Environmental-cost constraints, performance-composition imbalances, and medium-temperature stress relaxation failure significantly hinder the development and application of Cu-Be alloys. In this study, a novel low-cost, high-performance Cu-1.47Be-0.62Ni-0.1Mg alloy was designed using a data-augmented machine learning approach. Subsequently, the synergistic effects of Ni and Mg elements on the microstructure and properties of Cu-Be alloys were investigated, and the unique performance-enhancement mechanism was revealed. Ni and Mg synergistically enhance precipitation strengthening via cooperative regulation of the solid solubility of Be in Cu. Meanwhile, Ni suppresses grain boundary segregation of Be via the formation of the thermally stable NiBe phases, while Mg occupies grain-boundary vacancies and reduces the diffusion rate of Be. This synergy promotes the transition of precipitation behavior from grain-boundary-dominated discontinuous precipitation to intragranular-dominated continuous precipitation. The transition promotes the proliferation of nanoscale NiBe phases and inhibits the formation of coarse-grain-boundary phases, thereby effectively hindering dislocation motion, preventing recrystallization, and significantly improving stress relaxation resistance. Compared to the commercial C17200 alloy, the Cu-1.47Be-0.62Ni-0.1Mg alloy exhibits comparable tensile strength (1350 MPa), 26% higher electrical conductivity (29.2% IACS) and 53% better stress relaxation resistance (8.5% after 200 degrees C/20 h), achieving an 18% reduction in raw cost. This work proposes a novel multicomponent microalloying design strategy based on the quaternary synergistic mechanism of "elemental solubility optimization + second-phase directed precipitation + interfacial solute segregation + grain-boundary vacancy depletion", providing novel insights for the design of other high-performance alloys for extreme service conditions.

Partial substitution of beryllium with low-cost elements, coupled with multi-stage thermo-mechanical treatment, provides a promising pathway for developing high-performance, low-cost beryllium-copper alloys. In this work, the role of Si element in the microstructure and properties of a Cu-Be-Ni-Co alloy during multi-stage thermomechanical treatment was investigated, and the underlying mechanism was elucidated. Si addition promotes grain refinement and transforms the strengthening phase from a single gamma '-(Ni,Co)Be phase into nanoscale (Ni,Co, Si)Be and delta-(Ni,Co)2Si phases. This dual-phase synergistic strengthening effectively suppresses precipitate coarsening during aging. First-principles calculations indicate that Si atoms preferentially occupy Be sites in the (Ni,Co)Be phase, forming strong Si-Ni/Co bonds that lower the precipitate/matrix interfacial energy. Through an optimized multi-stage thermo-mechanical treatment, a novel Cu-1.5Be-0.1Ni-0.3Co-0.2Si alloy was developed, which possesses an ultra-high strength of 1441 MPa and a high electrical conductivity of 24.3 %IACS. This alloy outperforms the conventional C17200 alloy in overall properties, while the reduced consumption of strategic Be lowers raw material costs by approximately 13%. This work provides valuable theoretical and processing insights for the development of high-performance, low-beryllium copper alloys.

The effects and underlying mechanisms of aging treatment on the microstructure and properties of Cu-0.3Be-2.0Ni and Cu-0.3Be-2.0Ni-0.2Al alloys(subjected to solid solution treatment followed by 70%cold rolling)were investigated.Results showed that the Cu-0.3Be-2.0Ni alloy mainly precipitated the Ni-Be phase,while the Cu-0.3Be-2.0Ni-0.2Al alloy exhibited co-precipitation of both Ni-Be and Ni3Al phases.The combined strengthening from nanoscale Ni3Al and Be-Ni phases significantly increased the strength of the Cu-0.3Be-2.0Ni-0.2Al alloy compared to the Cu-0.3Be-2.0Ni alloy.Thermo-mechanical treatment improved both the hardness and electrical conductivity of the alloys.Compared with conventional aging,the Cu-0.3Be-2.0Ni-0.2Al alloy showed a 13%increase in hardness and a 6.8%increase in electrical conductivity,while the Cu-0.3Be-2.0Ni alloy exhibited a 6%hardness increase with slight conductivity improvement.

Existing machine learning-assisted alloy design studies often treat models as “black boxes”, lacking interpretability and thus failing to translate predictions into physically meaningful design guidelines. Herein, we propose an integrated strategy combining feature engineering, autonomous model optimization, and interpretability analysis for the efficient design of (CuNiMn)-X alloys, where X denotes Al, Ti, Cr, and Fe alloying elements, with strength-ductility synergy. Through feature cleaning and grid-based hyperparameter optimization, prediction accuracies of 92.6%, 89.2%, and 88.1% are achieved for the microstructure, compressive strength, and fracture strain models, respectively. Experimental validation of the optimized alloy shows errors of -5.0% for compressive strength and -3.2% for fracture strain. SHapley Additive exPlanations analysis reveals the underlying physical mechanisms: valence electron concentration (M-E10) governs phase selection; mean atomic radius (M-A4) and its variance dominate compressive strength via lattice distortion; variances of fusion enthalpy, covalent radius, and shear modulus collectively control compressive strain through triple homogeneity in thermodynamics, structure, and elasticity. This analysis unveils a mirror-image symmetry between high strength and high ductility in feature space, elucidating the strength-ductility trade-off. Guided by predictions, the (CuNiMn)-Al16Cr16Fe16 alloy is experimentally validated, exhibiting a compressive strength of 2,542 MPa and fracture strain of 15.8%. The microstructure consists of Cr-rich body-centered cubic dendrite arms and a Cu-Ni-Fe-Al-rich face-centered cubic interdendritic network, forming a hard-and-tough dual-phase architecture. This closed-loop design strategy provides a new route for the efficient discovery of CuNiMn-based multi-principal element copper alloys and offers guidance for the broader design of complex multi-principal element alloys.

Copper alloys are critical for high-temperature applications, including rocket combustion chamber linings and nuclear reactor deflectors, yet traditional smelt-cast alloys struggle under extreme thermal conditions. Recent advances in big data and machine learning have enabled the efficient design of high-performance copper alloys. Here, we propose a novel interpretable transfer learning model-the electrical conductivity-hardness artificial neural network (ECH-ANN)-that integrates feature and model transfer to accurately predict properties of dispersion-strengthened copper alloys via powder metallurgy. We further established a feature screening pipeline comprising correlation screening, recursive elimination, recursive addition, and exhaustive screening. Using SHapley Additive exPlanations (SHAP), we quantified the importance and impact of key features on performance. Guided by these insights, a high-performance Cu-5.0 vol% Cr2O3 composite was designed and fabricated through in-situ internal oxidation, achieving a strength of 570 MPa, electrical conductivity of 73.6 % IACS, and a softening temperature of 1000 degrees C, approaching copper's melting point. This work provides a data-efficient framework for designing advanced materials, while elucidating the relationships between component characteristics and material properties.

Thermal compression with different strain rates is applied to Cu-Be alloys with varying beryllium (Be) contents. It is found that a high strain rate enables the secondary electron yield (SEY) of the low-Be alloy (Cu-3Be) to approach that of the high-Be alloy (Cu-3.8Be). The highest SEY is achieved in Cu-3.8Be deformed at 10 s-1, which correlates with the formation of an optimal oxide film thickness of 33.5 +/- 1.7 nm. Based on the modified Arrhenius diffusion model, the oxide film layer model, X 2 = 1.14 & times; 10-20f alpha + 1.17 & times; 10-20f beta + 8.82 & times; 10-15 rho g-5.09 & times; 10-29 rho GND, is established to rationalize the kinetics. The analysis demonstrates that the grain boundary density term (rho g), whose coefficient is approximately five orders of magnitude larger, dominates the oxide film thickness, while the contributions from the bulk phase terms (f alpha, f beta) are limited and the dislocation term (rho GND) exhibits a slight inhibitory effect. The proposed diffusion model quantitatively reveals that grain-boundary density is the dominant factor controlling BeO film growth, whereas the contributions of phase fraction and dislocation density are comparatively weaker. These findings demonstrate that SEY optimization in Cu-Be alloys is governed primarily by microstructural factors rather than composition alone.

The influence of hot-rolling reduction on the evolution of secondary electron yield (SEY) properties in Cu-3.0Be alloy were investigated. The microstructures of both the as-received and oxidation states of the alloy under varying degrees of hot-rolling reduction were systematically examined. It was observed that with increasing hotrolling reduction, the (1 phase becomes progressively fragmented, and the grain size is significantly refined. As the hot-rolling reduction intensifies from 10% to 40%, the population of low-angle grain boundaries significantly decrease, while high-angle grain boundaries significantly increase. Dislocations become increasingly concentrated along the (1 phase interfaces, and the dislocation density increases significantly. During the oxidation process, phase boundaries, grain boundaries, and dislocations successively act as preferential pathways for atomic diffusion, facilitating the formation of BeO layers. Under a 40% hot-rolling deformation, the thickness of the BeO layer is relatively thick, ranging from 21.5 to 41.3 nm. The appropriate thickness of BeO in 40% hotrolling reduction can not only prevent the collision between secondary electrons and the substrate during their transport to the surface, but also promptly obtain the electrons replenished by the substrate, resulting in a favorable linear dynamic range of 0-161.7 eV and a high SEY of 4.49. The SEY of Cu-Be alloy is significantly enhanced through controlling the distribution of the (1 phase, grain size, and dislocation density.

Decades of materials genome remain locked in fragmented multi-modal literature, and existing data-mining techniques struggle to reconstruct Composition–Processing–Structure–Performance (intrinsic and service performance) chains. Herein, we developed DualTrack-MatExtractor, the dual-pipeline framework integratingrule-based natural language processing with large language models for heterogeneous table parsing and text mining.Leveraging high-throughput semantic parsing, it distills 31,833 structured C–P–S–P linkages from 10,098 full-text articles across 7,600 alloys, generating datasets far exceeding the scale of the source literature. It achieves ∼43% higher token efficiency than purely LLM-based methods and a 94.52% extraction F1-score through automated anomaly detection. Distilled knowledge is validated through two pathways: Processing-route knowledge graphs with distributions of PFZ width, yield strength, and corrosion Icorr, reveal processing–structure–property relationships previously buried. Application to representative alloy systems further demonstrates predictive capability: for Cu-Ni-Sn, semantic co-word networks enables a physics-informed neural net-work prediction of strength-conductivity with 92% accuracy; for Al-Zn-Mg-Cu, a DeepSeek-Math-7B–assisted symbolic regression enables strength–ductility synergy (Q-index) prediction with an R-squared of 93.60%, far exceeding the standalone model (41.12%);for high-entropy alloys, 1,340 extracted samples train a neural network achieving 95.9% accuracy in phase-structure prediction. Overall, DualTrack-MatExtractor offers a scalable and high-fidelity pathway for materials data mining and AI-enabled materials design.

Al-Fe-Cu alloy is easy to form coarse Fe-rich phase under conventional casting conditions, which seriously damages its plasticity and restricts its further application in engineering. Rare earth as a modifier can significantly improve the microstructure and properties of the alloy. In this study, four Al-Fe-Cu alloys with different Y contents were prepared, and the regulation mechanism of Y on the grain structure and coarse phases of Al-Fe-Cu alloy were investigated, and the synergistic improvement of strength and elongation of the alloy was realized. The results show that the addition of Y can cause constitutional supercooling and refine the alpha-Al grains and secondary dendrite arm spacing of the as-cast alloy. The microstructure of the as-cast alloy without Y addition is mainly composed of Al6Fe phase discontinuously distributed along the grain boundaries and Al-Al6Fe eutectic at the intersections of grain boundaries. The addition of Y can change the solidification behavior of the alloy, change the type, amount, and size of the coarse phases in the alloy, especially promote the formation of Al10Fe2Y phase. Excessive Y can lead to the formation of lamellar Al-Al3Y eutectic. The optimal comprehensive mechanical properties (UTS = 88 MPa, YS = 44 MPa, EI = 32.2%) can be obtained by adding 0.25% Y, which is mainly attributed to the smaller alpha-Al grains and Al-Al10Fe2Y eutectic structure.

High strength copper alloy is the key structural material for manufacturing high-end products, which is often accompanied by discontinuous precipitation (DP) during aging treatment. DP is not completely harmful, and reasonable adjustment can improve the properties of the alloy. However, the current adjustment method for DP is still insufficient. Aging temperature is considered as the adjustment parameter of DP. However, a few studies on the effect of temperature on DP have given contradictory conclusions, and the existing precipitation model has not explained this contradiction. In this paper, the temperature dependent precipitation behavior in Cu-5.0Ni-1.2Si alloy was studied by using the thermodynamic model coupled with continuous precipitation (CP) and DP competition effect. The results show that the initial nucleation rate of DP peaks at 550 °C, while the initial growth rate increases monotonically with aging temperature. Both the nucleation rate and growth rate of DP are time-dependent variables affected by solute competition between continuous and discontinuous precipitation. The maximum volume fraction of DP cellular structure is obtained at 450 °C. At the same time, the spacing of DP increases monotonously with temperature. The correctness of the model prediction is verified by the corresponding system experiments. Aging temperature mainly affects the relative rate of grain boundary diffusion and volume diffusion, which changes the competition between CP and DP. The contradictory conclusions in previous studies are explained. This study provides theoretical support for the development of ultra-high strength copper alloys.

High-Be Cu-Be alloys exhibit dendritic segregation and brittle β/γ phases, which complicate processing and applications. This study investigates the influence of cooling path on eutectoid transformation, microstructure, and mechanical properties in Cu-2.8Be and Cu-3.8Be alloys. A two-step homogenization treatment effectively eliminates segregation and suppresses the formation of harmful acicular phases. Diffusion-kinetic and thermodynamic analyses demonstrate that both the initial temperature and cooling rate determine eutectoid morphology and extent. Crystallographic and Eshelby-based analyses reveal that the β → γ transformation involves an isotropic contraction of ∼3.9 α = 0.0167, ε γ = 0.0095). The mechanical incompatibility and high internal strain at these interfaces cause stress concentration and crack initiation. This work establishes a process-micro-structure-property-mechanism framework essential for controlling the performance of high-Be Cu-Be alloys.

A hypoeutectic Al-Fe alloy was prepared by chill casting, and the evolutions of microstructure and mechanical properties of the alloy during homogenization were investigated. The results show that in the as-cast alloy, a large amount of discontinuously Al6Fe phase is distributed on the grain boundaries, and coarse rod-like Al-Al6Fe eutectic structure is distributed at the intersections of grain boundaries. During homogenization of the alloy, the phase transformation from Al6Fe to Al3Fe occurs through the dissolution-reprecipitation mechanism, including the gradual dissolution of Al-Al6Fe eutectic and the precipitation of Al3Fe phase. The dissolution process of Al6Fe phase in the eutectic structure includes necking, fragmentation, and dissolution. The phase transformation begins at 530 degrees C, characterized by the nucleation of Al3Fe phase between 530 and 580 degrees C, while by the growth of Al3Fe phase between 580 and 630 degrees C. Compared with the as-cast alloy, the elongation of the alloy increases significantly and the ultimate tensile strength decreases slightly after homogenization at 480 degrees C for 8 h, which is due to the elimination of dendritic segregation and the relief of residual stresses. With the increase of homogenization temperature and time, the ultimate tensile strength gradually increases and the elongation gradually decreases, which is attributed to the transformation from Al6Fe phase to rod-like Al3Fe phase.

High thermal resistance oxygen-free copper (OFC) is an essential component of power semiconductor devices. The effects of ultra-trace Mo, Ce, and Yb elements on the thermal stability and grain boundary migration behavior of OFC are methodically examined in this work. The results indicated that Mo element considerably suppressed grain boundary migration in OFC at high temperatures. The average grain size of the Mo-containing OFC was only 50 % that of the conventional OFC after 20 min of soaking at 1070 degrees C. In particular, the average grain size containing Sigma 3n boundaries was measured to be around 90 mu m. It was discovered how ultra-trace Mo, Ce, and Yb elements improve thermal stability. Both Mo and Ce elements reduce the grain boundary energy, thereby stabilizing the OFC system in a lower energy state and suppressing grain boundary migration at high temperatures. Through the grain boundary pinning effect, Mo and Yb elements both prevent grain boundary migration at high temperatures. In particular, the Mo element performs a dual role by providing a strong pinning effect and concurrently lowering the grain boundary energy, which together give the OFC remarkable thermal stability. The primary mechanisms for enhancing the thermal stability of OFC involve two aspects. Firstly, resistance to grain boundary migration is increased by reducing grain boundary connectivity and enhancing the pinning effect of alloying elements. Secondly, the driving force for grain boundary migration at high temperatures is reduced by lowering the grain boundary energy. This study provides fresh perspectives on the microstructure management of heat-resistant OFC and ultra-trace alloying design.

Conventional liquid-phase in-situ synthesis of Cu-TiB2 composites often suffers from coarse and non-uniformly distributed reinforcements. These challenges stem from an insufficient understanding and a lack of effective control over the in-situ nucleation and growth mechanisms of TiB2 particles. This study introduces a novel melt dispersionturbulent mixing (MDTM) in-situ reaction technology to fabricate high-performance Cu-TiB2 composites. The MDTM strategy synergistically refines reaction micro-regions by reducing the initial melt droplet size via melt dispersion while enhancing solute convection via turbulence. This synergy promotes high-density nucleation and refinement of TiB2 particles. Based on turbulence characteristics and in-situ reaction kinetics, we optimized the melt disperser parameters and established a quantitative model linking particle size to disperser rotation speed and reactant solute concentration. It was found that disperser rotation speed governs three distinct nucleation and growth mechanisms for TiB2 particles. Low-density nucleation at low disperser rotation speeds (0–50 r/min) leads to coarse TiB2 particles. At medium rotation speeds (100–150 r/min), the refinement of micro-regions in the dual-melt reaction achieves high-density TiB2 nucleation. Conversely, at high rotation speeds (150–200 r/min), intense turbulence weakens the nucleation driving force and induces TiB2 particle coarsening. This work provides new insights into liquid-phase in-situ reaction mechanisms and offers a novel, controllable route for fabricating highperformance micro/nano particle-reinforced metal matrix composites.

ABSTRACT To overcome the strength‐conductivity trade‐off in Cu–Ni–Sn alloys, this study integrates machine learning, thermodynamic calculations, and experiments to design alloys from a phase‐selectivity perspective. SHAP analysis reveals that the coexistence of Ni 3 Sn and Ni 3 Sn 2 precipitates yields synergistic strengthening superior to single‐phase precipitation. Feature screening identifies the variance of covalent radius and mean Allred–Rochow electronegativity as key physical parameters coupling mechanical and electrical properties. By combining a support vector regression model with thermodynamic phase‐equilibrium calculations, an optimal 350°C aging design window, corresponding to a Sn/(Ni + Sn) mass ratio of approximately 40%–56%, was identified for dual‐phase coexistence. Experimental validation of the predicted Cu–9Ni–9Sn alloy demonstrates a favorable property balance, achieving a tensile strength of 1351 MPa, hardness of 415 HV, and electrical conductivity of 12.43% IACS. Microstructural analysis confirms that the excellent performance originates from the multi‐scale dislocation obstruction by γ‐DO 22 /γ‐L1 2 ordered phases and Ni 3 Sn 2 precipitates, coupled with reduced electron scattering due to solute depletion. This closed‐loop paradigm offers a transferable route for multiphase regulation in precipitation‐strengthened alloys.

High strength and conductivity (HSC) copper alloys with excellent high-temperature softening resistance are critical for applications such as high-power electrical connectors and controlled nuclear fusion. In this study, a Cu-1.0Cr-0.4Zn-0.1Zr-0.05Si alloy with an initial heterogeneous structure was prepared by an intensive plastic deformation (IPD) method (solution treatment followed by rotary swaging and aging). The IPD-prepared Cu-Cr-Zn-Zr-Si alloy exhibited a softening temperature of 640 degrees C, outperforming existing HSC copper alloys. To investigate the effect of the initial deformation structure on the resistance to softening, in-situ high-temperature electron backscatter diffraction was used to observe the recrystallization behavior. The initial multi-oriented heterogeneous fibrous structure was formed by alternating <111>//LD and <100>//LD deformation bands, which induced an uneven stored energy distribution and led to partial recrystallization. Johnson-Mehl-Avrami-Kolmogorov (JMAK) kinetic analysis indicated that this structural feature significantly reduced the softening rate in the later stages of recrystallization. Furthermore, IPD facilitated the transformation of <111>//LD deformation bands into <100>//LD recrystallized grains through annealing twinning, revealing a novel recrystallization mechanism contrary to conventional theories. Molecular dynamics (MD) simulations confirmed that this orientation transformation further promoted the recrystallized grain boundary migration. This study provides novel experimental evidence and theoretical insights into improving the high-temperature softening resistance through deformation structure design. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

A Cu-1.9Ni-1.9Co-0.9Si (mass fraction, %) alloy with high strength and electrical conductivity was designed by cluster formula approach. The microstructure evolution of the alloy during thermomechanical treatment was systematically investigated. The strengthening mechanism and electrical conductivity of the alloy were discussed in detail. The optimal thermomechanical treatment process was as follows: solid solution-* 80% cold rolling-* (450 degrees C, 4 h) aging-* 50% cold rolling-* (400 degrees C, 4 h) aging. The designed alloy achieved excellent comprehensive properties with a microhardness of HV 260, a yield strength of 843 MPa, a tensile strength of 884 MPa, and an electrical conductivity of 42.6%(IACS). Compared to direct aging treatment, the designed alloy subjected to multi-stage thermomechanical treatment had refined grains, high density of dislocations, and accelerated of precipitation of (Ni,Co)2Si precipitates. High strength was mainly attributed to the combined effect of dislocation strengthening, work hardening and sub-grain strengthening, while good electrical conductivity was maintained through the precipitation of the large number of nanoparticles.

Machine learning-assisted methods for rapid and accurate prediction of temperature field, mushy zone, and grain size were proposed for the heating-cooling combined mold (HCCM) horizontal continuous casting of C70250 alloy plates. First, finite element simulations of casting processes were carried out with various parameters to build a dataset. Subsequently, different machine learning algorithms were employed to achieve high precision in predicting temperature fields, mushy zone locations, mushy zone inclination angle, and billet grain size. Finally, the process parameters were quickly optimized using a strategy consisting of random generation, prediction, and screening, allowing the mushy zone to be controlled to the desired target. The optimized parameters are 1234 degrees C for heating mold temperature, 47 mm/min for casting speed, and 10 L/min for cooling water flow rate. The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7 degrees.

The advanced alloy design relied on machine learning (ML) methods has been widely employed. Nevertheless, the traditional empirical and data-driven methods were generally lack of physical interpretability, which largely misunderstood the underlying mechanisms to hinder the potential applications of models to some extent. Herein, a dual engine-driven alloy design framework is developed by integrating large language models (LLMs) with physics-informed neural networks (PINN). Compared with traditional ML methods, the evolution of microstructure such as phase transition precipitation sequence can be extracted by LLMs to embed into the neural network, thus largely enhancing physical interpretability and accuracy (from 84% to 92%) for models to guide novel alloy design. Taking the Cu-Ni-Sn as a typical example, our proposed strategy screened 234,361 alloys and identified Cu-10Ni-11Sn-0.1In and Cu-7.5Ni-11Sn-0.1In, based on their optimal balance of strength and conductivity. The targeted alloys were then prepared experimentally, which could deliver a tensile strength of 1610MPa and 1441MPa, and an electrical conductivity of 13.34%IACS and 16.73%IACS, respectively, which are superior to the existing Cu-15Ni-8Sn alloy (1379MPa, 8.78%IACS).

Heterostructured materials achieve excellent strength-ductility matching through the cooperative stress-strain distribution mechanism of soft and hard phases. Inspired by the periodic arrangement of hard and soft zones in biological armors which can achieve load redistribution and impact dissipation, this work successfully constructed a biomimetic armor structure of multi-scale heterostructures (MSH) in Cu-2.9 wt.% Ti alloy sheets based on the localized electropulsing treatment (LEPT) technology. Room-temperature tensile testing combined with Digital Image Correlation (DIC) analysis revealed that the heterostructure reduces strain concentration during deformation. This results in a similar to 64 % enhancement in ductility compared to conventionally aged samples, with only a similar to 14 % reduction in strength, leading to a 36 % increase in the strength-ductility product (UTS x EL). The retained extensive soft zones provide a foundation for good ductility, while the heterogeneous deformation-induced (HDI) strain hardening and HDI strengthening resulting from deformation incompatibility significantly enhance the alloy's strength, effectively suppress localized necking, and enable sustained, uniform deformation. This multi-scale heterostructure significantly enhances the strain hardening capacity of the material, not only providing a novel paradigm for designing high-strength, high-ductility alloys, but also offering theoretical basis and technical support for the flexible design and targeted regulation of heterostructured materials. (c) 2025 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.