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The use of artificial intelligence (AI) is revolutionizing many professions and research fields. Thus, the present study focuses on the implications that AI is having on research in materials science and engineering (MSE). To this end, a bibliometric review has been conducted to analyze the advances that AI is generating in MSE. Although expectations for AI advances in the field of MSE are high, the results of this study indicate that we are still at a preliminary stage of development. It is worth highlighting that despite the progress made, the potential of AI in MSE has not been fully exploited and numerous challenges remain to be overcome to achieve effective and widespread implementation. It should be noted that the subarea "Materials structure, processing, and properties" is the one that currently presents the largest number of research works linked to AI. It appears that the United States and China are currently the countries with the greatest involvement in the use of AI in the field of MSE. The emerging themes and thematic map of the topic are revealed, and future research directions are provided. [ABSTRACT FROM AUTHOR]

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]

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.

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]

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.)

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]

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.

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.

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]

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]

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]

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.

The use of artificial intelligence (AI) is revolutionizing many professions and research fields. Thus, the present study focuses on the implications that AI is having on research in materials science and engineering (MSE). To this end, a bibliometric review has been conducted to analyze the advances that AI is generating in MSE. Although expectations for AI advances in the field of MSE are high, the results of this study indicate that we are still at a preliminary stage of development. It is worth highlighting that despite the progress made, the potential of AI in MSE has not been fully exploited and numerous challenges remain to be overcome to achieve effective and widespread implementation. It should be noted that the subarea “Materials structure, processing, and properties” is the one that currently presents the largest number of research works linked to AI. It appears that the United States and China are currently the countries with the greatest involvement in the use of AI in the field of MSE. The emerging themes and thematic map of the topic are revealed, and future research directions are provided.

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]

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]

Abstract Following the Materials Genome Initiative project, materials research has embarked a new research paradigm centered around material repositories, significantly accelerating the discovery of novel materials, such as thermoelectrics. Thermoelectric materials, capable of directly converting heat into electricity, are garnering increasing attention in applications like waste heat recovery and refrigeration. To facilitate research in this emerging paradigm, we have established the Materials Hub with Three‐Dimensional Structures (MatHub‐3d) repository, which serves as the foundation for high‐throughput (HTP) calculations, property analysis, and the design of thermoelectric materials. In this review, we summarize recent advancements in thermoelectric materials powered by the MatHub‐3d, specifically HTP calculations of transport properties and material design on key factors. For HTP calculations, we develop the electrical transport package for HTP purpose, and utilize it for materials screening. In some works, we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results, enhancing the fundamental understanding about interested compounds. In our work associated with material design, we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses. Finally, we discuss the future developments of the MatHub‐3d and the evolving directions of database‐driven thermoelectric research.

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.

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.

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.

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.

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]

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]

Quantitative prediction on mesoscale crystal plasticity via first principle-based methods or first principle-based multiscale methods is still unreachable, even though several approaches have arisen throughout decades for developing atomic-informed models for modeling crystalline materials from first principles. Several major difficulties have prevented to first principle-based realistic prediction of mechanical material properties. For instance, the solution of numerical models is highly affected by the loading rate and the size of the model. In this work, we aim to develop a procedure that efficiently eliminates the loading rate and the mesh size effects. This is achieved by first using machine learning (ML)-aided constitutive relations extrapolated from the first-principle-based modeling for the finite element solver, and then assessing the stress–strain relation at the mesoscale based on the homogenized strain field but utilizing the same atomistic or lattice information. As it may compromise the consistency at the finite element level, yet the calculations are based on the same atomistic potential, but performed independently. This can be interpreted as using the FE as an auxiliary tool for calculating the deformations field and achieving homogenization, while the stresses are computed from the atomic-based potential. It is shown herein that the proposed ML-based multiscale method significantly enhances the accuracy of the homogenization results comparing with other multiscale methods reported in the literature. [ABSTRACT FROM AUTHOR]

Copyright of HOW: A Colombian Journal for Teachers of English is the property of Asociacion Colombiana de Profesores de Ingles 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.)

[Display omitted] Triboelectric nanogenerators (TENGs), as a new energy conversion device in self-powered sensing devices, have been used at extreme environmental conditions, which poses great challenges to the structural stability and chemical tolerance of triboelectric materials. However, the low tolerance characteristics of traditional triboelectric materials limit their expansion in emerging applications. Cellulose, with its unique multidimensional structure and, controlled surface chemistry regulation advantages, shows great potential in resisting extreme environmental conditions and is emerging as a new candidate. This review aims to design and fabricate cellulosic triboelectric materials with exceptional tolerance, providing a perspective on the emerging applications of TENGs at extreme environmental conditions. First, the performance advantages and design principles of action of cellulosic triboelectric materials at extreme environmental conditions are described. Second, the importance of physical and chemical regulation strategies is discussed through the multi-scale perspective from macroscopic fiber bundles to microscopic cellulose molecules. It also presents the latest applications of cellulosic triboelectric materials at extreme environmental conditions such as wide temperature, high humidity, high corrosion, and radiation. Finally, the idea of how cellulosic triboelectric materials can further expand the development of TENGs application areas is presented. [ABSTRACT FROM AUTHOR]

Nucleic acid delivery provides effective options to control intracellular gene expression and protein production. Efficient delivery of nucleic acid typically requires delivery vehicles to facilitate the entry of nucleic acid into cells. Among non-viral delivery vehicles, cationic materials are favored because of their high loading capacity of nucleic acids and prominent cellular uptake efficiency through electrostatic interaction. However, cationic moieties at high dosage tend to induce severe cytotoxicity due to the interference on cell membrane integrity. In contrast, non-cationic materials present alternative delivery approaches with less safety concerns than cationic materials. In this Progress Report, principles of non-cationic material design for nucleic acid delivery are discussed. Examples of such non-cationic platforms are highlighted, including complexation or conjugation with nucleic acids and self-assembled nucleic acid structures.

The continuous development of mobile platforms provides the opportunity to integrate and improve existing applications or to introduce new features to make life better. The purpose of this paper is to investigate the use of mobile platforms in civilization (museums) and present the design and the implementation of a mobile application that satisfies various design criteria. Through this application the visitor can navigate and tour virtually in the museum through a smartphone. In addition, features have been included in the application that make it easier for a user to visit the museum. First, we present the operating parameters and the aesthetic presentation of the application, which delimits usability and ease of access through the interfaces of a smartphone. Then we highlight the cloud features that were exploited in the application. Then, an extended evaluation of the mobile application is presented, that proves its high applicability and user acceptability.

Thermal conductivity, one of critical properties for composite materials, constitutes an indispensable part of high-efficient materials design. However, a quantitative model to describe the thermal conductivity of two-phase composites has yet to be developed. In this work, we proposed an effective evaluation framework of a microstructure-based thermal conductivity model with key inputs from CALPHAD-type approach. Two-phase WC-Ni composites with Ni–W solid-solution matrix were used as the target system to validate this framework combined with key thermal conductivity measurements. Calculated thermal conductivities agree well with experimental results within the experimental error of 10%. Applying this well-established framework, thermal conductivities depending on wide ranges of temperature, WC grain size (dWC), and phase constitution are predicted, suggesting some unique features. The thermal conductivity becomes less dependent on dWC when dWC is >3 μm. Additionally, the thermal conductivity of nano-sized WC-Ni shows a quite different temperature-dependent variation tendency in contrast to that of micron-sized WC-Ni. These correlations are found and quantitatively described in the present work. Furthermore, the thermal conductivity for WC-Ni carbide rolls is predicted by the established framework, demonstrating a high accuracy. The present work is expected to guide the design of composite materials with desirable thermal properties efficiently.

A timely review is presented on artificial intelligence (AI) and, more specifically, deep learning, which is a subfield of machine learning (ML), applied to the design and behaviour of modern composite materials systems. The use of composites is increasing due to their high specific strength and stiffness, which make them comparable to metals, and their tunable properties that can be altered to produce lightweight materials with efficient structural configurations. Recent studies are examined and discussed, wherein computational tools have been developed that mimic human brain activity to answer questions and solve challenging problems toward characterizing materials behaviour and improving the performance of materials with less effort and cost. The attractiveness of AI comes from its self-learning capability, the faster computer processing time of large datasets, and the potential to yield highly accurate results. However, as a data-driven method, the quantity and quality of data largely affect the accuracy of ML in addition to the need for well-designed AI algorithms and virtual reality models, hence the need to continue the research efforts in this area.

In molecular, material, and process design, Gaussian mixture regression (GMR) can directly predict values of explanatory variables x such as experimental and process conditions from target values of objective variables y such as properties and activities of materials. In GMR, a prediction result of a Gaussian mixture distributions is provided as a probability density distribution. The weighted average of the means of Gaussians has been used as the representative value of Gaussian mixture. In this study, true GMR has been proposed, where the predicted value of the GMR model is calculated by solving a continuous optimization problem to obtain the maximum value of the probability density function (PDF). As the continuous optimization problem cannot be solved when variables contain discrete values, a genetic algorithm is used to search for a solution that satisfies the constraints, while maximizing the value of the PDF. Numerical simulations using nonlinear functions between y and x, and the case studies using real material datasets confirmed that the proposed methods produced solutions with drastically higher values of the PDF than the traditional methods, especially when x is constrained in the inverse analysis. The Python codes of the proposed methods are freely available. Direct inverse analysis with GMR using the proposed methods is expected to improve the efficiency of molecular, material, and process design.

The article aims to read the transition of material design approaches identifying an evolutive models with major principles and contribution into the health improvement through material innovations. The analysis follows a bottom-up approach, using a case-based reasoning methodology to study well-being concept evolution and identify new material experimentations frontiers within three approaches: Imitative, Augmentative, and Mutational. The contribution of the article reflects and suggests the introduction of new material design approaches to manage the design of advanced materials' behaviuors in their future development, highlighting their potential impact on user satisfaction and acceptance, collective welfare improvement, performance optimization, and environmental concerns. [ABSTRACT FROM AUTHOR]

The rational design of molecularly imprinted polymers has evolved along with state-of-the-art experimental imprinting strategies taking advantage of sophisticated computational tools. In silico methods enable the screening and simulation of innovative polymerization components and conditions superseding conventional formulations. The combined use of quantum mechanics, molecular mechanics, and molecular dynamics strategies allows for macromolecular modelling to study the systematic translation from the pre- to the post-polymerization stage. However, predictive design and high-performance computing to advance MIP development are neither fully explored nor practiced comprehensively on a routine basis to date. In this review, we focus on different steps along the molecular imprinting process and discuss appropriate computational methods that may assist in optimizing the associated experimental strategies. We discuss the potential, challenges, and limitations of computational approaches including ML/AI and present perspectives that may guide next-generation rational MIP design for accelerating the discovery of innovative molecularly templated materials. [ABSTRACT FROM AUTHOR]

Biobased composites offer unique properties in the context of sustainable material production as well as end-of-life disposal, which places them as viable alternatives to fossil-fuel-based materials. However, the large-scale application of these materials in product design is hindered by their perceptual handicaps and understanding the mechanism of biobased composite perception, and its constituents could pave the way to creating commercially successful biobased composites. This study examines the role of bimodal (visual and tactile) sensory evaluation in the formation of biobased composite perception through the Semantic Differential method. It is observed that the biobased composites could be grouped into different clusters based on the dominance and interplay of various senses in perception forming. Attributes such as Natural, Beautiful, and Valuable are seen to correlate with each other positively and are influenced by both visual and tactile characteristics of the biobased composites. Attributes such as Complex, Interesting, and Unusual are also positively correlated but dominated by visual stimuli. The perceptual relationships and components of beauty, naturality, and value and their constituent attributes are identified, along with the visual and tactile characteristics that influence these assessments. Material design leveraging these biobased composite characteristics could lead to the creation of sustainable materials that would be more attractive to designers and consumers. [ABSTRACT FROM AUTHOR]

High-entropy oxides (HEOs), with their multi-principal-element compositional diversity, have emerged as promising candidates in the realm of energy materials. This review encapsulates the progress in harnessing HEOs for energy conversion and storage applications, encompassing solar cells, electrocatalysis, photocatalysis, lithium-ion batteries, and solid oxide fuel cells. The critical role of theoretical calculations and simulations is underscored, highlighting their contribution to elucidating material stability, deciphering structure-activity relationships, and enabling performance optimization. These computational tools have been instrumental in multi-scale modeling, high-throughput screening, and integrating artificial intelligence for material design. Despite their promise, challenges such as fabrication complexity, cost, and theoretical computational hurdles impede the broad application of HEOs. To address these, this review delineates future research perspectives. These include the innovation of cost-effective synthesis strategies, employment of in situ characterization for micro-chemical insights, exploration of unique physical phenomena to refine performance, and enhancement of computational models for precise structure-performance predictions. This review calls for interdisciplinary synergy, fostering a collaborative approach between materials science, chemistry, physics, and related disciplines. Collectively, these efforts are poised to propel HEOs towards commercial viability in the new energy technologies, heralding innovative solutions to pressing energy and environmental challenges.

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

Abstract Simultaneously improving the energy density and power density of electrochemical energy storage systems is the ultimate goal of electrochemical energy storage technology. An effective strategy to achieve this goal is to take advantage of the high capacity and rapid kinetics of electrochemical proton storage to break through the power limit of batteries and the energy limit of capacitors. This article aims to review the research progress on the physicochemical properties, electrochemical performance, and reaction mechanisms of electrode materials for electrochemical proton storage. According to the different charge storage mechanisms, the surface redox, intercalation, and conversion materials are classified and introduced in detail, where the influence of crystal water and other nanostructures on the migration kinetics of protons is clarified. Several reported advanced full cell devices are summarized to promote the commercialization of electrochemical proton storage. Finally, this review provides a framework for research directions of charge storage mechanism, basic principles of material structure design, construction strategies of full cell device, and goals of practical application for electrochemical proton storage.

The problem of ballast water is caused by microorganisms. The solution to solve this problem is ballast water disinfection. Until the recent day, there have not been many practical solutions. Usually, ozone, chlorine, and UV treatments were used for disinfection. However, these methods still have many weaknesses in treating ballast water.

[Display omitted] Due to structural tunability, high surface area, abundant pore structures, and abundant active sites, covalent heptazine frameworks (CHFs) constructed from heptazine and other molecular blocks are especially prominent. Here, we proposed a reaction-dependent strategy for designing two dimensional CHFs including high-throughput precursors screening, structure generation, and performance evaluation. Assuming that oxamide-like precursors can undergo the same thermal polymerization reaction as producing C 6 N 7 , seven precursors were screened from more than 109 molecules in the ZINC20 database in terms of molecular weight, number of substructures, shape index, and symmetry. Furthermore, CHF-L1 to CHF-L7 were constructed from urea and the seven precursors according to the topologically assembling scheme in thermal polymerization. The designed CHFs had band gaps ranging from 1.89 to 3.10 eV. Among them, CHF-L3 assembled structurally by urea and 1,2,4,5-tetrazine-3,6-dicarboxamide with the smallest bandgap and an oxidative potential bias of 1.38 V for oxygen evolution reaction was screened as the candidate with high oxidative ability. The negative formation energy based on the synthesis route indicated the synthetic feasibility of CHF-L3, and negative cohesive energy as well as the stable structure under ab initio molecular dynamics simulations confirmed the stability of CHF-L3. The present work is expected to provide a powerful design strategy for two-dimensional CHFs design and is broadly applicable to various computational covalent organic framework design systems and experimental studies. [ABSTRACT FROM AUTHOR]