples exhibit superior dielectric properties due to the high content of Al₂O₃ and CaAl₂(BO₃)₂O. The introduction of Na₂O enhances mass transport, promoting CaAl₂(BO₃)₂O precipitation and further reducing both the dielectric constant and loss. In contrast, Li₂O addition causes a premature crystallization, increasing glass viscosity and hindering CaAl₂(BO₃)₂O formation, thereby deteriorating dielectric performance. The optimization of sintering conditions indicates that CaAl₂(BO₃)₂O precipitation occurs at a heating rate of 2 ℃/min, achieving optimal dielectric properties (i.e., ε_r of 6.70 and tanδ of 0.80×10^-3).

The glass network of LBCP glass consisted of low-polymerized borate and phosphate groups, exhibiting a great crystallization. LBCP glass crystallizes LaBO₃, LaPO₄ and CaB₂O₄ when sintered at 850 ℃. Introducing alkali metal oxides further depolymerized the glass network, enhancing a crystallization behavior with an improved grain growth, reducing both dielectric constant and dielectric loss. However, this greater crystallization also increased the porosity of the sintered glass. After sintering, the LBCP glass/Al₂O₃ composite material precipitates a variety of crystal phases, mainly including LaBO₃, LaPO₄ and CaAl₂(BO₃)₂O. High contents of Al₂O₃ fillers and CaAl₂(BO₃)₂O phase could improve the dielectric properties of the sintered composites. The addition of Na₂O promoted CaAl₂(BO₃)₂O formation, thereby reducing both the dielectric constant and dielectric loss of sintered composites. In contrast, Li₂O addition induced a premature crystallization, increasing glass viscosity of the glass phase and inhibiting CaAl₂(BO₃)₂O formation, thereby elevating dielectric loss. Optimizing the heating rate to 2 ℃/min could facilitate complete sintering and maximize CaAl₂(BO₃)₂O content, yielding LTCC materials with optimal dielectric properties (i.e., ε_r of 6.70 and tanδ of 0.80×10^-3).

With the rapid iteration of smart displays, especially smartphones, cover plate glass as one of the key materials has made recent progress, becoming an important component in the field of electronic information display applications. Aluminosilicate glass with its unique properties (i.e., good impact resistance, scratch resistance, and high hardness, as well as excellent chemical and thermal stability) is an ideal candidate material for plate cover glass. There is relatively in-depth research on the influence of composition changes in aluminosilicate glass on its structure and properties, but most of the existing research focuses on the short-range structure (<5 Å) of the aluminosilicate glass network structure, while there is less research on its medium-range structure (5-20 Å). In this work, the influence of multi-layer network structure of aluminosilicate glass on its properties was investigated by molecular dynamics simulation and experiments.

The relationship between the composition-structure-properties of glass was investigated by experimental methods and molecular dynamics simulations. The glass samples were prepared by a conventional high-temperature melting method. The raw materials were weighed and mixed uniformly according to the molar ratio of oxides, put into a platinum-rhodium crucible (90% Pt and 10% Rh), and then heated in a high-temperature lifting furnace at 1640 ℃ for 2 h. After melting, the crucible containing the molten glass was removed and poured onto a preheated copper mold. Once it solidified, it was transferred to an annealing furnace, followed by natural cooling to room temperature to eliminate internal stresses in the glass, resulting in uniform, transparent bulk glass. The glass was then cut and mechanically polished to obtain the samples with various sizes.

The glass samples were simulated, and the simulated structure was a cubic box composed of approximately 10000 atoms. The initial dimensions of the simulation box were calculated based on predicted density and atomic mass. The initial structure was optimized by a soft potential to ensure structural integrity and prevent atomic loss. The potential function used was the Buckingham potential, and the Ewald summation method was used to handle long-range Coulomb interactions. The cutoff radius was set to 10 Å, with an integration time step of 0.001 ps. The initial structure was randomly generated by an open-source software named ATOMSK.

The statistical results of the short-range structural characteristics of silicate glass indicate that there are no significant changes in the position and intensity of the O—O peak, and the intensity of the Al—O peak near 1.7 Å gradually increases as Al₂O₃ content increases. The number of Al—O structural units in the glass network structure increases, having a clear symmetrical distribution with aluminum primarily in a four-coordinate environment. The strength of the Si—O bond decreases with the reduction of SiO₂. The number of [SiO₄] tetrahedral groups decreases, and some alkaline earth metal ions in the glass network structure transform into ions that serve to compensate for the charge of the [AlO₄] tetrahedral groups.

The statistical analysis of the medium-range structural characteristics of aluminosilicate glass indicates that as Al₂O₃ content increases, the Q value of the first sharp diffraction peak (FSDP) shifts towards higher values, the ring size distribution shows a certain sharpening degree with the decreasing proportions of small rings (i.e., 3-membered and 4-membered rings) and large rings (i.e., ≥7-membered rings), while increasing the proportions of medium-sized rings (i.e., 5-membered and 6-membered rings). The smaller rings, such as the three-membered and four-membered rings, typically possess a higher energy and are more prone to structural rearrangement or breakage, whereas the larger rings, such as the five-membered and six-membered rings, are relatively stable. This indicates a reduction in the medium-range disorder of the glass network structure, resulting in a denser network structure.

The mechanical properties of the glass (i.e., density, elastic modulus, and Vickers hardness) gradually increase with the increase of Al₂O₃ content. The mechanical characteristics of the glass are positively correlated to the density of its network structure. The increase in the content of the [AlO₄] network structure makes the network structure denser, while the combined effect of [AlO₄] and [SiO₄] gradually enhances the integrity of the glass network. This results in a decrease in the average coefficient of thermal expansion, an increase in the strain point and softening point, and an increase in high-temperature viscosity.

The relationship between the composition-structure-properties of aluminosilicate glass was elucidated through experiments and molecular dynamics simulations. The substitution of Al³⁺ for Si⁴⁺ induced changes in the short-range and medium-range network structures of the glass, leading to an increase in the bridging oxygen content and a sharpening of the ring size distribution. The structural factor could shift towards higher values at the position of the first sharp diffraction peak (FSDP), resulting in an increase in the order of the short-range and medium-range network structures, and making the network structure denser. Consequently, the density, elastic modulus, Vickers hardness, strain point, softening point, and viscosity of the glass melt all increased, while the average coefficient of thermal expansion decreased.

Electrochromic device (ECD) is comprised of five superimposed layers, i.e., transparent conducting layer, electrochromic layer, ion conductor layer, ion storage layer and another transparent conducting layer, respectively. It can modulate the amount of solar radiation transmitting through it under applied voltage, often used as smart windows or facades to adjust the light and heat entering buildings proactively, which can sufficiently reduce the energy consumption. The existing ECDs are mainly fabricated by a magnetron sputtering method, which has a low productivity and high cost, especially for the ion conductor layer. Sol-gel method is a simple and relatively cheap method for preparing thin films, and massive researches are conducted on sol-gel electrochromic thin films such as tungsten oxide, nickel oxide etc.. In this work, an ion conductor layer was prepared by a sol-gel method, and its application in electrochromic device and their performance were also investigated.

Lithium tantalite sol was synthesized with lithium acetate (LiAc) and tantalum pentaethoxide (TaEt) as precursors and 1,2-Propanediol as a solvent. The molar ratio of LiAc and TaEt was 1∶1. Lithium tantalite thin films were prepared by a spin coating method and heat-treated in air at 400 ℃ for 2 h. Some of the sol was dried and the subsequently formed powder was heated at 600-700 ℃. The phase compositions of the films and powders were characterized by X-ray diffraction (XRD), and the electrochemical properties of thin films were analyzed.

The electrochromic devices with a structure of ITO/NiO/LiTaO₃/WO₃/ITO were prepared by PVD method and subsequent sol-gel method. The tungsten oxide or nickel oxide thin film layer was firstly sputtered on the ITO glass and then lithium tantalite thin films were spin-coated onto the as prepared films. After heating process at 400 ℃ for 2 h, another layer of the tungsten oxide or nickel oxide films were sputtered, and another layer of ITO thin film was finally sputtered as a conducting electrode. The electrochromic devices were noted as EC-N when lithium tantalite was spin-coated onto nickel oxide films and as EC-W when lithium tantalite was spin-coated onto tungsten oxide films. The transmittance changes were measured by UV-vis-IR spectrometer.

The XRD patterns show that powders dried and heated from the sol are a polycrystalline lithium tantalite, and thin films are amorphous. The analysis of electrochemical impedance spectroscopy indicates that the sol-gel lithium tantalite thin film has an ionic conductivity of 7.44×10^-7 S/cm at room temperature, which is comparable to that of sputtered film. As for the devices, the XRD patterns show that nickel oxide thin film layers are both polycrystalline in EC-N and EC-W, while tungsten oxides is polycrystalline in EC-W and amorphous in EC-N. The XRD patterns and XPS spectra also show the existence of WO_3-x phase in polycrystalline tungsten oxide films.

For electrochromic properties of the two groups of devices, transmittance regulations are measured at a fixed wavelength of 550 nm. For both groups, the differences of transmittance between colored and bleached state increase with increasing voltage. The maximum of transmittance change for EC-N is 26% at 5 V, with transmittance of around 72% at bleached state and 46% at colored state. Its coloring time is 50 s and bleaching time is 56 s. The results of cyclic test show that the differences of colored and bleached states keep stable, no obvious degeneration occurs after 60 cycles. The transmittance changes for EC-W have a lower value when applied the same voltages as in EC-N, and the switching speed is much slower. Its transmittance change is 14% at 3 V, while the coloring and bleaching time are both larger than 2 min, exceeding the setting range. It can be concluded that EC-N has a better property for regulation of visible light, and crystallinity can slow the switching process.

The transmittance spectra of colored states in vis-NIR range show that EC-W has a significant modulating performance for NIR, which can be attributed to the existence of WO_3-x components. As reported, oxygen deficient components can generate a local surface plasmon resonance effect that can result in absorption in the spectral range of NIR.

Electrochromic devices fabricated by a process that lithium tantalite was spin-coated on nickel oxide had better electrochromic properties in a visible spectral range, with larger optical modulations and a higher switching speed less than 1 min. The devices with lithium tantalite spin-coated on the tungsten oxide films could have a slower coloring and bleaching rate, while showing a better near-infrared modulation effect. These results indicated a routine for the realization of electrochromic devices that could modulate light and heat simultaneously and independently.

The existing research on modeling glass properties has some limitations in critical modeling stages, thus hindering predictive accuracy and interpretability. Primarily, systematic data pre-processing is often neglected, i.e., most literature relies solely on basic data cleaning (e.g., removing outliers and duplicates), lacking thorough investigation into optimal missing value imputation methods and the crucial sequence of imputation versus feature standardization. Secondly, hyperparameter optimization strategies remain simplistic. As some advanced techniques like random search or Bayesian optimization are introduced, studies generally overlook a critical adaptation of tuning methods to specific algorithm characteristics and a lack comparative analysis of hyperparameter optimization approaches across different models. This potentially restricts the performance of hyperparameter-sensitive models like Support Vector Machines (SVM). To address these challenges, this study was to propose a comprehensive, optimized framework for glass property prediction.

This research established a fully optimized framework for glass property prediction. The methodology comprised four integrated stages, i.e., 1) Systematic Data Preprocessing: For dataset A, the framework rigorously evaluated the combinatorial effects of three missing value imputation strategies (i.e., Median Imputation, Polynomial Imputation, Random Forest Imputation) and two standardization sequences (i.e., Imputation followed by Standardization; Standardization followed by Imputation); 2) Predictive Model Construction and Evaluation: Six distinct machine learning algorithms, which were chosen for their varied inductive biases (i.e., Random Forest-RF, XGBoost-XGB, Classification and Regression Trees-CART, K-Nearest Neighbors-KNN, Support Vector Machines-SVM, Artificial Neural Networks-ANN), were employed. The model predictive efficacy was rigorously quantified using a triple evaluation metric (i.e., Mean Squared Error-MSE, Mean Absolute Error-MAE, Coefficient of Determination-R²). The model generalization was validated by a novel dataset (A); 3) Interpretable Analysis: The SHAP (SHapley Additive exPlanations) framework was applied to elucidate the contribution paths of key glass components towards the predicted properties; and 4) Hyperparameter Optimization Strategy Analysis: For representative models SVM and RF, a comparative experiment was conducted by three hyperparameter tuning methods, i.e., Random Search, Grid Search, and Bayesian Optimization. This stage was to establish adaptable criteria matching hyperparameter tuning strategies to model architectures.

The results show that the sequence of standardization and imputation significantly affects model performance metrics, with a substantially larger impact observed on MSE and MAE (up to 49.64% variation), compared to R². Conversely, the choice of imputation method exerts a greater influence on R² (up to 63.68%) under conditions of high missing data rates (i.e., >10%). The optimal preprocessing is standardization-first followed by Random Forest Imputation. The SHAP analysis reveals that preprocessing sequence does not alter the fundamental trends in feature contributions, but significantly changes the numerical distribution morphology of SHAP values, thus introducing systematic bias. Imputation method, particularly at high missing rates (i.e., >40%), directly affects the ranking of feature importance derived from SHAP.

The comparative performance of the six algorithms, evaluated across metrics, follows an order of RF ≈ XGB > KNN ≈ ANN >SVM≥ CART. The RF and XGB demonstrate a superior performance, leveraging their ensemble learning advantages. The predictive accuracy of model (as measured by MSE, MAE, and R²) is robust to varying missing data rates. However, the interpretability of model predictions via SHAP becomes less reliable at high missing rates.

The SHAP analysis indicates distinct governing components for different properties, sometimes diverging from conventional understanding. While B₂O₃ dominates the Glass Transition Temperature (T_g), M_g content is primarily governed by SiO₂, and followed by B₂O₃. Na₂O is a dominant contributor to both Density and Refractive Index. The primary contributors to the thermal expansion coefficient (TEC) are temperature-range dependent. Na₂O followed by K₂O dominates at 20-300 ℃, whereas SiO₂ followed by Na₂O dominates at 20-400 ℃.

The use of hyperparameter tuning method proves critical, especially for hyperparameter-sensitive models. The Bayesian optimization can obtain the most substantial improvements for the lower-accuracy SVM model, achieving a maximum R² improvement of 76.56%. In contrast, its impact on the high-performing ensemble algorithm RF is marginal, indicating that alternative, potentially more explorative methods like Genetic Algorithms (GA) can be more suitable for such models. This highlights a necessity for model-specific hyperparameter optimization strategies.

This study proposed and validated a comprehensive optimization framework for glass property prediction, addressing key limitations in data preprocessing and hyperparameter tuning prevalent in the existing literature. The systematic evaluation demonstrated that preprocessing choices, particularly the sequence of operations and imputation method under high missing data scenarios, had profound and quantifiable impacts on both predictive accuracy (MSE, MAE, R²) and the reliability of interpretability frameworks like SHAP. Among the evaluated algorithms, RF and XGB consistently outperformed others. Critically, the SHAP analysis provided insights into composition-property relationships, thus revealing temperature-dependent dominance for TEC and challenging assumptions regarding M_g's primary drivers. Finally, the hyperparameter optimization experiments established that tuning strategy efficacy could be highly model-dependent. The Bayesian optimization offered significant gains for sensitive models like SVM but was less effective for robust ensembles like RF, necessitating tailored approaches. This framework could provide a validated, end-to-end methodology for enhancing the accuracy, reliability, and interpretability of predictive models in glass science and potentially other material domains.

TCT via quantifying the types and numbers of atomic constraints within the glass network effectively reveals the intrinsic correlation between glass structure and macroscopic properties. It provides a solid theoretical foundation for understanding and tailoring glass performance, offering a significant potential for the development of high-performance glass materials. Under the guidance of this theoretical framework, MD simulation serves as a powerful tool for investigating the atomic-scale structure and dynamic behavior of glasses, thereby offering an effective pathway to establish structure-property relationships. However, TCT is often limited to specific systems, which can introduce errors when applied to complex compositions. Meanwhile, MD simulations are computationally expensive and sometimes suffer from the absence of accurate potential functions. Several limitations still hinder their broader application, i.e., a) insufficient temporal resolution. Femtosecond-level time steps are inadequate for resolving high-frequency transient polarization responses; b) force fields often simplify quantum effects-current models, and fail to accurately describe local charge fluctuations and dynamic polarizability; and c) Limited spatial scales. Nano-sized models cannot fully capture structural heterogeneity, and statistical convergence under high-frequency electric fields is constrained by available computational power. MD simulations remain inadequate for directly investigating glass performance under high-frequency applications. To overcome these challenges, multiscale coupling models are needed, such as integrating ML algorithms to enhance the accuracy of polarization dynamics through deep learning-based potential functions, and employing materials informatics to accelerate the screening of high-performance glass compositions. These strategies are expected to significantly improve the efficiency of rational glass design.

Glass-ceramics, which evolve from glasses, are widely used in applications such as encapsulation materials, printed circuit boards, microwave components, sealing glasses, and low-temperature co-fired ceramic (LTCC) substrates, having a considerable value in high-frequency communications, microelectronic packaging, and power devices. The existing research on the crystallization phenomena in glass-ceramics mainly follows two technical pathways, i.e., a) employing structural characterization methods in combination with diffusion kinetics simulations and experimental validation to indirectly infer crystal precipitation behavior, and b) constructing glass-ceramic models in MD systems by manipulation strategies such as "dig-insert" or "cut-combine" approach. However, these approaches remain inherently limited to either indirect representations of crystalline formation or manually constructed models. Overcoming the existing technological bottlenecks to enable real-time visualization of crystal nucleation and growth mechanisms during dynamic simulations remains a critical challenge.

Finally, in the context of advanced packaging and heterogeneous integration, glass substrates play a crucial role in 3D integration, but face multiple challenges in interfacial reaction dynamics with silicon/metal substrates. These include atomic-scale interdiffusion leading to dielectric degradation, cross-scale coupling between nanoscale chemical bond reconstruction and macroscopic stress evolution, as well as non-equilibrium thermodynamic effects induced by laser-assisted processing. There is an urgent need to develop simulation frameworks that integrate co-evolution of multiple properties across scales, enabling quantitative prediction of atomic interdiffusion coefficients, chemical bond reconstruction energy barriers, and residual stress distributions. Such efforts will provide the theoretical foundation for the design and process optimization of high-reliability glass substrates.

Optical waveguides in transparent materials based on the multi-scan laser direct-writing technique are widely employed in the fabrication of three-dimensional integrated devices. The multi-scan laser direct-writing technique enables a precise control over the geometric symmetry of the waveguide cross-sections through the spatial superposition effect of laser-induced refractive index changes. Also, it simultaneously allows for submicron spatial modulation of the refractive index distributions and suppression of transmission losses, thus providing a novel approach for the design of on-chip mode conversion devices. Although the multi-scan laser direct-writing technique has significant advancements in recent years, several core challenges still remain in its practical application and ongoing industrialization. These challenges include a) minimizing losses in submillimeter-scale curved structures, b) maintaining consistent direct writing effects across millimeter-scale depths, and c) enabling the fabrication of dynamically reconfigurable photonic devices. To address these issues, a future research should focus on the development of a next-generation femtosecond laser processing system with adaptive compensation and multi-parameter coordinated control. Such innovations will be critical in overcoming the engineering barriers currently restricting the production of fully glassed, three-dimensional, monolithic integrated photonic chips.

Although artificial intelligence has a great potential in the field of flat glass, there are still some technical challenges and application bottlenecks, such as data quality, process complexity, dependence on professional knowledge, and talent shortage. In the future, it is necessary for the enhanced practicality and reliability of artificial intelligence technology in the complete production chain of flat glass to further strengthen the application research of artificial intelligence technology in practical scenarios such as formula adjustment, performance prediction, production process control, defect detection, logistics and warehousing of flat glass, solve the problems encountered in the process of technology implementation, and improve the connectivity of data and adjustment solutions between various production links. Meanwhile, it is important for the increasing application of artificial intelligence in the field of flat glass to establish unified industry standards and specifications. In the future, artificial intelligence will develop in the field of flat glass towards technological breakthroughs and innovation, as well as the expansion and deepening of application scenarios, thus promoting the integration of technological innovation and industrial transformation in the flat glass industry.

The advancements in glass property calculations are shifting from data accumulation to high-efficiency prediction, with databases differing in scale, openness, algorithm integration, and applicability. Future developments will focus on data sharing, multi-method integration, and experimental-computational-machine learning synergies, providing an enhanced technical support for both scientific research and industrial applications of glass materials. Some methodologies can merge advances in materials science with cutting-edge computational tools. High-throughput preparation techniques promise a rapid discovery of new glass compositions via enabling swift synthesis and testing. Pairing these with advanced characterization methods like spectroscopy or diffraction can generate datasets for AI models, driving a further innovation.

Solid oxide electrolysis cells (SOECs) as a highly efficient energy conversion technology exhibit an immense potential in converting CO₂ into valuable chemicals during the electrolysis process, thereby providing an effective pathway to achieve carbon neutrality goals. However, the inadequate catalytic performance of conventional Ni-YSZ fuel electrode is a pivotal constraint hindering the advancement of SOEC technology. Lanthanum-strontium titanate (LST) perovskite oxides have a superior mixed ionic-electronic conductivity (MEIC) under cathodic polarization, making them a promising alternative fuel electrode for the SOECs. However, the performance of LST in CO₂ reduction reactions (CO₂RR) remains inadequate. In this work, Cu nanoparticles-decorated (La₄Sr₄)_0.9Ti_8-xCu_xO₂₆ (LSTCux, with x=0.4, 0.8, 1.2, and 1.6) layered perovskite oxides as a novel fuel electrode were synthesized by a conventional solid-phase reaction method.

For the synthesis of (La₄Sr₄)_0.9Ti_8-xCu_xO₂₆ (LSTCux, x = 0.4, 0.8, 1.2, 1.6), La₂O₃, SrCO₃, TiO₂, and CuO as raw materials were weighed, ground for 2 h, and calcinated at 1400 ℃ for 10 h. Subsequently, the samples were reduced in H₂ atmosphere at 800 ℃ for 10 h, yielding the reduced samples of LSTCu-rx. A solid electrolyte-supported structure was adopted with commercial SSZ electrolyte disks. To prevent reactions, the GDC slurry was spin-coated on the both sides of the SSZ disks and sintered at 1400 ℃ for 3 h to obtain a dense GDC functional layer. LSTCu-rx and LSCF powders were mixed with organic binders to prepare electrode slurries, which were then coated on the both sides of the electrolyte disk and sintered at 900 ℃ for 3 h, resulting in LSTCu-rx|GDC|SSZ|GDC|LSCF structured SOEC button cell. Silver paste and silver wires were applied on the both sides of the cell as current collectors.

The phase compositions of LSTCux and LSTCu-rx were characterized by X-ray diffraction (XRD). The morphology was determined by field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) with energy dispersive spectroscopy (EDS). The elemental valence states were determined by X-ray photoelectron spectroscopy (XPS). The electrochemical performance was analyzed by Solartron equipment. CO₂ electrolysis tests were performed on the SOEC button cells via the measurement of j-V curves and electrochemical impedance spectroscopy (EIS).

The results indicate that the electrode exhibits superior electrochemical activity and stability in CO₂ Reduction Reactions (CO₂RR). The results of electrochemical performance tests reveal that at 850 ℃ and 2.0 V, the SOECs utilizing LSTCu-r0.4, LSTCu-r0.8, LSTCu-r1.2, and LSTCu-r1.6 as electrodes have current densities of 1.07, 2.01, 1.65, and 1.52 A/cm², respectively. Among them, LSTCu-r0.8 has the maximum current density with polarization resistance (R_p) and Ohmic resistance (R_Ω) of 0.12 and 0.05 Ω·cm², respectively. These results indicate that LSTCu-r0.8 possesses superior electrocatalytic activity and efficient electron transport.

The LSTCu-rx series of fuel electrodes exhibited superior electrochemical performance and catalytic activity, particularly in the field of CO₂ electrolysis. Among them, LSTCu-r0.8 could be used as a promising material for CO₂ electrolysis fuel electrodes due to its efficient CO₂ adsorption and conversion capabilities, excellent electron transport properties, and stable electrochemical performance. In the future, the further optimization of the preparation process and exploration of a wider range of reaction conditions could be conducted to achieve a more efficient and stable CO₂ electrolysis process.

Solid oxide electrolysis cells (SOECs) are promising devices that use high-temperature electrolysis to convert H₂O and CO₂ into valuable fuels like H₂ and CO. Compared with conventional electrolysis, SOECs operate at elevated temperatures, which improves efficiency via utilizing industrial waste heat and renewable energy sources. However, CO₂ electrolysis faces challenges due to the chemical stability of CO₂, requiring a high energy input to break its bonds. NiO-YSZ based fuel electrode-supported SOECs are widely used due to their good electrochemical performance and mechanical strength at a high temperature. Nonetheless, some issues such as reduced electrode conductivity, chemical stability, and interface reaction activity during long-term operation hinder their performance and lifespan. It is thus important for the enhancement of gas transport and catalytic activity to optimize the microstructure and composition of the fuel electrode.

In this work, a dual modification strategy for microstructure optimization and impregnation modification was proposed to improve SOEC fuel electrode performance for CO₂ electrolysis. The electrode microstructure was optimized via introducing starch as a pore former during slurry preparation to adjust the porosity and improve gas diffusion. Half-cells consisting of NiO-YSZ support, NiO-YSZ functional layer, and YSZ electrolyte were fabricated by tape casting and sintered at 1350 ℃. In addition, Ce_0.8Gd_0.15Ni_0.05O₂ (CGN) was synthesized by a sol-gel method and impregnated into the porous fuel electrode. After impregnation, samples were reduced in hydrogen to produce Ni nanoparticles dispersed on the Ce-based matrix. The microstructure was characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD), while the oxygen vacancies and active sites were determined by X-ray photoelectron spectroscopy (XPS), electron paramagnetic resonance (EPR), and carbon dioxide temperature programmed desorption (CO₂-TPD). The electrochemical performance was evaluated by impedance spectroscopy and current-voltage measurements.

The introduction of starch effectively increases the electrode porosity, thus reducing mass transfer polarization resistance and enhancing gas transport. The results of electrochemical tests demonstrate that the optimized electrode has a current density of 0.64 A·cm^-2 at 750 ℃ and 1.6 V under CO₂ electrolysis atmosphere. The impregnation of CGN precursor leads to the uniform dispersion of Ni nanoparticles and increases oxygen vacancy concentration. These modifications significantly improves catalytic activity toward CO₂ reduction, and the electrolysis current density is increased by 58% and 64% at 1.1 V and 1.6 V, respectively. The impedance data indicate a marked decrease in polarization resistance. The long-term stability tests over 100 hours show a stable electrochemical performance, indicating that the combined structural and compositional modifications effectively enhance electrode durability and activity.

The dual modification approach integrating microstructural optimization and CGN impregnation significantly enhanced SOEC fuel electrode performance in CO₂ electrolysis. Increased porosity improved gas transport, while Ni catalyst modification boosted catalytic activity and oxygen ion transport. This strategy could provide a practical and cost-effective method to improve SOEC durability and efficiency without using expensive noble metals. The findings could offer a valuable guidance for the development of next-generation high-performance SOEC systems for carbon-neutral energy applications.

With the advancement of global energy transition, the development of efficient and clean energy conversion technologies becomes crucial to addressing energy shortages and environmental pollution. Solid oxide fuel cells (SOFCs) have attracted much attention due to their high energy conversion efficiency and fuel flexibility, while methanol as a liquid hydrogen carrier offers advantages such as safe storage/transportation and wide availability. However, a systematic research on the off-design performance and optimal design of methanol-SOFC systems remains insufficient, particularly for planar-tubular SOFC stacks through modeling and experimental validation. This study was to establish an accurate mathematical model combined with experimental verification to elucidate the power generation characteristics of methanol-SOFC systems under variable operating conditions, providing theoretical foundations and technical support for efficient and stable system operation.

This study proposed an equilibrium-based SOFC stack model based on the principle of minimizing Gibbs free energy, integrating heat and mass balance calculations, while comprehensively considering the effects of ohmic polarization, activation polarization, and concentration polarization on the voltage. An industrial-sized flat-tube SOFC stack (5 cells) was used to compare the power generation performance of pure hydrogen and methanol reforming gas at 750 ℃. The composition of the reforming gas was analyzed by gas chromatography, and the I-V curve was tested. A system flow model wss constructed using a software named Aspen Plus, and the I-V curves obtained from the SOFC system model simulation aligned closely with the experimental results, confirming the accuracy of the computational model used in this study. The sensitivity analysis was then performed to evaluate the effects of key parameters (i.e., the steam-to-carbon molar ratio (STCR), fuel utilization rate (U_f), and current density) on the system performance.

In the comparation of the performance of the SOFC system under pure hydrogen and methanol reforming gas power generation conditions, the maximum power density difference was only 5.59%, confirming the practicality of methanol fuel. Furthermore, the sensitivity analysis indicates that a) since anode carbon deposition is a key factor leading to stack failure, the impact of the steam-to-carbon molar ratio (STCR) on the system power generation performance is studied. The operating voltage decreases from 3.505 V to 3.226 V, and the system efficiency reduces from 45.95% to 42.35% when the STCR increases from 1 to 4 (a commonly used STCR in industrial processes). A low STCR (=2) can balance both the efficiency and carbon deposition suppression; b) the impact of fuel utilization rate on the system power generation performance is investigated. The system efficiency increases from 35.79% to 57.25% when the fuel utilization rate increases from 55% to 95%. However, the excessive concentration polarization can be avoided, and a fuel utilization rate of 85% is recommended; and c) after determining the system's fuel utilization rate and STCR, the impact of current density on the system power generation performance is studied. The calculated current density range is 0.602-0.733 A·cm^-2, with the operating voltage range of 3.25-3.50 V under the target power range conditions (i.e., P=147.3-166.9 W). The system efficiency ranges from 38.18% to 43.27% at the optimal current density of 0.704 A·cm^-2.

This study proposed a computational model for the methanol power generation characteristics of flat-tube SOFC stacks, enabling the performance prediction of the methanol-SOFC stack under different operating conditions. The accuracy of this model was validated through modeling and experimental results. The study further clarified the performance behavior of the methanol-SOFC system under varying conditions and proposed optimization ranges for key parameters. The results indicated that methanol fuel could effectively replace pure hydrogen, and the stack output power could be controlled under high voltage conditions. The efficient and stable operation could be achieved via adjusting the steam-to-carbon molar ratio, fuel utilization rate and current density. At an output power of 150 W, the optimal operating parameters were a steam-to-carbon molar ratio of 2, a fuel utilization rate of 85%, and a current density of 0.704 A·cm^-2. Under the optimum conditions, the stack efficiency was 36.71%, and the system efficiency was 42.21%. The research findings could provide an important reference for the engineering application of methanol-SOFC systems and have significant implications for advancing clean energy technologies.

Methane as a significant energy and chemical raw material boasts higher energy density, lower pollution, and abundant reserves, compared to coal or petroleum. The reforming of methane to syngas is one of the key technologies for its clean and efficient utilization. The produced syngas can be applied in hydrogen production, chemical synthesis, combustion, and as fuel gas for solid oxide fuel cells (SOFC). The chemical energy in the fuel gas can be directly converted into electrical energy when produced syngas used as SOFC fuel gas, serving as an important pathway for the clean and efficient utilization of carbon-based fuels. The dry reforming of methane (DRM) is a promising technology that converts methane and carbon dioxide into syngas (i.e., CO and H₂), offering notable environmental and economic benefits. However, the DRM reaction is confronted with some challenges like high CO₂ costs and propensity for carbon deposition. This paper was to propose a technological approach based on a mixed-conductive CO₂ permeable membrane reactor (i.e., MOCC membrane) to realize the efficient conversion of CH₄ and the capture and resourceful utilization of CO₂. The membrane reactor used a dual-phase membrane composed of samarium-doped ceria (SDC) and lithium-sodium eutectic carbonate (MC) when loaded with the LaNi_0.8Cu_0.2O_3-δ (LNC) catalyst, significantly enhancing the CO₂ permeation flux and the catalytic performance of the DRM reaction.

A dual-phase MOCC membrane composed of samarium-doped ceria (SDC) and lithium-sodium binary eutectic carbonate (Li_0.52Na_0.48)₂CO₃ was fabricated. The SDC porous matrix was synthesized via coprecipitation and subsequent impregnation with molten carbonate to form a dense structure. A Cu-doped LaNiO₃ perovskite catalyst (LaNi_0.8Cu_0.2O_3-δ) was prepared by a coprecipitation method and loaded onto the membrane reactor. The membrane's CO₂ permeation flux was evaluated under simulated flue gas and SOFC anode exhaust conditions. The catalytic performance, including CH₄ conversion, H₂/CO production rates, and stability, was systematically tested at 650-850 ℃. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and gas chromatography, respectively.

The catalytic performance of LaNi_1-xCu_xO_3-δ (LNC) catalysts with varying Cu doping levels (x = 0, 0.2, 0.4, 0.6) is systematically evaluated in a fixed-bed reactor for methane dry reforming (DRM). At 850℃, LaNi_0.8Cu_0.2O_3-δ catalyst exhibits the maximum CH₄ conversion rate (i.e., 99.8%) and CO₂ conversion rate (i.e., 98.4%), along with optimal H₂ and CO production rates of 1.36 and 1.50 mL·min^-1, respectively. In contrast, undoped LaNiO₃ catalyst shows inferior activity (i.e., CH₄ conversion: ~85%), highlighting the critical role of Cu doping in enhancing catalytic performance. The improved activity is attributed to Cu incorporation promoting oxygen vacancy formation and lattice oxygen mobility, suppressing carbon deposition and enhanced reaction kinetics. Note that the excessive Cu doping (x ≥ 0.4) leads to reduced activity due to the partial blockage of active sites and structural instability, underscoring the importance of balanced Cu substitution.

The dual-phase mixed oxygen and carbonate ionic conductor (MOCC) membrane, composed of SDC and lithium-sodium eutectic carbonate (Li_0.52Na_0.48)₂CO₃, has robust CO₂ permeation capabilities. Under non-reactive conditions, the CO₂ flux increases from 0.27 to 0.90 mL·cm^-2·min^-1 as the temperature increases from 650 to 850 ℃, with an activation energy of 51.93 kJ·mol^-1, indicating a thermally driven permeation mechanism. When coupled with DRM, the CO₂ flux surges to 1.52 mL·cm^-2·min^-1 at 850 ℃, driven by the enhanced CO₂ partial pressure gradient resulting from continuous CO₂ consumption in the reaction. This synergy between CO₂ capture and conversion significantly outperformes conventional fixed-bed reactors, where CO₂ utilization is limited by equilibrium constraints.

In the integrated membrane reactor loaded with LaNi_0.8Cu_0.2O_3-δ, the exceptional DRM performance is achieved under simulated flue gas (i.e., 60% N₂-40% CO₂) and SOFC anode exhaust (i.e., 40% CO₂, 2% H₂, 2% CO) conditions. At 850 ℃, CH₄ conversions reach 97.31% and 95.51%, respectively, with CO and H₂ production rates of 2.39 and 1.44 mL·cm^-2·min^-1 (flue gas) and 2.93 and 1.38 mL·cm^-2·min^-1 (SOFC exhaust). The H₂/CO ratio (i.e., ~0.6-0.7) deviates slightly from the stoichiometric value of 1, likely due to the reverse water-gas shift (RWGS) reaction at elevated temperatures. The results of long-term stability tests at 825 ℃ CH₄ conversion (i.e., ~97%) or syngas production rates, indicating the system's robustness. The analysis of SEM images and XRD patterns indicate the absence of carbon deposits and preserved catalyst phase structure, thus validating the anti-coking properties of the Cu-doped perovskite and the membrane's thermal stability.

Compared to the existing studies, this work develops a membrane reactor technology via achieving higher CO₂ permeation fluxes and catalytic efficiencies. LaNi_0.8Cu_0.2O_3-δ catalyst has a higher activity with a prolonged stability. Furthermore, the dual-phase membrane's unique ion-conduction mechanism circumventes the trade-off between selectivity and permeability in conventional membranes, offering a scalable solution for industrial CO₂ capture and utilization. These results indicate a potential of integrating advanced perovskite catalysts with MOCC membranes to achieve efficient, stable, and economically viable DRM systems.

This work presented a breakthrough in coupling CO₂ capture with DRM using a MOCC membrane reactor. When loaded with LaNi_0.8Cu_0.2O_3-δ catalyst, the membrane reactor could enhance the CO₂ permeation flux and the catalytic performance of the DRM reaction. At 850℃, employing the LaNi_0.8Cu_0.2O_3-δ catalyst, methane conversion rates achieved were 97.31% and 95.51% for simulated flue gas and SOFC anode off-gas, respectively. Correspondingly, CO₂ conversion rates were 98.34% and 78.49%, while the production rates of CO were 2.39 and 2.93 mL·cm^-2·min^-1, and those of hydrogen were 1.44 and 1.38 mL·cm^-2·min^-1, respectively. The results of long-term stability tests indicated that the membrane reactor had a stable performance for over 60 h, demonstrating promising application prospects. The technology could reduce CO₂ emissions and convert waste gases into valuable syngas, aligning with global carbon-neutral goals. A future research could focus on scaling up the reactor and optimizing operational parameters for broader implementation.

In view of the problems of easy oxidation and carbon deposition of conventional electrode materials in the actual working environment of SOEC, as well as the lack of understanding of the structure-activity relationship between oxygen vacancies and electrode performance of the existing materials, a dual regulation strategy of simultaneous doping of fluoride ion (F^-) and introducing A-site defects in Sr₂FeCo_0.7Mo_0.3O_6-δ oxide was investigated. The crystal structure, electronic structure, chemical stability and other aspects of electrode materials were optimized to overcome the limitations of traditional materials and develop high-performance SOEC cathode materials. The influence mechanism of the dual regulation strategy on the key factors (i.e., oxygen vacancy concentration, ion transport capacity and surface chemical properties) of the material was explored. It was expected that the adsorption and activation ability of the material to CO₂ could be enhanced, the electrode overpotential could be reduced, and the electrode reaction rate could be improved via increasing the oxygen vacancy concentration, enhancing the oxygen ion transport capacity and optimizing the surface chemical state, thereby significantly improving the catalytic activity and electrolysis efficiency of CO₂RR, realizing the efficient conversion and utilization of carbon dioxide, and providing technical support for alleviating greenhouse gas emissions and energy crisis.

A series of Sr₂FeCo_0.7Mo_0.3O_6-δ based electrode materials and related powders were prepared by an improved EDTA-citric acid composite sol-gel method. In the preparation process, the raw material ratio and reaction conditions were accurately controlled. For the preparation of A-site defect samples by adjusting the stoichiometric ratio of strontium source, and for the preparation of fluorine-containing samples by replacing part of Sr (NO₃) ₂ with SrF₂ as fluorine source and strontium source, all the samples were calcined under the same conditions to ensure the comparability of experimental results. Also, the LSGM electrolyte sheet was prepared by a tape casting method, and the LDC buffer layer was prepared by a sol-gel method. The symmetrical battery was assembled by a screen printing process. The printing thickness and sintering temperature of each functional layer were strictly controlled to ensure the structural integrity and interface bonding quality of the battery.

The prepared materials are analyzed by advanced characterization techniques and experimental methods. The analysis of X-ray diffraction patterns clearly shows the crystal structure change of the material. Fluorine doping causes a lattice expansion, while the introduction of A-site defects causes a lattice contraction, and effectively inhibits the precipitation of SrF₂ secondary phase. The analysis of X-ray photoelectron spectroscopy further reveals a significant change in the chemical state of the material surface. The fluorine element is effectively incorporated into the lattice, and the lattice Sr ratio is greatly increased, which effectively reduces the surface segregation of Sr and enhanced the stability of the material. Also, the decrease of the valence state of the B-site transition metal increases the number of d-orbital electrons, thus improving the electrocatalytic activity of the material. The increase of the relative content of adsorbed oxygen also indicates that the concentration of oxygen vacancies increases, which creates favorable conditions for ion transport and CO₂ adsorption activation. The analysis of CO₂ adsorption and thermal stability, temperature-programmed desorption and thermogravimetric experiments shows that the synergistic effect of fluorine doping and A-site defects greatly improves the desorption temperature, desorption amount and anti-carbonation ability of the material for CO₂. The desorption amount of SFCMAF sample is 2.25 times greater than that of SFCM sample. The electron paramagnetic resonance characterization also confirms that SFCMAF has the maximum oxygen vacancy concentration, providing a great support for efficient CO₂ electrolysis reaction.

The results of electrochemical performance test indicate that the current density of SFCMAF material is 66.7% higher than that of the original material, and the performance of each sample improves and the amplitude is similar for CO₂/H₂O co-electrolysis under the working conditions of 850 ℃ and 2 V. The analysis of electrochemical impedance spectroscopy and distribution relaxation time shows that the polarization resistance of SFCMAF reduces and the surface catalytic activity enhances. The SFCMAF electrode has a superior stability in both short-term and long-term stability tests, with only 7% decay in 120 h long-term test. The calculations based on the density functional theory indicate the essential reasons for the performance improvement in micron scale. SFCMAF has a stronger ability to adsorb and activate CO₂. Fluorine doping and A-site defects synergistically optimize the electronic structure, reduce the oxygen vacancy formation energy, and change the electronic structure of the active site, which is highly consistent with the experimental results, and provides a solid theoretical guidance for further optimizing the material properties.

This study developed a high-performance SOEC cathode material via introducing fluoride ion doping and A-site defects in Sr₂FeCo_0.7Mo_0.3O_6-δ matrix. The modified material maintained a basic double perovskite structure with significant changes in lattice parameters. The dual-regulation strategy improved the surface chemical properties, CO₂ adsorption, and electrochemical performance. The oxygen vacancy concentration increased, and the polarization resistance decreased. The theoretical calculations revealed the mechanism of performance improvement. This strategy could provide some ideas for designing efficient SOEC cathode materials and validate the effectiveness of anion doping combined with cation defect regulation.

Solid oxide fuel cells (SOFCs) are considered as a leading candidate for clean energy conversion due to their high efficiency and flexibility. Yttria-stabilized zirconia (YSZ) serves as an electrolyte material in SOFCs due to its high ionic conductivity and mechanical stability at a high operating temperature. However, the long-term stability of YSZ can be impaired via the cubic-tetragonal (c-t) phase transformation. This transformation leads to a decrease in ionic conductivity and mechanical integrity, which contributes to an overall degradation of SOFC performance. Despite its importance, most of the previous investigations primarily focused on electrochemical degradation mechanisms, while less attention was paid to the material stability of YSZ, especially at the Ni-YSZ interface. For instance, the atomic diffusion of Ni into YSZ during sintering can accelerate the phase transformation, leading to a significant decrease in ionic conductivity within 100 hours. Despite these findings, there is a lack of comprehensive quantitative analysis on the kinetics and morphological impacts of the c-t phase transformation at the Ni-YSZ interface. Moreover, the existing models often oversimplify an interplay between phase transformation and mechanical stress, restricting their ability to predict a long-term degradation behavior accurately. In this work, the phase field method was applied to simulate the c-t phase transformation in YSZ at the Ni-YSZ interface for elucidating its effect on the interface morphology and gaining insights into the SOFC degradation mechanisms.

A 3D microelastic phase field model was proposed to investigate the c-t phase transformation in YSZ. The evolution of both conserved and nonconserved order parameters were governed by the Allen-Cahn and Cahn-Hilliard equations, respectively. The total free energy was constructed under multiphysics coupling. The elastic constants for cubic YSZ and tetragonal YSZ were defined based on their respective crystal structures, with the values assigned to reflect their distinct mechanical properties. The misfit strain was determined using the lattice parameters for the cubic and tetragonal phases. The simulations were performed on a 64×64×64 voxel grid representing a single-crystal YSZ. The governing equations could be solved by the Fourier spectral method and a semi-implicit time-stepping scheme. The shape of the nucleus distribution was restricted to the YSZ surface to ensure a realism under SOFC operating conditions.

In this work, a dense YSZ disk with a diameter of 24 mm and a thickness of 0.5 mm was served as a substrate. After mechanical polishing and ultrasonic cleaning in acetone and ethanol, a stainless-steel shadow mask was employed to perform two perpendicular magnetron-sputtering depositions, producing a continuous Ni patterned film anode with a thickness of approximately 1 μm. For the cathode, a Pt paste was screen-printed onto the opposite side of the YSZ substrate. The discharge tests of the SOFC were carried out in a custom-designed alumina reaction chamber. Humidified 97% H₂ was supplied to the anode and pure O₂ to the cathode, both at a fixed flow rate of 50 mL_·min^-1. The cell was polarized at 0.7 V for 100 h. After testing, the Ni film was mechanically peeled off, regions where Ni detached were selected and the underlying YSZ surface morphology was examined by scanning electron microscopy (SEM).

The phase field simulations reveal that tetragonal variants preferentially nucleate at near the Ni-YSZ interface. High stresses appear at around the Ni-YSZ interfaces with the development of the t-variants. At certain iterations, high local stresses of exceeding 2 GPa can occur, leading to a radial growth of the variants into the bulk. A significant morphological evolution occurs, forming ridge structures along the YSZ surface and submicron pores in the interior at near the Ni-YSZ interface. The agreement between simulation data and experimental results confirms that the c-t phase transformation causes these morphological evolutions. The formation of pores is particularly detrimental as it disrupts the ion conduction pathways and weakens mechanical stability of YSZ, which can be correlated to the degradation of the SOFC performance.

In this work, a phase field modeling was used to simulate the c-t phase transformation in YSZ at the Ni-YSZ interface in SOFC anodes. The simulations accurately reproduced the morphological evolutions during long-term operation of SOFC, i.e., the formation of surface ridges and internal submicron pores, providing important insights into the SOFC degradation mechanisms. The preferential nucleation of tetragonal variants at the surface and stress-driven void formation could highlight some key factors affecting interface stability. Although the simulations were conducted based on a single crystal, this work could lay a foundation for future research on polycrystalline and porous YSZ substrates. This work could indicate strategies for improving SOFC durability, such as optimizing material compositions or designing stress-relieving structures via identifying stress generation as a primary driver of degradation. The findings of this work could contribute to advancing SOFC technology towards sustainable and efficient energy solutions.

Solid oxide fuel cells (SOFCs) can directly convert chemical energy into electrical energy with high efficiency and environmental friendliness. In the operation of SOFCs (at 600-800 ℃), hydrogen and air are supplied to the fuel and oxygen electrodes, respectively. Gas-tight sealing components are critical for isolating these electrodes to prevent gas crossover and efficiency loss, ensuring safe operation. Ceramic cell-metal interconnect sealing represents a bottleneck technology for SOFCs. Glass is widely used as a sealing material due to its scalability, operational simplicity, and cost-effectiveness. However, monolithic glasses exhibit a narrow functional temperature range and a poor thermal gradient resistance, often developing cracks due to the uneven thermal stress distribution. Glass-matrix composite seals offer enhanced thermal stability and hermeticity. Also, stack operation faces some challenges during startup/shutdown cycles. While mica-based seals demonstrate a superior thermal cycling resistance, their interlaminar bonding strength degrades a post-annealing, leading to delamination and compromised gas tightness. Vermiculite as a another layered silicate clay mineral with a structure similar to mica exhibits a better airtightness rather than mica in high-temperature sealing processes. This is because vermiculite contains an interlayer water, which turns into expanded vermiculite after high-temperature heating. The flexural interlayer structure blocks gas leakage channels, making the layers of vermiculite have greater constraint and bonding properties, thus having a potential for high-temperature sealing.

A homogeneous mixture of SiO₂, Na₂CO₃, K₂CO₃, KNO₃, BaCO₃, and minor TiO₂ was loaded into an alumina crucible, calcined at 1450 ℃ for 2 h at 5 ℃·min^-1, melted at a high temperature, and quenched in deionized water. The quenched glass block was washed, crushed, ground, dried, and sieved with a 200 mesh sieve to obtain a glass powder (labeled G0). A natural vermiculite powder heat-treated at 900 ℃ for 20 h was blended with G0 at 5%, 10%, and 15% to prepare vermiculite-glass composite sealing materials (i.e., designated as GV5, GV10, and GV15), respectively. Sealing material powder and organic binder (prepared by uniformly mixing ethyl cellulose and terpineol in a mass ratio of 4∶96) were homogenized in a mass ratio of 2.5∶1.0 to form a composite seal paste. This paste was applied between polished SUS430 stainless steel plates to prepare the specimens for high-temperature interfacial compatibility studies, and was also used in a custom-built high-temperature hermeticity testing apparatus.

The XRD patterns of the sealing material show that the crystallization degree of the composite sealing material increases with increasing vermiculite content in sample G0. The overall phase structure changes of the composite sealing material are affected due to the glass structure changes, and vermiculite as an additive exhibits stable high-temperature thermal properties.

The results of a further open circuit voltage (OCV) test at 750 ℃ show significant OCV fluctuations in sample GV5 possibly due to its low softening temperature (T_s), causing excessive softening of the glass matrix at 750 ℃ and forming gas leakage channels. However, the self-healing property of glass heals microcracks and other leakage channels during softening, leading to OCV fluctuations in sample GV5. Sample G0 has a T_s of 25 ℃, which is higher than sample GV5, showing little OCV fluctuations at 750 ℃ with a good stability. The OCVs of samples GV10 and GV15 remain stable for 900 min, and sample GV10 stabilizes at 1.1 V and sample GV15 does at 1.06 V. The lower OCV of sample GV15 is presumably due to the excessive amount of vermiculite, where the glass matrix cannot fully wet and encapsulate vermiculite particles after high-temperature softening, creating potential leakage channels and affecting gas tightness, while sample GV10 exhibits an excellent gas tightness. The results of further gas tightness tests show that for sample GV10 at an inlet pressure of 7kPa, gas leakage rates at 700, 750 and 800 ℃ are 0.0019, 0.0031 and 0.0073 sccm·cm^-1, respectively.

Increasing vermiculite content from 0 to 15% increases porosity and pore coarsening in the sealing material, possibly because vermiculite particles reabsorb water during green body preparation. At high temperatures, some water vapor enters the lamellar structure of vermiculite, and vermiculite particle expansion under a water vapor pressure causes pore coarsening and increases porosity. Based on the analysis of energy dispersive spectroscopy (EDS), vermiculite particles embed in glass blocked diffusion of Cr, Fe and other elements from the interconnect into the sealing material. More importantly, lamellar vermiculite with a reversible interlayer expansion potentially adjusts a thermal stress distribution in glass, bridges crack, slows crack propagation and growth, making it a promising sealing material for SOFCs.

Three vermiculite-glass composite sealing materials were developed via incorporating 5%, 10%, and 15% vermiculite powder into a K-Na-Ca-Ba-Si silicate glass matrix and applied to SOFCs. These composites demonstrated excellent gas-tightness and chromium resistance. Among them, the composite with sample GV10 was tested in a button cell configuration, having a stable OCV of 1.1 V at 750 ℃. The results of gas leakage tests confirmed its superior sealing performance, with a leakage rate of 0.0031 sccm·cm^-1 under an input gas pressure of 7 kPa. The gas tightness improved at lower operating temperatures. At 700 ℃ under the same pressure, the leakage rate decreased to 0.0019 sccm·cm^-1, which was attributed to a lower T_s (538 ℃) of sample GV10. Reducing the operating temperature effectively mitigated excessive glass softening, thereby enhancing a sealing reliability. The further microstructural characterization by scanning electron microscopy and EDS revealed that the lamellar vermiculite particles embedded in the glass matrix significantly suppressed a chromium diffusion into the sealing material. This unique microstructure also contributed to reduced gas leakage rates and improved fracture toughness. Therefore, vermiculite-glass composite sealing materials could have a promising potential used as high-performance sealing candidates for SOFCs.

Solid oxide fuel cell (SOFC) stacks depend on metallic interconnects to ensure the performance with cathode-side protection and interfacial resistance as key determinants. The existing coatings like perovskite, active element oxides and spinel all have drawbacks. For instance, perovskite coatings fail to inhibit oxygen ion diffusion, while active element oxides are typically thin and porous. In contrast, spinel coatings have a promise due to their lower densification temperature and ability to suppress chromium volatility. However, there is little work on the contact resistance. In this work, a Mn-Co spinel coating was applied to SUS441 stainless steel to reduce both oxidation and interfacial resistance. This approach could offer a viable solution for optimizing the performance of SOFC stacks.

SUS441 was chosen as an interconnect substrate material, and all the samples were cut to small pieces with the dimensions of 10.0 mm × 10.0 mm × 1.5 mm. Manganese nitrate and cobalt nitrate (AR, Sinopharm Group Co., Ltd., China) were dispersed in ethanol solvent, with dispersant, binder, and defoamer added to prepare a precursor solution. A wet-spray device was used to deposit Mn_1.5Co_1.5O₄ coating on the SUS441 substrate (coated SUS441), which was then dried in air and calcined at 850 ℃ for 10 h.

The area-specific resistance (ASR) of the samples was measured by a four-terminal method with a CHI760e electrochemical workstation (Shanghai Chenhua Instrument Co., Ltd., China), with a voltage range of -1 to 1 V. The interfacial contact resistance was calculated based on the ASR values.

The thermal expansion behavior was characterized by a model DIL402C dilatometer (Netzsch Co., Germany) at a heating rate of 5 ℃/min. The microstructures were determined by a model S4800 scanning electron microscope (Hitachi Co., Japan). The oxidation mass gain was monitored by a model FA2104 electronic balance (Shanghai Shunyu Hengping Scientific Instruments Co., Ltd., China). A three-dimensional surface profile was obtained by a model LSM700 laser confocal microscope (Zeiss Co., Germany).

The coated SUS441 exhibits an uneven surface but a dense cross-section free of cracks or bubbles, indicating a robust adhesion between the coating and substrate. The Mn-Co spinel coating substantially reduces the interconnect's resistance of 0.47 mΩ·cm², compared to 3.29 mΩ·cm² for uncoated SUS441. Also, the coating enhances the activation energy (E_a) for electrical conductivity from 0.59 eV to 0.91 eV, thereby slowing electron migration and reducing the oxidation rate.

The results of long-term testing at 750 ℃ show that the ASR of uncoated SUS441 increases from 26.07 mΩ·cm² to 52.41 mΩ·cm² after 720 h, then gradually decreases and stabilizes at 25.54 mΩ·cm². This may result from the maturation and reconstruction of the oxide layer, which lowers the resistance. In contrast, the coated SUS441 has an initial ASR of 3.96 mΩ·cm², which increases to 16.38 mΩ·cm² after 300 h and then slowly declines to 11.97 mΩ·cm² at 820 h. These findings demonstrate that the Mn-Co coating significantly enhances the high-temperature oxidation resistance of SUS441.

The oxidation mass gain is a key indicator of interconnect oxidation resistance. The coated and uncoated SUS441 interconnects both exhibit a rapid mass gain in the first 200 h. After 200 h, the mass gain rate of the coated SUS441 slows and stabilizes, whereas the uncoated SUS441 further increases. The dense Mn-Co coating effectively blocks oxygen penetration and metal ion migration, thereby slowing oxidation and enhancing resistance. The relationship between the square of oxidation mass gain and time indicates a change in oxidation behavior after approximately 200 h, likely due to the thickening of the oxide layer hindering further oxidation. As the oxide layer thickens, the internal mass transfer becomes more difficult, slowing the oxidation rate. The K_p value decreases by one order of magnitude during the slow oxidation phase, compared to the initial rapid stage.

The coating has a limited impact on the thermal expansion of the interconnect but brings the expansion coefficient of SUS441 closer to that of the La_0.6Sr_0.4Co_0.2Fe_0.8O₃ (LSCF) cathode, thus improving the thermal compatibility. The initial interfacial contact resistance between SUS441 and LSCF is 3.54 mΩ·cm², stabilizing at 14.57 mΩ·cm². For coated SUS441 and LSCF, the initial interfacial contact resistance decreases from 20.78 mΩ·cm² to 7.99 mΩ·cm². The Mn-Co coating likely improves the interface microstructure, reducing defects and roughness, and enhancing electron transfer. The analysis of laser confocal microscopy indicates that the roughness matching value between SUS441 and LSCF is 0.605, and it is 1.156 for coated SUS441, indicating better surface compatibility and contact with LSCF.

The Mn-Co spinel coating was coated on the surface of SUS441 interconnect, and its effect on suppressing the interconnect's oxidation behavior and reducing interfacial resistance were investigated. The results showed that the coating significantly reduced the ASR of SUS441 from 3.29 mΩ·cm² to 0.47 mΩ·cm² at 800 ℃, maintaining a relatively low ASR of 11.97 mΩ·cm² after 800 h oxidation. The results of oxidation mass gain tests indicated that the coating effectively reduced the oxidation rate of coated SUS441, which was only 43% of that of the uncoated sample. For the interfacial properties, the contact resistance between the Mn-Co coated SUS441 and the LSCF cathode was 7.99 mΩ·cm², which was just 55% of that of the uncoated sample, with improved interfacial roughness matching. These findings could demonstrate that the Mn-Co spinel coating effectively mitigated the oxidation rate and interfacial resistance of SUS441, thereby enhancing its high-temperature oxidation resistance.

Compared to Co-based cathode materials, Fe-based perovskite materials exhibit superior thermal stability and electrochemical stability, but they have lower conductivity and redox activity. The Fe-based perovskite materials are regarded as one of the cathode materials with a great development potential for SOFCs due to their lower thermal expansion coefficient and cost. In the LnBaFe₂O_5+δ (Ln = lanthanide elements, Y) double perovskite materials, PrBaFe₂O_5+δ (PBF) has the lowest activation energy for the redox reaction, making it a promising alternative material for SOFC cathodes. For the PBF materials, there are a few reports on their crystal structure and redox reaction mechanisms. There is still a debate regarding the tetragonal and cubic structures of PBF materials. Also, the mechanism of the redox reaction remains unclear and requires a further in-depth investigation. This study was to prepare the PBF cathode materials by a sol-gel method. In addition, the synthesis process, crystal structure, and redox reaction mechanisms of PBF materials were also investigated.

The average binding energy of the possible structures of PBF was calculated by the Cambridge Sequential Total Energy Package (CASTEP) module in Material Studio software. The reflex module in Materials Studio was used to simulate the X-ray diffraction (XRD) patterns after optimizing the structures. The PBF powder was prepared by a sol-gel method, and the precursors were analyzed by thermgravity-differential thermal analysis (TG-DTA), X-ray diffraction (XRD) and transmission electron microscopy(TEM). The calcined samples were treated in air, 20% (in volume) H₂(H₂+N₂) atmosphere, and pure H₂ at 800 ℃ for 20 h. In addition, the electrochemical impedance spectra of the PBF|SDC|PBF symmetrical cell were measured, and analyzed via distribution of relaxation times (DRT).

The simulated XRD patterns show distinct differences in the XRD peaks of the four PBF structures with the main difference between the tetragonal and simple cubic structures after 80°. The characteristic double peak of the double perovskite is attributed to an increased lattice distortion caused by an increase in oxygen vacancy defects, while its structural symmetry remains unchanged. The tetragonal structure has the lowest average binding energy, while the simple cubic structure has the highest. This indicates that the likelihood of PBF adopting a simple cubic structure is minimal, whereas the likelihood of it adopting a tetragonal structure is maximal.

The results of TG-DTA and XRD indicate that the precursors form BaFeO₃ and PrFeO₃ heat-treated at 800 ℃ and a subsequent two-phase solid solution process after 800 ℃. Pure PBF is obtained after roasting in air at 1200 ℃ for 5 h. In a 20%H₂(H₂+N₂) atmosphere, pure PBF can be achieved at 800 ℃, indicating that the 20%H₂(H₂+N₂) treatment accelerates the solid solution of BaFeO₃ and PrFeO₃. The PBF is structurally stable in both air and a 20%H₂(H₂+N₂) atmosphere, but decomposes into Fe, BaFeO₃, and Pr₂O₃ in pure H₂, indicating that PBF material decomposes under pure hydrogen and is not suitable for use on the anode side. Also, the occupancy of O in the [Pr-O] layer decreases, indicating that more oxygen vacancies are generated after reduction. The analysis of the crystal structure shows that the unit cell is elongated in a 20%H₂(H₂+N₂) atmosphere due to the formation of more oxygen vacancies in the [Pr-O] layer.

Based on the different relaxation times of various cathodic reactions, the DRT analysis indicates that the limiting step of the ORR at 400−550 ℃ involves the ionization of oxygen atoms and the transport of oxygen ions, while that at 600−700 ℃ involves the chemisorption of oxygen and the dissociation of oxygen molecules. At 700−800 ℃, the limiting step is related to the physisorption of oxygen. Clearly, the conclusions drawn from time-domain analysis differ from those obtained through conventional impedance spectroscopy. At 750−800 ℃, time-domain analysis indicates that the limiting step affecting the ORR is the physisorption of oxygen, whereas conventional impedance spectroscopy indicates that it is the ionization of oxygen atoms and the transport of oxygen ions. This study posits that both sets of results can serve as a basis for optimizing the oxygen catalytic performance of PBF materials.

This study investigated the formation process, structure, stability, and oxygen catalytic performance of PBF materials by DFT calculations, XRD, TEM, EIS, and DRT analysis. The PBF synthesized through the solid solution formation of PrFeO₃ and BaFeO₃ at 800 ℃ exhibited a tetragonal structure (i.e., space group: P4/mmm). The crystal structure remained stable in air and 20%H₂(H₂+N₂) atmosphere, but partially decomposed into Fe, BaFeO₃, and Pr₂O₃ in pure hydrogen. The limiting steps of the oxygen reduction reaction (ORR) were identified as the ionization of oxygen atoms and the transport of oxygen ions at 400−550 ℃, chemisorption of oxygen and dissociation of oxygen molecules at 600−700 ℃, and physisorption of oxygen at 700−800 ℃.

Cathode contact components play a pivotal role in determining the efficiency, reliability, and longevity of SOFC stacks. The future development of cathode contact components may focus on several aspects, the first of which is the development of new materials. Innovative cathode materials can be used for the cathode itself and served as candidate materials for the cathode contact layer. It is important to optimize the preparation process. Lab-scale studies can explore various novel or established preparation techniques. However, in industrial production, the choice of preparation processes is constrained by sample stability and cost. Therefore, optimizing the preparation process to compensate for some inherent shortcomings of the materials is a direction worthy of research. Furthermore, the study of contact components will become more complex when considering the actual operating conditions within the stack, such as flow field distribution, temperature distribution, and current distribution. In terms of structural design, concepts such as gradient configuration and asymmetric structures can be applied to achieve the overall performance and structural stability.

In summary, some challenges remain in achieving large-scale commercialization while significant progress is made in cathode contact materials and components. Collaborative efforts integrating material science, engineering, and computational modeling will be a key to advancing SOFCs technology toward widespread adoption in clean energy systems.

Conducting in-depth research and taking measures to mitigate Cr poisoning on the cathode materials is crucial for further advancing the commercialization of SOFCs technology. In this review, recent development in enhancing the Cr tolerance of cathodes is represented. The theoretical mechanisms of Cr deposition on cathodes are introduced, and the reported strategies to alleviate Cr poisoning on cathodes are summarized. Despite the significant progress made in the existing research, there are still some challenges to be addressed, i.e., a) it seems that a single theory cannot perfectly explain the Cr deposition on different cathode materials. For different materials under different test conditions, these mechanisms may occur simultaneously or compete with each other. In addition, in proton-conducting SOFCs (H-SOFCs), these processes may be more complicated due to the introduction of protons. Although some researchers deal with the problem of Cr poisoning in H-SOFCs, there are no detailed literature reports on the mechanism of Cr poisoning in H-SOFCs system; b) Redesigning or improving the currently commercialized interconnect materials faces high costs. Applying a protective coating on the interconnects is a feasible approach. However, the added cost of additional manufacturing processes also needs to be considered; c) The surface modification of cathodes can effectively strengthen its Cr resistance. However, isolated nanoparticles are unable to achieve complete coverage of the inherently fragile electrodes. The areas of the cathode surface that are not covered may be still poisoned by contaminants such as CO₂, Cr vapor, and steam. Moreover, if the impregnated surface catalysts are used as a preferred reactant of the gaseous Cr species, they may be continuously consumed for long periods of operation. Although the poisoning effect of the deposits is mild, excessive accumulation of the deposits will occupy the active sites for the oxygen reduction reaction and hinder the diffusion of gases in the cathode; d) The design of contaminants-tolerant high-entropy materials needs to balance electrocatalytic activity and stability. In addition, the interaction mechanisms of different metal cations in the high-entropy cathodes are still unclear and require further in-depth research; and e) Recent progress has been made in the application of SOFCs in electrolysis mode. As is known, the electrode material on the air side undergoes cathodic polarization in fuel cell mode, while in electrolysis mode, anodic polarization occurs on the electrode on the air side. Therefore, the mechanisms and degree of Cr poisoning will be different. Unfortunately, there is still a lack of relevant research on Cr deposition in electrolysis mode. In summary, the phenomenon of Cr poisoning in materials is a long-standing challenge for SOFCs and other high-temperature electrochemical devices. A future research should focus on the investigation on Cr poisoning mechanisms under various operating modes and optimization of the composition and structure of cathode materials. These efforts aim to enhance the stability and reliability of SOFCs in practical industrial applications, ultimately advancing the commercialization process of SOFCs technology.

High-temperature proton-conducting ceramic ammonia synthesis remains in the fundamental research stage, requiring further studies in proton source optimization, material development, and reaction mechanism elucidation. For proton source exploration, previous studies predominantly used high-cost pure H₂ as a proton source. Recent research has confirmed the feasibility of H₂O as a proton source, but this imposes stringent requirements on electrode stability under steam exposure. Hydrogen derived from steam methane reforming or water-gas shift reactions offers an alternative strategy, enabling potential reductions in energy consumption and CO₂ emissions. Future work should systematically evaluate the protonation efficiency of H₂O, CH₄, and other candidates, establishing a matching mechanism among hydrogen sources, electrolytes, and electrodes. Compared to conventional metal electrodes, perovskite electrodes exhibit unique advantages in cost-effectiveness and performance tunability. However, their catalytic activity and long-term operational stability require an enhancement. Strategies such as ionic doping, defect engineering, in-situ exsolution, and advanced synthesis techniques can optimize crystal structures, enabling electrodes with high activity and durability. The reaction mechanism of protonic ceramic ammonia synthesis remains unclear. In-depth studies on catalytic pathways and degradation mechanisms at ammonia electrodes are urgently needed. The density functional theory should be used to calculate N₂ adsorption/activation energy barriers and proton transport pathways, establishing structure-activity relationships between catalyst electronic structures and NRR performance. In-situ X-ray photoelectron spectroscopy and in-situ transmission electron microscopy are used to analyze the proton migration process at the hydrogen electrode and N₂ activation process at the ammonia electrode, and the DRT analysis is used to reveal the velocity determination steps under multi-field coupling conditions. These studies will provide a theoretical support for the construction of efficient and stable proton ceramic ammonia synthesis system.

The"job-sharing" type mixed ionic-electronic conductor presents a promising strategy to overcome the transport limitations inherent in conventional MIECs. JSMCs enable both fast ambipolar diffusion and interfacial charge storage via decoupling ion and electron transport into separate phases and localizing their coupling at interfaces. Composite electrodes in solid oxide cells naturally embody the structural preconditions for JSMCs, making them fertile ground for experimental validation and application of this concept. Several key research directions emerge for the continued development and practical implementation of JSMCs, i.e., a) mechanistic clarification of interfacial space charge layers: Although space charge layers are central to the JSMC mechanism, their microscopic origin, spatial extent, dynamic formation process, and impact on chemical diffusion remain poorly understood. Advanced in-situ techniques like near-ambient pressure X-ray photoelectron spectroscopy, scanning probe microscopy, or operando impedance mapping should be leveraged to directly resolve interfacial electrostatic potential distributions and charge accumulation behavior; b) structural engineering of well-defined model systems: The fabrication of vertically aligned nanostructures or multilayer heterostructures can serve as model JSMC platforms with tunable interfacial density and orientation. Such systems can bridge microscopic interface features with macroscopic electrochemical performance, enabling quantitative structure-function correlations; c) extension to new material systems: To date, most JSMC studies focus on cation conductors (especially Li⁺). A future work should explore a potential of JSMC behavior in oxide-ion conductors, proton conductors, and emerging solid-state or aqueous battery chemistries such as Na⁺ and Zn²⁺ systems, where interfacial ion-electron cooperation may also enhance device performance; and d) multiscale modeling and design principles: To move toward predictive control of JSMC interfaces, it is necessary to integrate first-principles calculations, phase-field simulations, and defect chemistry frameworks. These tools can help establish design rules for optimizing ionic/electronic phase connectivity, space charge layer thickness, and interface orientation for maximum performance. In summary, JSMCs offer a transformative approach to interfacial transport and storage in electrochemical systems. The mechanistic understanding and establishing controllable model systems will be a key to fully unlocking their potential in a wide range of solid-state energy technologies.