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The vast chemical composition space of multicomponent ceramics provides opportunity for regulating their properties and performances. Other than trial-and-error and screening, a physics-and-data co-driven material design strategy was reported to efficiently explore chemical composition space through establishing prediction model between physical properties and chemical composition via physics-guided data mining method. Firstly, causal and correlative relationships were discriminated according to physicochemical knowledge. Secondly, ensemble features such as chemical formula and chemical bond were digitized by constructing descriptors for crystalline solids with atomic features including atomic weight and radius contained in the Periodic Table of elements. Finally, quantitative causal relations between material structure/properties and ensemble descriptors were mined out of small dataset using a least square regression method, on which physical prediction model established therefore for forward predicting structure/properties and inverse designing chemical composition. Choosing diboride ceramics as a case, those causal analytic relations of composition-structure and composition-property were successfully derived and illustrated to predict the value of structure and properties accompanying with quantitative relation between chemical pressure and chemical bonding ensemble descriptors. This method bridges the connection between atomic features and macroscopic properties via ensemble descriptors, which significantly accelerates the discovery of new materials. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing (MAM) technology has experienced rapid development in recent years. As both equipment and materials progress towards increased maturity and commercialization, material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM. The calculation of phase diagrams (CALPHAD) is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems. By combining with the element mobility data and non-equilibrium phase transition model, it has been widely used in the analysis of traditional metal materials. The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials, the reduction of metallurgical imperfections, and the control of microstructural attributes. This endeavor carries considerable theoretical and practical significance. This paper summarizes the important achievements of CALPHAD in additive manufacturing (AM) technology in recent years, including material design, process parameter optimization, microstructure evolution simulation, and properties prediction. Finally, the limitations of applying CALPHAD technology to MAM technology are discussed, along with prospective research directions. [ABSTRACT FROM AUTHOR]

With the advancement of the manufacturing industry, performing submerged arc welding subject to varying welding heat inputs has become essential. However, traditional thermodynamic models are insufficient for predicting the effect of welding heat input on elemental transfer behavior. This study aims to develop a model via CALPHAD technology to predict the influence of heat input on essential elements such as O, Si, and Mn when typical SiO2-bearing fluxes are employed. The predicted data demonstrate that the proposed model effectively forecasts changes in elemental transfer behavior induced by varying welding heat inputs. Furthermore, the study discusses the thermodynamic factors affecting elemental transfer behavior under different heat inputs, supported by both measured compositions and thermodynamic data. These insights may provide theoretical and technical support for flux design, welding material matching, and composition prediction under various heat input conditions subject to submerged arc welding processes when SiO2-bearing fluxes are employed. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Minor alloying is an effective method to improve the performance of lead-free solder alloys. In this study, we propose a complementary Machine Learning (ML) strategy for minor alloying to design solder alloys with enhanced creep resistance. Two ML models, leveraging compositional and knowledge-aware features, respectively, were constructed to predict the creep stress exponent of Sn–Ag–Cu (SAC)-based solder alloys. Five new solder alloys were designed and experimentally evaluated by screening the virtual sample space consisting of the critical elements, including Bi, In, and Ni, affecting the creep resistance of SAC solder alloys with the ML model. The creep resistance of the designed alloys was characterized using nanoindentation tests. Notably, the creep stress exponent of SAC387-3 wt % Bi-0.4 wt% Ni was determined to be 12.82 at room temperature, indicating a significant 33.9% decrease in creep strain rate compared to the SAC387 alloy. This study demonstrates the potential of the ML approach to design solder alloys with enhanced creep resistance.

Core structural materials such as cladding in advanced nuclear reactors face harsh service environments such as high-temperature, and their comprehensive performance is one of the bottlenecks restricting the development of advanced reactors. Mo and its alloys are key candidate, but pure Mo suffers from a lack of high-temperature strength. Mo-based alloys are superior to pure Mo in terms of mechanical properties. Research found that the addition of a certain amount of Re, W, Ti, Zr, C and other elements will enhance the high-temperature strength of Mo-based alloys. The effects produced by the ingredients are very complex and cannot be found in great detail by testing. Now materials genetic engineering, based on the prediction of data-driven material properties and optimization, shortening the cycle of materials research and development, and thus speed up the process of the entire field. Thus a CNN regression model based on Bayesian optimization was constructed based on the data obtained from literature research and preliminary experiment. The collected Mo-based alloy dataset contained 220 samples and 38 compositions as well as test temperature characteristics of tensile with the target variable being tensile strength. Found that the Re, C, O, N, Ti, Zr, Nb, La, W, Hf, Ta, Si, and Ni have a higher weight on the target value, and C, Ti, and Zr positively affect the tensile strength. Re, O, N, La, W, Hf, Ta, Si, and N have a negative tendency to correlate the characteristics with the tensile strength. Using the R2 value as an evaluation metric, the model is able to achieve 0.832 6. Combined with correlation analysis and modeling prediction analysis, it is concluded that Ti, Zr, Re and La2O3 are beneficial to the enhancement of high-temperature tensile strength of Mo-based alloy. N and Si are not conducive to the enhancement of tensile strength of Mo-based alloy at high-temperature. According to the correlation analysis conclusion, the effect of the coupling of these compositions C-Zr, Nb-Hf-Ta, Ni-Si, N-O and Zr-Ti on the tensile properties of the materials at high-temperatures will be considered subsequently, along with the simultaneous enhancement of toughness. Subsequently, by analyzing the influence of the combination of two elements or three elements on the change of tensile strength, it can get the composition design scheme that the tensile strength of Mo-based alloy reaches a certain range under specific temperature conditions.

The development of advanced nuclear energy systems imposes stringent requirements on the service stability of nuclear materials under extreme environments characterized by multiple stressors. Continuous silicon-carbide-fiber-reinforced silicon carbide (SiCf/SiC) ceramic matrix composites possess advantages such as low density, excellent high-temperature mechanical properties,corrosion resistance, and irradiation tolerance. Furthermore, SiCf/SiC composites demonstrate a pseudo-ductile fracture behavior under external forces, positioning them as highly promising structural materials for advanced nuclear energy systems. This study systematically summarizes the fundamental research framework on nuclear-grade SiCf/SiC composites at the material, component,and service performance levels. It also analyzes the developmental trends in this field among traditional nuclear powerhouses such as the United States, France, and Japan, as well as in other emerging nuclear energy countries and China. Furthermore, the study identifies existing issues and challenges faced by China’s nuclear-grade SiCf/SiC sector in terms of raw materials, data accumulation,and patent standards, and proposes targeted measures and suggestions: (1) strengthening the research and development of material preparation technologies, (2) developing a new paradigm of research and development, (3) reinforcing the industry-universityresearch-application cooperation, and (4) enhancing international exchanges on the basis of maintaining independence. The aim of the study is to provide guidance and reference for the research directions and policy-making in China’s nuclear-grade SiCf/SiC sector.

Abstract The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure–property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high‐dimensional spaces. This review exclusively focuses on the current state‐of‐the‐art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse‐grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm‐aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed. [ABSTRACT FROM AUTHOR]

Aqueous zinc‐ion batteries (AZIBs) are considered as the promising candidates for large‐scale energy storage because of their high safety, low cost and environmental benignity. The large‐scale applications of AZIBs will inevitably result in a large amount of spent AZIBs, which not only induce the waste of resources, but also pose environmental risks. Therefore, sustainable AZIBs have to be considered to minimize the risk of environmental pollution and maximize the utilization of spent compounds. Herein, this minireview focuses on the sustainability of AZIBs from material design and recycling techniques. The structure and degradation mechanism of AZIBs are discussed to guide the recycling design of the materials. Subsequently, the sustainability of component materials in AZIBs is further analysed to pre‐evaluate their recycling behaviors and mentor the selection of more sustainable component materials, including active materials in cathodes, Zn anodes, and aqueous electrolytes, respectively. According to the features of component materials, corresponding green and economic approaches are further proposed to realize the recycling of active materials in cathodes, Zn anodes and electrolytes, respectively. These advanced technologies endow the recycling of component materials with high efficiency and a closed‐loop control, ensuring that AZIBs will be the promising candidates of sustainable energy storage devices. This review will offer insight into potential future directions in the design of sustainable AZIBs. [ABSTRACT FROM AUTHOR]

Polymer-based dielectric capacitors have become important energy storage components in electronic power systems attributed to their high breakdown strength, low dielectric loss, good self-healing characteristics and easy-processability. However, the poor dielectric constant and energy storage density of polymers severely limit further practical applications of polymer-based film capacitors. Therefore, it is critical to enhance the dielectric constant as well as the energy density and the energy storage efficiency of polymer-based capacitors. Polyvinylidene fluoride (PVDF) has become a hot research topic due to its good dielectric properties and high discharged energy density. Beginning with the energy storage principle of dielectrics, three major schemes for the material design and the performance regulation of PVDF-based nanocomposite dielectrics are reviewed: (1) polymers + inorganic high-dielectric nanofillers; (2) polymers + inorganic low dielectric nanofillers; (3) polymers + metal nanoparticles. This review provides an important reference for further improving the energy storage performance of polymer-based dielectric capacitors. [ABSTRACT FROM AUTHOR]

Copyright of BHM Berg- und Hüttenmännische Monatshefte is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, as the upcoming limitation of operational speed enhancement in conventional silicon device technologies, quantum computing methodologies are realized one of the promising candidates that could potentially instead conventional multi-core supercomputer platforms with GPUs. The functional material optimization problem, that idealize high performance electric vehicle powertrain systems, would be a suitable target for benchmarking the efficiency of this novel high performance computing technologies. Regarding this direction, for the purpose of elucidating latent powerfulness of quantum annealing algorithms in functional material design, we performed quantum inspired study for the optimization of compound compositional design with substituting heterogeneous elements. (Quantum inspired computing is as the simulation technology for solving specific Ising Hamiltonian that reproduce energetic behavior of systems under the latest GPU platforms.) Carbon material design, in relation to fuel cell catalyst or electrode material for Li ion battery in automotive application, was studied as the representative case in functional material investigation. Notable optimization performance improvement was observed. [ABSTRACT FROM AUTHOR]

The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries. [ABSTRACT FROM AUTHOR]

Titanium alloy exhibits exceptional performance and a wide range of applications, with the high performance serving as the foundation for the development. However, traditional material design methods encounter numerous calculations and experimental trial-and-error processes, leading to increased costs and decreased efficiency in material design. The datadriven model presents an intriguing alternative to traditional material design methods by offering a novel approach to expedite the materials design process. In this study, a framework for computer-aided design high performance titanium alloys based on machine learning is proposed, which constructs an intelligent search space encompassing various combinations of 18 elements to facilitate alloy design. Firstly, a proprietary dataset was constructed for titanium alloy materials using feature design and a combination of unsupervised and supervised feature engineering methods. Secondly, six machine learning algorithms were employed to establish regression models, and the hyperparameters of each algorithm were optimized to improve model performance. Thirdly, the model was screened using five regression algorithm evaluation methods. The results demonstrated that the selected optimized model achieved an R² value of 0.95 on the verification set and 0.93 on the test set, yielding satisfactory outcomes. Finally, a comprehensive model framework along with an intelligent search methodology for designing highstrength titanium alloys has been established. It is believed that this method is also applicable to other properties of titanium alloys and the optimization of other materials. [ABSTRACT FROM AUTHOR]

The aim of this study is to design various instructional materials to teach biological systems in a 'Systems in Our Body' unit to 6th grade students with visual impairment (SVI) and to evaluate the effectiveness of these materials. In the study, a design-based research model was used. In the first stage, individual learning needs of SVI and their understanding of biological structures in the 'Systems in Our Body' unit were determined. In the second stage, instructional materials matching the individual needs were designed and implemented. In the last stage, the usefulness of these materials and their effects on learning were explored. As a result of the study, it was seen that designed instructional materials made positive contributions to students' learning and understanding. [ABSTRACT FROM AUTHOR]

Several interactions can occur between the liquid clad and solid core alloys during brazing of multi-layered Al brazing sheets used in automotive heat exchanger fabrication. The objective of the current study is to evaluate the use of a physical barrier layer between the clad and core alloys to prevent the occurrence of certain solid–liquid interactions during brazing. A novel brazing sheet comprised an AA4045 clad, AA1100 barrier layer, and Al–1.95 Mg–1.5Mn–0.5Cu core alloy (NS) was investigated and compared with a commercially available AA4045/X901 brazing sheet (CS). Interrupted tensile tests were performed to impart strains between 0 and 10% in both sheet conditions prior to brazing. Differential scanning calorimetry (DSC) was used to measure the brazing characteristics through the ratio of solidification peak energy to initial melting peak energy after a 3-min hold at 600 °C (i.e., ΔHsol/ΔHmelt). The post-braze microstructures of DSC samples and samples subjected to furnace brazing were investigated. The ΔHsol/ΔHmelt ratio for both CS and NS conditions exhibited a non-linear relationship with the level of applied strain prior to brazing, but the nature of the relationship was different for the two sheets. Liquid film migration (LFM) was generally observed in CS conditions exhibiting low values of ΔHsol/ΔHmelt. The NS barrier layer was found to effectively prevent LFM in strained brazing sheet, but diffusional solidification of the clad layer still resulted in relatively low ΔHsol/ΔHmelt values at low levels of pre-strain. It was also found that the post-braze microstructures of samples heat treated in the DSC and furnace were different, with furnace-treated samples containing a greater volume of Al–Si eutectic due to the shorter time at elevated temperatures. It is concluded that the barrier layer can be an effective method to prevent liquid film migration in strained brazing sheet. [ABSTRACT FROM AUTHOR]

To overcome the complex challenges of our time and prepare for a sustainable future, we must break new ground and find strategies for collaboration. In this experimental case study, we demonstrate how industrial design and materials science worked together on an equal footing to design a public status symbol. Since both disciplines are practice-oriented, the focus was on creating a specific object: a seat for a public bus as a pars pro toto for enhancing the value of public mobility. The design object was at the same time the object around which interdisciplinary collaboration evolved. In spite of differences in thinking between the two disciplines, the approaches finally enriched each other. The most important findings include the identification of mesh structures as a common denominator for the interdisciplinary co-design. The mesh structures' flexibility fulfilled ergonomic requirements and, at the same time, their aesthetic and haptic qualities inspired the development process. Working together between disciplines from the outset can help transform industrial design in a way that addresses the fundamental environmental challenges of our time. [ABSTRACT FROM AUTHOR]

Segmented thermoelectric generators (STEGs) can exhibit present superior performance than those of the conventional thermoelectric generators. Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg. This may significantly hinder performance improvement. In this study, a five-layer STEG with three pairs of thermoelectric (TE) materials was investigated considering the thermal and electrical contact resistances on the material contact surface. The STEG performance under different contact resistances with various combinations of TE materials were analyzed. The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning. Based on the genetic algorithm, for each contact resistance combination, the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency. To reveal the underlying mechanism that determines the heat-to-electrical performance, the total electrical resistance, output voltage, ZT value, and temperature distribution under each optimized scenario were analyzed. The STEG can augment the heat-to-electricity performance only at small contact resistances. A large contact resistance significantly reduces the performance. At an electrical contact resistance of RE = 10–3 K·m2·W−1 and thermal contact resistance of RT = 10–8 Ω·m2, the maximum electric power was reduced to 5.71 mW (90.86 mW without considering the contact resistance). And the maximum energy conversion efficiency is lowered to 2.54% (12.59% without considering the contact resistance).

Lightweight design is a key challenge to further reduce the worldwide greenhouse gas emissions. To be able to design lightweight concepts in a fast-changing environment, a stronger interlinking between engineering disciplines is required. Therefore, the generic and extendable method of component-oriented material design is postulated. In this method, not only the standardized material properties are considered during the development process, but rather the material properties tailored to the application (e.g., stress-state-dependent strain variable) and the component performance (e.g., energy absorption capacity) are formulated based on simulations as target variables within the material design process. The developed method’s potential was demon­strated for a lean medium manganese steel (MMnS) on the example of a b-pillar subjected to quasi-static loading in a side impact test. The results show that the designed MMnS exhibits better ductility properties (εkrit = 0,52) compared to the reference material 22MnB5 (εkrit = 0,17). The strength (Rp0.2 = 1049 MPa, Rm = 1404 MPa) is at similar level to that of the reference material. Subsequently, from the concluding numerical evaluation of the component performance, it is evident that the energy absorption potential of the b-pillar consisting of the developed steel could be significantly increased compared to the reference material.

AbstractAcoustic metamaterials (AMMs), with extraordinary physical properties, could be used to suppress the specific frequencies elastic waves by altering the structure artificially. However, as the complexity of AMMs structures continues to rise, classical design methods are time-consuming and high-computational. Therefore, there is a pressing need to explore more efficient and accurate design methods for AMMs. In this work, a deep learning algorithm-based forward and inverse design method for acoustic metamaterial plates (AMPs) is proposed. First, the initial samples of AMPs are created with parametric model and the bandgaps properties of the AMPs are generated by the finite element method. The dataset consists of different structure parameters and corresponding bandgap characteristics. Then, A neural network model is constructed by concatenating a pretraining network and an inverse design network. Through inputting the dataset to the concatenated network, the mapping relationship between the structural parameters and the bandgap characteristics of the AMPs can be explored. Ultimately, the trained network enables both forward designs, yielding bandgap characteristics for given structural parameters, and inverse design, deducing structural parameters for specific bandgap characteristics. The accuracy of the proposed design methodology is verified through illustrative examples. The results demonstrate that the trained neuron networks can effectively replace the complex physical mechanisms between the structural parameters and bandgap characteristics. [ABSTRACT FROM AUTHOR]

High energy‐conversion efficiency (ZT) of thermoelectric materials has been achieved in heavy metal chalcogenides, but the use of toxic Pb or Te is an obstacle for wide applications of thermoelectricity. Here, high ZT is demonstrated in toxic‐element free Ba3BO (B = Si and Ge) with inverse‐perovskite structure. The negatively charged B ion contributes to hole transport with long carrier life time, and their highly dispersive bands with multiple valley degeneracy realize both high p‐type electronic conductivity and high Seebeck coefficient, resulting in high power factor (PF). In addition, extremely low lattice thermal conductivities (κlat) 1.0–0.4 W m−1 K−1 at T = 300–600 K are observed in Ba3BO. Highly distorted O–Ba6 octahedral framework with weak ionic bonds between Ba with large mass and O provides low phonon velocities and strong phonon scattering in Ba3BO. As a consequence of high PF and low κlat, Ba3SiO (Ba3GeO) exhibits rather high ZT = 0.16–0.84 (0.35–0.65) at T = 300–623 K (300–523 K). Finally, based on first‐principles carrier and phonon transport calculations, maximum ZT is predicted to be 2.14 for Ba3SiO and 1.21 for Ba3GeO at T = 600 K by optimizing hole concentration. Present results propose that inverse‐perovskites would be a new platform of environmentally‐benign high‐ZT thermoelectric materials. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. [ABSTRACT FROM AUTHOR]

Magnesium (Mg) alloy is an extremely excellent structural material, and the improvement of its mechanical properties has attracted extensive attention. However, there is still lack of a systematical study that considers various types of Mg alloys and versatile mechanical performance. In this paper, extensive and in-depth machine learning (ML) research is carried out on the four mechanical properties of Mg alloys, including ultimate tensile strength (UTS), elongation (EL), yield strength (YS) and hardness (HV). By collecting experimental data in the published literatures, the mechanical properties database of Mg alloy was established. The experimental parameters, basic properties and thermodynamic parameters of alloys are used as descriptors to construct the ML models thus predicting the four mechanical properties of Mg alloys. In the total dataset and the cast and extrusion subsets, the optimum prediction models for UTS, YS, and HV achieved high accuracy, with the values of coefficients of determination above 0.8 and a maximum value of 0.93 in the test set, although the prediction model of EL was not satisfactory. In addition, the machine learning model is interpreted by using the Shapley additive explanations (SHAP) model, and the critical values of important descriptors affecting the mechanical properties are obtained, especially the range of feature values that can improve YS and EL at the same time, which is of great significance for the development of the mechanical properties in the design of high-performance Mg alloys. [ABSTRACT FROM AUTHOR]

Copyright of INES Journal: The Journal of International Education Science / Uluslararası Eğitim Bilimleri Dergisi is the property of INES Journal: The Journal of International Education Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A family of solids including crystalline phase change materials such as GeTe and Sb2Te3, topological insulators like Bi2Se3, and halide perovskites such as CsPbI3 possesses an unconventional property portfolio that seems incompatible with ionic, metallic, or covalent bonding. Instead, evidence is found for a bonding mechanism characterized by half‐filled p‐bands and a competition between electron localization and delocalization. Different bonding concepts have recently been suggested based on quantum chemical bonding descriptors which either define the bonds in these solids as electron‐deficient (metavalent) or electron‐rich (hypervalent). This disagreement raises concerns about the accuracy of quantum–chemical bonding descriptors is showed. Here independent of the approach chosen, electron‐deficient bonds govern the materials mentioned above is showed. A detailed analysis of bonding in electron‐rich XeF2 and electron‐deficient GeTe shows that in both cases p‐electrons govern bonding, while s‐electrons only play a minor role. Yet, the properties of the electron‐deficient crystals are very different from molecular crystals of electron‐rich XeF2 or electron‐deficient B2H6. The unique properties of phase change materials and related solids can be attributed to an extended system of half‐filled bonds, providing further arguments as to why a distinct nomenclature such as metavalent bonding is adequate and appropriate for these solids. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years multilingual approaches to teaching have been widely discussed and translanguaging has been proposed as a pedagogy that offers very positive results in many contexts. Overall, the need to reverse monolingual assumptions among teachers and to adapt pedagogical translanguaging to each context has been highlighted. For that purpose, training preservice teachers on multilingualism, translanguaging and on how to transform their educational practises is fundamental. In the present study, we train 117 preservice teachers on translanguaging and materials' design and analyse the created materials quantitatively and qualitatively. The outcomes reveal some common traces among the materials presented and these are analysed as related to infant and primary teachers' professional development and to a broader educational context. [ABSTRACT FROM AUTHOR]

In this research, which was conducted to improve the web-based material design skills of Turkish teacher candidates, action research pattern, one of the qualitative research methods, was used. The study group of the research consisted of 61 teacher candidates studying in the second year of Yıldız Technical University, Faculty of Education, Department of Turkish Language Teaching. The research was carried out within the scope of the instructional technologies course. The material design rubric was used to observe the development of teacher candidates' web-based material design skills. After making their presentations, the teacher candidates were given feedback and this continued until the final presentation. Observations were also made by the researcher from the sixth week when the teacher candidates started their presentation to the last week. Between the sixth and tenth weeks, when teacher candidates started making their presentations, it was observed that they had sufficient knowledge about how to use web 2.0 tools in their materials in teaching Turkish. When the rubric scores were considered, it was seen that there was a development and improvement. In addition, it was observed by the researcher that teacher candidates were willing and motivated while preparing and presenting web-based materials, and that other teacher candidates willingly participated in the activities and had fun while the materials were implemented in the classroom. As a result, the study carried out had a positive impact on the development of Turkish teacher candidates' web-based material design skills. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.

To improve the elastic performance and deformation adaptability of seamless expansion joint materials at the joints of the subsea immersed tunnel under different temperature conditions, the representative asphalt and polyurethane seamless expansion joint materials are designed and their deformation properties are examined. According to the cyclic tensile and compressive deformation characteristics of seamless expansion joint materials under actual service conditions, the tensile, tensile recovery, and compression recovery tests are conducted under various temperature conditions, and the tensile, tensile recovery, and compression recovery properties of asphalt and polyurethane seamless expansion joint materials are analyzed. The results reveal the following: (1) The optimum oil to aggregate ratio of the these two kinds of materials is 24% . The oil to aggregate ratio will significantly affect the tensile properties and high temperature performance of asphalt materials, and the gradation has a significant effect on the tensile properties of polyurethane materials. (2 ) These two types of seamless expansion joint materials have excellent ability to adapt to tensile deformation, and the tensile strength of polyurethane materials is approximately 70% higher than that of asphalt materials. (3) In the temperature range of 20 to 40 °C, the tensile recovery rate of two kinds of seamless expansion joint materials is more than 70%, and the compression recovery rate is more than 60%, which can meet the requirements of expansion joint of immersed tube tunnel. (4) In the temperature range of 0 to 60 °C, the performance of asphalt materials is significantly affected by temperature, while polyurethane materials have lower temperature sensitivity. [ABSTRACT FROM AUTHOR]

Solar energy utilization is of great significance to current challenges of the energy crisis and environmental pollution, which benefit the development of the global community to achieve carbon neutrality goals. Hydrogen energy is also treated as a good candidate for future energy supply since its combustion not only supplies high‐density energy but also shows no pollution gas. In particular, photocatalytic water splitting has attracted increasing research as a promising method for H2 production. Recently, single‐atom (SA) photocatalysts have been proposed as a potential solution to improve catalytic efficiency and lower the costs of photocatalytic water splitting for H2 generation. Owing to the maximized atom utilization rate, abundant surface active sites, and tunable coordination environment, SA photocatalysts have achieved significant progress. This review reviews developments of advanced SA photocatalysts for H2 generation regarding the different support materials. The recent progress of titanium dioxide, metal–organic frameworks, two‐dimensional carbon materials, and red phosphorus supported SA photocatalysts are carefully discussed. In particular, the material designs, reaction mechanisms, modulation strategies, and perspectives are highlighted for realizing improved solar‐to‐energy efficiency and H2 generation rate. This work will supply significant references for future design and synthesis of advanced SA photocatalysts. [ABSTRACT FROM AUTHOR]

Deep learning (DL) methods combined with computational simulations have shown promise for novel material discovery and establishing structure–response relationships within extensive design spaces. However, obtaining sufficient simulation data for precise predictions on nonlinear responses remains challenging, limiting DL models' predictive capabilities for unexplored composite configurations. To address this, we introduce the hierarchical generative network (HGNet), comprising three customized convolutional neural networks (NNs). HGNet predicts stress fields and crack patterns during mechanical failure events, encompassing linear response regimes, ultimate strength, and complete fracture. By jointly training these networks within a unified DL model, predictive capacity significantly improves. HGNet is applied to a two‐phase composite optimization problem with vast design configurations, demonstrating accurate predictions of complex stress distributions and fracture patterns in unseen configurations. Moreover, it explores uncharted designs with superior stiffness and strength across vast design spaces using genetic algorithms. The methodology provides insight into the model's reasoning through visualizing the inference process. Overall, this approach represents a potent and versatile tool for accelerating material discovery, facilitating a more efficient and cost‐effective approach to finding novel materials, even with limited training data. [ABSTRACT FROM AUTHOR]

Controlling the chemical reaction rate concerning degradation and repair is found to be important to design advanced self-healing ceramics. The recovery and degradation behaviors of strength and stiffness were investigated by exposing aqueous solutions of different pH and calcium ion concentrations to the introduced crack on typical self-healing ceramics dispersed with alumina cement as a self-healing agent. The chemical reaction of cement undergoes the following three stages: dissolution of components such as calcium ions, formation of a gel, and formation of final products. Experimental and thermodynamic assessments revealed that even under conditions where the final products are identical (thermodynamic equilibrium), kinetic effects (excessive dissolution of components or insufficient crystal formation) result in strength degradation rather than repair. It was also suggested that the repair function could be enhanced by controlling the nucleation site of the crystals. [ABSTRACT FROM AUTHOR]

Abstract High energy‐conversion efficiency (ZT) of thermoelectric materials has been achieved in heavy metal chalcogenides, but the use of toxic Pb or Te is an obstacle for wide applications of thermoelectricity. Here, high ZT is demonstrated in toxic‐element free Ba3BO (B = Si and Ge) with inverse‐perovskite structure. The negatively charged B ion contributes to hole transport with long carrier life time, and their highly dispersive bands with multiple valley degeneracy realize both high p‐type electronic conductivity and high Seebeck coefficient, resulting in high power factor (PF). In addition, extremely low lattice thermal conductivities (κlat) 1.0–0.4 W m−1 K−1 at T = 300–600 K are observed in Ba3BO. Highly distorted O–Ba6 octahedral framework with weak ionic bonds between Ba with large mass and O provides low phonon velocities and strong phonon scattering in Ba3BO. As a consequence of high PF and low κlat, Ba3SiO (Ba3GeO) exhibits rather high ZT = 0.16–0.84 (0.35–0.65) at T = 300–623 K (300–523 K). Finally, based on first‐principles carrier and phonon transport calculations, maximum ZT is predicted to be 2.14 for Ba3SiO and 1.21 for Ba3GeO at T = 600 K by optimizing hole concentration. Present results propose that inverse‐perovskites would be a new platform of environmentally‐benign high‐ZT thermoelectric materials.

Abstract A family of solids including crystalline phase change materials such as GeTe and Sb2Te3, topological insulators like Bi2Se3, and halide perovskites such as CsPbI3 possesses an unconventional property portfolio that seems incompatible with ionic, metallic, or covalent bonding. Instead, evidence is found for a bonding mechanism characterized by half‐filled p‐bands and a competition between electron localization and delocalization. Different bonding concepts have recently been suggested based on quantum chemical bonding descriptors which either define the bonds in these solids as electron‐deficient (metavalent) or electron‐rich (hypervalent). This disagreement raises concerns about the accuracy of quantum–chemical bonding descriptors is showed. Here independent of the approach chosen, electron‐deficient bonds govern the materials mentioned above is showed. A detailed analysis of bonding in electron‐rich XeF2 and electron‐deficient GeTe shows that in both cases p‐electrons govern bonding, while s‐electrons only play a minor role. Yet, the properties of the electron‐deficient crystals are very different from molecular crystals of electron‐rich XeF2 or electron‐deficient B2H6. The unique properties of phase change materials and related solids can be attributed to an extended system of half‐filled bonds, providing further arguments as to why a distinct nomenclature such as metavalent bonding is adequate and appropriate for these solids.

EFL teachers’ professional development in Colombia has been characterized by top-down policies that neglect the regions in terms of contextualized professional development programs and resources. This article is an effort to make EFL teachers’ needs in the Eastern region of Antioquia, Colombia, visible for academic institutions and local authorities to propose coherent professional development programs. This report is based on a section of the findings of a larger study called “Diagnosis of the needs about plurilingualism in the regions of Antioquia”. Findings show that EFL teachers in the Eastern region claim for ongoing and contextualized professional development programs, differentiated for primary and secondary-school teachers, and focused on instructional material design and adaptation according to the resources available in the local communities. Conclusions from this study outline implications for educational institutions to support teachers in pursuing their professional development within their contexts, methodological limitations for researchers, and possibilities for further research for stakeholders in the territory.

Separation membranes, which have the possibility to develop energy-saving and compact processes, are expected to be applied in the field of CO2 separation and capture and are attracting considerable interest from both industries and academia. The purpose of this review is to provide an overview of recent advances in both organic and inorganic materials for high-performance CO2 separation membranes from the viewpoint of molecular design to an engineering perspective. The overview includes gas permeation theory for CO2 capture and separation, controlled intrinsic micropores and their size-sieving property, CO2 diffusivity in the membrane matrix, selective CO2 solubility and selective permeability, functionalization using chemical reactions, etc. CO2 separation mechanisms and the effects of properties of membrane materials on both the CO2 permeability and permselectivity over other light gases are discussed, and the recent developments regarding inorganic and glassy polymer membranes with intrinsic microporous structures, rubbery polymer membranes, ionic liquid-based gel membranes, and facilitated-transport membranes are reviewed. Taking into consideration the industrial applications, the characteristics of each membrane with respect to process design, such as membrane thickness, and physical aging, and dependence of CO2 permeability on gas properties, are also discussed. Finally, future research challenges and perspectives for the commercialization of CO2 capture and separation process using high-performance materials are proposed.

This study presents an early-stage design exploration of NRG-Foam, an innovative insulation material composed of cementitious foam doped with microencapsulated phase change materials (MPCMs). The study comprises the static part that utilizes life cycle assessment and life cycle costing assessment for getting insight into the impacts of the NRG-Foam production process and the dynamic part that identifies the trade-offs between performance characteristics of NRG-Foam using multi-objective optimization. The production of MPCMs was found to be a major contributor to environmental impacts while the addition of small amounts of reduced graphene oxide amplifies the impacts even further. The hot spot analysis pinpointed high electricity consumption as the main driver of environmental impacts. A multi-objective optimization analysis revealed trade-offs between performance characteristics, emphasizing the necessity of compromises during material development. The selection of the MPCM type was shown to be determinative of the final properties of NRG-Foam.

In this paper, we present compact and efficient MATLAB codes for NURBS-based IGA and material design using isogeometric topology optimization. We make full use of the advantages of MATLAB in the vector and matrix computation and calculate the span indexes of and NURBS basis functions at multi-parameters simultaneously, so that the efficiency in generating IGA element and global stiffness matrices can be increased significantly. Based on the presented IGA MATLAB codes, we apply the IGA-based SIMP topology optimization into material design with extreme properties, such as shear modulus maximization and negative Poisson's ratio, where the effective macroscopic elasticity tensor is calculated by the energy-based homogenization approach. Numerical examples demonstrate the efficiency of the presented codes in generating stiffness matrices and the effectiveness in material optimization design. All the codes are included as appendixes. [ABSTRACT FROM AUTHOR]

In this work, polymeric materials designed for enhanced oil recovery (EOR) were evaluated for their intended application. Properties including viscosity, flow through porous media (resistance factor and residual resistance factor), and heavy oil displacement (incremental oil recovery) were assessed for designed terpolymers of 2‐acrylamido‐2‐methylpropane sulphonic acid (AMPS), acrylamide (AAm), and acrylic acid (AAc). The same properties were evaluated for two commercially available reference materials (e.g., partially hydrolyzed polyacrylamides or HPAM) with similar characteristics, which allowed for direct comparison between the newly designed terpolymers and materials that are currently on the market for the polymer flooding application. The incremental oil recovery directly associated with polymer flooding, which includes both the polymer flooding and post‐polymer waterflooding stages (excluding the initial waterflooding injection (or secondary) oil recovery), demonstrates that the designed terpolymers provided a higher incremental recovery (42% and 58%) than the reference materials (33% and 46%). Therefore, the terpolymers provided a higher contribution to incremental (or enhanced) oil recovery than the typical HPAM. Additionally, both designed terpolymers showed better injectivity in unconsolidated porous media and are less likely to cause plugging than the commercially available reference materials. Therefore, using a targeted design approach ultimately led to polymeric materials with excellent performance for EOR polymer flooding applications. [ABSTRACT FROM AUTHOR]

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