Search

Energy-efficient non-fullerene acceptors attracting great attention for developing efficient organic solar cells (OSCs). Though many materials have been developed to improve the optical and optoelectronic characteristics of OSCs, the search continues to strengthen this field further. Therefore, herein, we designed an environmentally-benign indacenodifuran-based electron acceptor molecules (MH1-MH8) by substituting various end-capped electron-withdrawing moieties (COOH, SO3H, NO2, and CN). The open-circuit-voltages, binding energy, transition energy, transition density analysis, and electron and hole reorganization energies for MH1–MH8 were computed for these materials. These designed materials MH1-MH8 have better photovoltaic, photophysical, and electrical properties than R due to their narrower bandgap (1.91 eV), higher absorption (725.56 and 785.46 nm in gas and chloroform), low-mobility of electrons (0.0033) and holes (0.0019), and lower binding energy of 0.20 eV). We have also performed a charge transfer study by establishing a donor:acceptor complex MH2:PTB7-TH, showing a great charge transformation at the donor:acceptor interface. Thus, the designed compounds (MH1-MH8) with excellent optoelectronic properties could be considered a promising and environmentally friendly option to create compelling organic solar cells. [ABSTRACT FROM AUTHOR]

This paper reports on a study that examines how a school teacher, a university researcher, and a professional textbook writer interact when they collaborate in designing materials for English language teaching. Data were collected from interviews with the participants and audio recordings of their interaction in collaborative design supplemented with drafts of the designed materials. The findings showed that the three participants placed different emphases on the design of the teaching materials due to their individual principles, values, and approaches towards English language learning and teaching and materials design. Their values and beliefs were found to inform their practices in collaborative materials design and thus influenced the processes of interaction and negotiation. The study contributes to our understanding of the personalized and diversified principles and beliefs of the stakeholders regarding the development of teaching materials, and suggests that textbook writers and their publishers need to pay more heed to the parties for whom they are producing their materials. [ABSTRACT FROM AUTHOR]

Given the vast compositional possibilities NanMmMm′′Si3−p−aPpAsaO12$\left(\text{Na}\right)_{n} \left(\text{M}\right)_{\text{m}} \text{M}_{m^{&aposx;}}^{&aposx;} \left(\text{Si}\right)_{3 - \text{p} - \text{a}} \left(\text{P}\right)_{\text{p}} \left(\text{As}\right)_{\text{a}} \left(\text{O}\right)_{12}$, Na‐ion superionic conductors are attractive but complicated for designing materials with enhanced room‐temperature Na‐ion conductivity σNa,300 K$\left(\sigma\right)_{\text{Na} , 300 \text{K}}$. An explicit regression model for σNa,300 K$\left(\sigma\right)_{\text{Na} , 300 \text{K}}$ with easily‐accessible descriptors is proposed by exploiting density functional theory molecular dynamics (DFT‐MD). Initially, it is demonstrated that two primary descriptors, the bottleneck width along Na‐ion diffusion paths d1$d_{1}$ and the average Na–Na distance ⟨dNa−Na⟩$$ <{d}_{\text{Na}-\text{Na}}>$$, modulate room‐temperature Na‐ion self‐diffusion coefficient DNa,300 K$D_{\text{Na} , 300 \text{K}}$. Then, two secondary easily‐accessible descriptors are introduced: Na‐ion content n, which influences d1$d_{1}$, ⟨dNa−Na⟩$$ <{d}_{\text{Na}-\text{Na}}>$$, and Na‐ion density ρNa$\left(\rho\right)_{\text{Na}}$; and the average ionic radius ⟨rM⟩$$ <{r}_{\text{M}}>$$ of metal ions, which impacts d1$d_{1}$ and ⟨dNa−Na⟩$$ <{d}_{\text{Na}-\text{Na}}>$$. These secondary descriptors enable the development of a regression model for σNa,300 K$\left(\sigma\right)_{\text{Na} , 300 \text{K}}$ with n and ⟨rM⟩$$ <{r}_{\text{M}}>$$ only. Subsequently, this model identifies a promising yet unexplored stable composition, Na2.75Zr1.75Nb0.25Si2PO12$\left(\text{Na}\right)_{2.75} \left(\text{Zr}\right)_{1.75} \left(\text{Nb}\right)_{0.25} \left(\text{Si}\right)_{2} \left(\text{PO}\right)_{12}$, which, upon DFT‐MD calculations, indeed exhibits σNa,300 K>10−3$\left(\sigma\right)_{\text{Na} , 300 \text{K}}> \left(10\right)^{- 3}$ S cm−1. Furthermore, the adjusted version effectively fits 140$140$ experimental values with R2=0.718$R^{2} = 0.718$. [ABSTRACT FROM AUTHOR]

Metal foils are being widely used, from the chemical or electronics sector to household appliances. The joining of these foils by adhesive bonding is often the preferred method due to discolouring and warping under the thermal stresses of other joining methods, such as welding. However, long curing times are a disadvantage of adhesive bonding compared to welding. The use of electromagnetic induction is a promising solution for accelerated curing. This work investigates induction heating for accelerated curing of 1-C epoxy adhesives for bonding of thin nickel foils. Process parameters for rapid curing of the adhesives were determined based on reaction kinetics using differential scanning calorimetry measurements. According to those results peel test specimens were fabricated, and the peel resistance was evaluated using a 90° peel load. [ABSTRACT FROM AUTHOR]

Introduction: Textbook evaluation plays a crucial role in enhancing language education by ensuring instructional materials meet learners' needs. Hence, this study examines the "Mega Goal" textbook series used in English as a Foreign Language (EFL) education through the evaluation of 773 male EFL teachers selected from Saudi Arabia's secondary schools. Methods: A quantitative approach was adopted using a descriptive survey. A structured questionnaire collected via Google Forms was employed to obtain the teachers' assessment of the series. The resultant data were systematically analyzed employing descriptive statistics to present the teachers' perspectives across multiple domains. The dataset explored practical factors, language content, layout and structure, language abilities, tasks and exercises, and cultural elements. Techniques like Kruskal-Wallis tests were also used to examine differences in evaluations according to teachers' qualifications, experience, and region. Results: Findings indicated that the series' external aspects such as structure and arrangement garnered positive reviews, with sufficient scores given to pragmatic factors. Internally, the series exhibited proficiency in its linguistic components due to the use of authentic language, though some teachers reported issues with the provided grammatical content. The textbook's language capabilities received a fair rating, and the "practices and ventures" domain exhibited strong positive feedback. The cultural elements, while deemed favorable, were recommended to be depicted more realistically. Overall evaluation insinuated that the textbook has evident usability and generalizability with opportunities for improvement in adaptability and flexibility. Discussion: Key findings suggest a need for greater interactivity and appropriate activities for differing student proficiency levels to stimulate higher cognitive skills and accommodate various learning capabilities. Consequently, this study offers significant insights into the effectiveness of the "Mega Goal" series and provides valuable information for future textbook evaluations, ultimately promoting continuous improvement in English language instruction in Saudi secondary schools. [ABSTRACT FROM AUTHOR]

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. In this paper, recent advances in data‐driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data‐driven processes. It is demonstrated how data‐driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data‐driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments. [ABSTRACT FROM AUTHOR]

Realization of the full potential of single‐atom photoelectrocatalysts in sustainable energy generation requires careful consideration of the design of the host material. Here, a comprehensive methodology for the rational design of photoelectrocatalysts using anodic titanium dioxide (TiO2) nanofilm as a model platform is presented. The properties of these nanofilms are precisely engineered to elucidate synergies across structural, chemical, optoelectronic, and electrochemical properties to maximize the efficiency of the hydrogen evolution reaction (HER). These findings clearly demonstrate that thicker TiO2 nanofilms in anatase phase with pits on the surface can accommodate single‐atom platinum catalysts in an optimal configuration to increase HER performance. It is also evident that the electrolyte temperature can further enhance HER output through thermochemical effect. A judicious design incorporating all these factors into one system gives rise to a ten‐fold HER enhancement. However, the reusability of the host photoelectrocatalyst is limited by the leaching of the Pt atom, worsening HER. Density‐functional theory calculations have provided insights into the mechanism underlying the experimental observations in terms of moderate hydrogen adsorption and enhanced gas generation. This improved understanding of the critical factors determining HER performance in a model photoelectrocatalyst paves the way for future advances in scalable and translatable photoelectrocatalyst technologies. [ABSTRACT FROM AUTHOR]

The metal‐free rocking chair type Zn‐ion batteries (ZIBs) provide a promising approach toward the promotion of the Zn‐based batteries by circumventing the challenges including dendrite growth, hydrogen evolution reaction (HER), and surface corrosion. In order to sufficiently exploit the available capacity of this metal‐free batteries, it is necessary to effectively enhance the sluggish reaction kinetics of divalent zinc ions. Equally important is to achieve a balance in the kinetics between cathode and anode. Here, hetero‐valent doping and oxygen vacancy engineering are employed to effectively enhance the reaction dynamics of V2O5 cathodes and MoO3 anodes. Moreover, to the best of the knowledge, for the first time, the strategy of kinetics matching between the two electrodes is applied to the construction of rocking‐chair zinc ion batteries, enabling the cathode and anode to share similar zinc ion migration rates, and achieving a high energy density of up to 58.7 Wh kg−1 (based on the total electrode mass) as well as excellent cycling stability (90% after 500 cycles). This work demonstrates the importance of kinetics matching in zinc‐ion full‐cell performance and pave a benefitable avenue to for the pursuit of advanced multi‐valent metal‐ion batteries. [ABSTRACT FROM AUTHOR]

The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb0.35Ta0.23Ti0.42N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb0.55Ti0.45N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions. [ABSTRACT FROM AUTHOR]

Wear continuously occurs in components of centrifugal pumps installed in thermal power plants. Various components of ash disposal systems such as a slurry pump is deteriorated due to the mechanical action of flowing media hence their life minimizes. So, the protection or maintenance of the ash disposal system becomes essential after an uncertain period. Maintenance generally increases the life span of machinery for a small time but requires additional cost and work downtime. Therefore, a necessary is there to govern the wear phenomenon by optimizing the effect of influencing parameters. In this context, this paper presents an overview of various testing facilities developed for the development of novel numerical models for erosion assessment. The literature survey presents a general introduction to various aspects of surface erosion in centrifugal pumps. This paper also covers the types of wear, mechanisms of erosion wear, and different techniques to minimize the surface erosion wear of different pump materials. This paper also comprises the literature about different erosion wear testing methods, parameters affecting erosion wear, different types of bench scale test rigs, and different thermal spraying processes. This study could be beneficial in the prediction as well as prevention of erosion in the slurry pump of an ash disposal system. [ABSTRACT FROM AUTHOR]

Among a promising photovoltaic technology for solar energy conversion, organic solar cells (OSCs) have been paid much attention, of which the power conversion efficiencies (PCEs) have rapidly surpassed over 20%, approaching the threshold for potential applications. However, the device stability of OSCs including storage stability, photostability and thermal stability, remains to be an enormous challenge when faced with practical applications. The major causes of device instability are rooted in the poor inherent properties of light‐harvesting materials, metastable morphology, interfacial reactions and highly sensitive to external stresses. To get rid of these flaws, a comprehensive review is provided about recent strategies and methods for improving the device stability from active layers, interfacial layers, device engineering and encapsulation techniques for high‐performance OSC devices. In the end, prospectives for the next stage development of high‐performance devices with satisfactory long‐term stability are afforded for the solar community. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning-driven efficient screening of energy materials is proposed. • Several novel descriptors are proposed to reduce required training data. • The GBRT algorithm exhibits superior reliability for predicting interface energy. • This work provides new perspectives for fast screening novel functional interfaces. Traditional trial–error approach severely limits and restricts rapid development of high-performance anode and electrolytes materials, searching huge parameters space of various anode-solid electrolyte interfaces in an effective and efficient way is the key issue. Here, a novel computational strategy combining machine learning and first-principles is proposed to achieve efficient high-throughput screening of oxides and sulfides electrolytes for highly stable silicon oxycarbide all-solid-state batteries. First-principles calculations demonstrate significant compact of material type and elemental doping on interfacial compatibility between silicon oxycarbide and various electrolytes. By proposing several novel descriptors including interfacial adhesion and formation energies of frozen system with low computation cost, the amounts of demanded trainings data are significantly reduced. Gradient-boosted regression tree model shows low mean absolute errors of 0.09 and high R2 value of 0.99 for the prediction of interface formation energy, demonstrating ultrahigh accuracy and reliability of the algorithm. The present work discovers a series of uninvestigated stable anode-solid electrolytes interfacial couples for further experimental preparation. [ABSTRACT FROM AUTHOR]

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

Disentangling nucleation and growth in materials that crystallize on the nanosecond time scale is experimentally quite challenging since the relevant processes also take place on very small, i.e., sub‐micrometer length scales. Phase change materials are bad glass formers, which often crystallize rapidly. Here systematic changes in crystallization kinetics are shown in pseudo‐binary compounds of GeTe and Sb2Te3 and related solids subjected to short laser pulses. Upon systematic changes in stoichiometry, the speed of crystallization changes by three orders of magnitude concomitantly with pronounced changes in stochasticity. Resolving individual grains with electron backscatter diffraction (EBSD) permits to disentangle of the process of nucleation and growth. From these experiments, supported by multiphysics simulations of crystallization, it can be concluded that high crystallization speeds with small stochasticity characterize phase change materials with fast nucleation, while compounds that nucleate slowly crystallize much more stochastically. [ABSTRACT FROM AUTHOR]

Monocrystalline SnSe is one of the most promising thermoelectric materials with outstanding performance and a high abundance of constituting elements. However, polycrystalline SnSe, which is more robust for applications, only shows large figure‐of‐merit (zT) values in its high‐symmetry phase. Stabilizing the high‐symmetry phase at low temperatures can thus enhance the average zT value over a broad temperature range. In this work, the high‐symmetry rock‐salt SnSe phase is successfully obtained by alloying SnSe with AgVVI2 compounds (V = Sb, Bi; VI = Se, Te). These cubic SnSe phases show a unique portfolio of properties including a high optical dielectric constant, a large maximum of optical absorption, a large Born effective charge, and abnormal bond‐breaking behavior in laser‐assisted atom probe tomography. All of these characteristics are indicative of metavalent bonding. In contrast, the Pnma phase of SnSe employs covalent bonding. The enhanced symmetry at low temperatures is realized by tailoring chemical bonding. Concomitantly, zT near room temperature is increased by a factor of more than 10 from the pristine Pnma SnSe to Fm3¯${{\bar{3}}}$m SnSe alloys. This provides insights into the enhancement of the thermoelectric performance of SnSe and other chalcogenides over a broad temperature range by manipulating the chemical bonds. [ABSTRACT FROM AUTHOR]

Improving the thermal conductivities and bending strengths of ceramic substrates is crucial for their applicability as key components of integrated chips. Herein, we report the latest study in which the thermal conductivity and bending strength of aluminum nitride (AlN) ceramics were systematically predicted using high-precision prediction model approached with a machine learning (ML) method based on extreme gradient enhancement method (XGBoost). The ML model was used to rank the effects of the process parameters, and SHapley Additive exPlanations (SHAP) was employed to quantify the contributions of different factors. Sintering additives were found to have the predominant influence on the thermal conductivity and bending strength; particularly, those with cationic radii within 0.085–0.105 nm enhance both properties. By using Pr 2 O 3 as the sintering additive and adopting the preparation conditions recommended by the ML model, we prepared AlN ceramics with thermal conductivity as high as 195.63 W m−1 K−1 and bending strength of 371.60 ± 9.31 MPa, thus satisfying the application requirement of high thermal conductivity. The proposed model is also applicable to alumina and silicon nitride ceramics. This study provides a practicable and once-for-all strategy to realize the entire process from the ML prediction design to the preparation of ceramic materials exhibiting superior thermal conductivity and high strength. [ABSTRACT FROM AUTHOR]

Given the vast compositional possibilities NanMmMm′′Si3−p−aPpAsaO12, Na‐ion superionic conductors are attractive but complicated for designing materials with enhanced room‐temperature Na‐ion conductivity σNa,300 K. An explicit regression model for σNa,300 K with easily‐accessible descriptors is proposed by exploiting density functional theory molecular dynamics (DFT‐MD). Initially, it is demonstrated that two primary descriptors, the bottleneck width along Na‐ion diffusion paths d1 and the average Na–Na distance ⟨dNa−Na⟩, modulate room‐temperature Na‐ion self‐diffusion coefficient DNa,300 K. Then, two secondary easily‐accessible descriptors are introduced: Na‐ion content n, which influences d1, ⟨dNa−Na⟩, and Na‐ion density ρNa; and the average ionic radius ⟨rM⟩ of metal ions, which impacts d1 and ⟨dNa−Na⟩. These secondary descriptors enable the development of a regression model for σNa,300 K with n and ⟨rM⟩ only. Subsequently, this model identifies a promising yet unexplored stable composition, Na2.75Zr1.75Nb0.25Si2PO12, which, upon DFT‐MD calculations, indeed exhibits σNa,300 K>10−3 S cm−1. Furthermore, the adjusted version effectively fits 140 experimental values with R2=0.718.

Abstract Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. In this paper, recent advances in data‐driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data‐driven processes. It is demonstrated how data‐driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data‐driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments.

IntroductionTextbook evaluation plays a crucial role in enhancing language education by ensuring instructional materials meet learners’ needs. Hence, this study examines the “Mega Goal” textbook series used in English as a Foreign Language (EFL) education through the evaluation of 773 male EFL teachers selected from Saudi Arabia’s secondary schools.MethodsA quantitative approach was adopted using a descriptive survey. A structured questionnaire collected via Google Forms was employed to obtain the teachers’ assessment of the series. The resultant data were systematically analyzed employing descriptive statistics to present the teachers’ perspectives across multiple domains. The dataset explored practical factors, language content, layout and structure, language abilities, tasks and exercises, and cultural elements. Techniques like Kruskal-Wallis tests were also used to examine differences in evaluations according to teachers’ qualifications, experience, and region.ResultsFindings indicated that the series’ external aspects such as structure and arrangement garnered positive reviews, with sufficient scores given to pragmatic factors. Internally, the series exhibited proficiency in its linguistic components due to the use of authentic language, though some teachers reported issues with the provided grammatical content. The textbook’s language capabilities received a fair rating, and the “practices and ventures” domain exhibited strong positive feedback. The cultural elements, while deemed favorable, were recommended to be depicted more realistically. Overall evaluation insinuated that the textbook has evident usability and generalizability with opportunities for improvement in adaptability and flexibility.DiscussionKey findings suggest a need for greater interactivity and appropriate activities for differing student proficiency levels to stimulate higher cognitive skills and accommodate various learning capabilities. Consequently, this study offers significant insights into the effectiveness of the “Mega Goal” series and provides valuable information for future textbook evaluations, ultimately promoting continuous improvement in English language instruction in Saudi secondary schools.

Abstract Realization of the full potential of single‐atom photoelectrocatalysts in sustainable energy generation requires careful consideration of the design of the host material. Here, a comprehensive methodology for the rational design of photoelectrocatalysts using anodic titanium dioxide (TiO2) nanofilm as a model platform is presented. The properties of these nanofilms are precisely engineered to elucidate synergies across structural, chemical, optoelectronic, and electrochemical properties to maximize the efficiency of the hydrogen evolution reaction (HER). These findings clearly demonstrate that thicker TiO2 nanofilms in anatase phase with pits on the surface can accommodate single‐atom platinum catalysts in an optimal configuration to increase HER performance. It is also evident that the electrolyte temperature can further enhance HER output through thermochemical effect. A judicious design incorporating all these factors into one system gives rise to a ten‐fold HER enhancement. However, the reusability of the host photoelectrocatalyst is limited by the leaching of the Pt atom, worsening HER. Density‐functional theory calculations have provided insights into the mechanism underlying the experimental observations in terms of moderate hydrogen adsorption and enhanced gas generation. This improved understanding of the critical factors determining HER performance in a model photoelectrocatalyst paves the way for future advances in scalable and translatable photoelectrocatalyst technologies.

Summary: In situ liquid-phase electrochemical transmission electron microscopy (ec-TEM) as a valuable technique has been widely used in studying metal deposition in battery materials. While real-time observations of metallic nucleation, growth, and dendrite formation using microscale ec-TEM liquid cells are investigated, existing cells exhibit nonuniform electric field distribution along electrodes, limiting measurement reliability and quantitative analysis. Here, we introduce an advanced electrode design for ec-TEM chips, ensuring a uniform electric field for precise characterization of early-stage metal deposition closer to practical battery conditions. Both simulation and experimental investigations demonstrate that these specially designed ec-TEM chips facilitate quantitative electrochemical characterization combined with the in situ TEM technique in comparison with commercially available chips. We thus provide a significant progression toward optimizing the performance and reliability of quantitative in situ liquid-phase TEM measurements, essential for understanding and improving electrochemical systems.

Abstract Among a promising photovoltaic technology for solar energy conversion, organic solar cells (OSCs) have been paid much attention, of which the power conversion efficiencies (PCEs) have rapidly surpassed over 20%, approaching the threshold for potential applications. However, the device stability of OSCs including storage stability, photostability and thermal stability, remains to be an enormous challenge when faced with practical applications. The major causes of device instability are rooted in the poor inherent properties of light‐harvesting materials, metastable morphology, interfacial reactions and highly sensitive to external stresses. To get rid of these flaws, a comprehensive review is provided about recent strategies and methods for improving the device stability from active layers, interfacial layers, device engineering and encapsulation techniques for high‐performance OSC devices. In the end, prospectives for the next stage development of high‐performance devices with satisfactory long‐term stability are afforded for the solar community.

Zwitterions (ZIs), which are molecules bearing an equal number of positive and negative charges and typically possessing large dipole moments, can play an important role in improving the characteristics of a wide variety of novel battery electrolytes. Significant Coulombic interactions among ZI charged groups and any mobile ions present can lead to several beneficial phenomena within electrolytes, such as increased salt dissociation, the formation of ordered pathways for ion transport, and enhanced mechanical robustness. In some cases, ZI additives can also boost electrochemical stability at the electrolyte/electrode interface and enable longer battery cycling. Here, a brief summary of selected key historical and recent advances in the use of ZI materials to enrich the performance of three distinct classes of battery electrolytes is presented. These include: ionic liquid‐based, conventional solvent‐based, and solid matrix‐based (non‐ceramic) electrolytes. Exploring a greater chemical diversity of ZI types and electrolyte pairings will likely lead to more discoveries that can empower next‐generation battery designs in the years to come. [ABSTRACT FROM AUTHOR]

As additive manufacturing becomes more efficient, many opportunities arise where conventionally manufactured structures can be replaced by 3D printing systems with application-specific properties. In protective helmets, an impact is usually dampened by a foamed inlet that cannot be used again once subjected to a crash. While ranges of the foam microstructure distributions can be influenced by the manufacturing process, precise tailoring of the microstructure is unfeasible. In contrast, additive manufacturing enables the use of cellular structures with enhanced and tuned properties. In this study, the focus is on multi-stable metamaterials as these structures can achieve energy absorption through significant geometrical changes. High non-destructive energy absorption can be realised as the main energy absorption is provoked by elastic buckling, which may allow the helmet to be re-used. The properties highly depend on geometrical parameters. Instead of a foam inlay, we propose multi-stable hexagonal prisms for helmet applications. There, multi-stable elements with a specific shape are arranged in a hexagonal shape. Different configurations of the structures were designed and simulated to investigate the influence of geometric parameters on the multi-stable behaviour and energy absorption properties. The finite element simulations showed that the properties are highly adaptable. Tailoring and optimising the cell parameters can make the proposed structures well-suited for energy absorption in helmet applications. [ABSTRACT FROM AUTHOR]

The glass-forming ability (GFA) is of great significance for the development of novel functional metal-based metallic glasses. In this study, seven popular machine learning (ML) algorithms were employed to design novel M-based (M = Fe, Co, Ni, Ti, Zr, and rare earth metal (RE)) andX-component (X = 2, 3, 4, 5,6, and >6) alloys with excellent GFA. A GFA containing 6957 data points with structural analysis was established. Feature engineering was used to analyze the importance and correlation of features. ML algorithms were utilized for GFA prediction, revealing that Xtreme Gradient Boosting Trees exhibited the strongest predictive capability, achieving a high accuracy of 94.0%, a true positive rate of 97.6%, and a root mean squared error of 0.3705 across the entire dataset. Subsequently, the GFA of ternary to hexahydroxy alloys based on Fe, Co, Ni, Zr, Ti, and Y was predicted using all possible compositions generated through Python. Finally, a series of alloys with good GFA was successfully designed and prepared. The present work suggests that the proposed ML method can be utilized to design novel multiple-M-based amorphous alloys with high GFA. [ABSTRACT FROM AUTHOR]

Due to the scarcity of metallic lithium and the limited specific energy density of traditional inorganic cathodes, the application of lithium‐ion batteries (LIBs) in the large‐scale energy storage market is severely limited. To meet the growing power demands of electric vehicles and electronics, low‐cost and sustainable battery technologies need to be developed. Organic batteries and organic electrode materials emerge at a historic moment and show great potential. Polymer organic materials (POEs) with longer cycle life than small organic molecules that are easily soluble in liquid electrolytes provide more opportunities for advanced electrode materials. However, many issues remain to be addressed before POEs are widely used, such as sluggish electrode reaction kinetics and poor electronic conductivity. Herein, the classification, energy storage mechanism and features of POEs are briefly summarized. Further, the existing problems and design strategies of POEs are discussed and summarized in depth. This review aims to highlight the potential role of structural and morphological design in improving the practical performance of POEs in LIBs and provides insights into the in‐depth understanding and development of organic batteries and organic electrode materials. [ABSTRACT FROM AUTHOR]

Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties. [ABSTRACT FROM AUTHOR]

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb0.35Ta0.23Ti0.42N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb0.55Ti0.45N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions.

This contribution presents an approach for studying and understanding bacterial cellulose (BC) as a growing material (GM) to be produced, optimized and controlled for potential applications in the design field. The dialogic exchange between the world of design and that of applied sciences led research groups to envision, as a promising environment, the practice of growing materials instead of extracting them. This research has been structured to explore and verify the possibilities offered by design, as an experimental and holistic discipline, in the management of GMs, and specifically of BC. Through a detailed experimental setup and in-depth observation of the materials, a procedure to grow repeatable samples of BC is presented. Several progressive attempts were made and reported to define a precise procedure to grow BC. Potential improvements to the growing techniques and future developments of the work are discussed in the final part of the article, defining possible directions for the research in the design field. [ABSTRACT FROM AUTHOR]

The ablation effect of focused ultrasound (FUS) has played an increasingly important role in the biomedical field over the past decades, and its non-invasive features have great advantages, especially for clinical diseases where surgical treatment is not available or appropriate. Recently, rapid advances in the adjustable morphology, enzyme-mimetic activity, and biostability of sono-activated materials have significantly promoted the medical application of FUS ablation. However, a systematic review of sono-activated materials based on FUS ablation is not yet available. This progress review focuses on the recent design, fundamental principles, and applications of sono-activated materials in the FUS ablation biomedical field. First, the different ablation mechanisms and the key factors affecting ablation are carefully determined. Then, the design of sono-activated materials with high FUS ablation efficiencies is comprehensively discussed. Subsequently, the representative biological applications are summarized in detail. Finally, the primary challenges and future perspectives are also outlined. We believe this timely review will provide key information and insights for further exploration of focused ultrasound ablation and new inspiration for designing future sono-activated materials. The ablation effect of focused ultrasound (FUS) has played an increasingly important role in the biomedical field over the past decades. However, there are also some challenges of FUS ablation, such as skin burns, tumour recurrence after thermal ablation, and difficulty in controlling cavitation ablation. The rapid advance in adjustable morphology, enzyme-mimetic activity, and biostability of sono-activated materials has significantly promoted the medical application of FUS ablation. However, the systematic review of sono-activated materials based on FUS ablation is not yet available. This progress review focuses on the recent design, fundamental principles, and applications in the FUS ablation biomedical field of sono-activated materials. We believe this timely review will provide key information and insights for further exploration of FUS ablation. [Display omitted] [ABSTRACT FROM AUTHOR]

As an advanced energy storage system, lithium-ion batteries play an essential role in modern technologies. Despite their ubiquitous success, there is a great demand for continuous improvements of the battery performance, including higher energy density, lower safety risk, longer cycling life, and lower cost. Such performance improvement requires the design and development of novel electrode and electrolyte materials that exhibit desirable properties and satisfy strict requirements. Atomistic modeling can provide a unique perspective to fundamentally understand and rationally design battery materials. In this paper, we review a few recent successful examples of computation-driven discovery and design in electrode and electrolyte materials. Particularly, we highlight how atomistic modeling can reveal the underlying mechanisms, predict the important properties, and guide the design and engineering of electrode and electrolyte materials. We have a conclusion with a discussion of the unique capability of atomistic modeling in battery material development and provide a perspective on future challenges and directions for computation-driven battery material developments. [ABSTRACT FROM AUTHOR]

In this paper, we present and illustrate four principles used to develop discipline-specific academic language tutorials for students who enter university with low levels of academic language proficiency. The tutorials are part of a university-wide language support program following a post enrolment language assessment (PELA). The principles respond to recent arguments about the importance of post-PELA language support being integrated into the overall university curriculum, being directly relevant to students' needs, and being discipline and context specific. Drawing on sociocultural theories of language learning, we outline each principle and then illustrate them with practical frameworks and activities used in academic language development tutorials at a university in Australia. We also provide a discipline-specific language development tutorial plan that incorporates all four principles. These principles will be of interest to practitioners both in English Language Intensive Courses for Overseas Students (EL/COS) centres and in university language programs planning discipline-specific language development activities. [ABSTRACT FROM AUTHOR]

Abstract In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

Carbon fiber–reinforced epoxy composites are widely used in the marine and aviation sectors due to their superior mechanical properties. As research into these materials progresses, the demand for materials with even more complex and superior properties has increased. Traditional trial-and-error methods are limited to optimizing single factors and are plagued by heavy workloads and prohibitive costs when applied to multifactor composite designs. Addressing these problems, this study introduces a nonparametric multiobjective optimization process for composite materials. This approach employs a Gaussian process (GP) to establish a multiobjective regression model and uses nondominated sorting genetic algorithm II (NSGA II) to determine the Pareto front. Pareto solutions were ranked in accordance with their similarity to ideal solutions and on the basis of predetermined preference weights. The results demonstrate variations in optimal outcomes contingent on the weight values assigned. The differences between the optimized results and experimental validations reached 12.9%, with the minimum deviation being 0.6%. The effectiveness of the proposed method was demonstrated. The strategy combines GP and NSGA II to extend design methods for small samples and materials with multiple attributes. This method enables the simultaneous optimization of multiple conflicting objectives, offering greater efficiency than addressing them individually.

In response to environmental challenges, design promotes emerging materials connected with the circular economy and environmental sustainability. However, there is confusion about their definition and contribution to sustainable design and production, showing a gap in their classification. This article proposes a taxonomy as a helpful tool to consolidate and unify terminology, definitions and general understanding of these emerging materials. An analysis of 31 real-world case studies helped outline the taxonomic proposal to formalise knowledge, fostering clarity in classifying and identifying them. The taxonomy aims to organise emerging materials, generate reflections, and encourage their responsible development, diffusion, and adoption.

This study presents the Boron Enhanced Complex Concentrated Silicides (BECCSs) as a novel category of high-temperature materials derived from the high entropy concept. Using a quaternary MoNbTaW equiatomic alloy as a starting point, four new alloy compositions were designed in a multi-step process oriented towards reducing the density of the material. By incorporating different combinations of Ti, Si, and B, the phase composition of the materials was altered from a BCC solid solution to a multi-phase structure consisting of BCC solid solution and various intermetallics (silicides, borides, and borosilicides). The suggested modification of the alloy composition led to a significant decrease in density, reaching down to 6.87 g/cm3. All four alloys were fabricated by the arc melting technique, while their microstructure and room temperature mechanical properties were evaluated by SEM/EDS/EBSD and micro-indentation methods. The results of structural characterization enabled the identification of specific phase constituents. Consequently, it was established that the transition from BCC solid solutions to silicides/borides based alloys results in a significant increase in hardness, achieving up to 1200 HV (13 GPa).

Refractory Metal Intermetallic Composites and Refractory Complex Concentrated Alloys have been identified as promising candidates for ultra-high-temperature applications that exceed the limits of superalloys. However, designing and developing new materials with the proper density for aerospace applications is a significant challenge. For this reason, new refractory metal-based materials are in continuous development. This study introduces a new class of materials known as Boron-Enhanced Complex Concentrated Silicides (BECCSs). By providing a balance between density and high-temperature performance, these materials with their density-optimized refractory metal silicide-borides have the potential to revolutionize high-temperature applications. Utilizing a quaternary MoNbTaW equiatomic alloy (ρ = 13.73 gcm−3) as a starting point and a computer-aided alloy modeling tool, seven alloy compositions were designed in a multi-step process aimed at lowering the material density. Through the introduction of Ti, Si, and/or B, the microstructure was transformed from a BCC solid solution to a multiphase structure comprised of silicides and borides. The proposed redesign of the alloy led to a significant reduction in density, even to 5.44 gcm−3. All seven alloys were produced by using a laboratory arc melter, and their microstructure and room-temperature mechanical properties were analyzed using SEM, EDS, EBSD, and micro-indentation. The results of structural characterization allowed us to identify specific phase constituents, and it was established that a transition from BCC solid solutions to silicides/borides-based alloys results in a substantial increase in hardness, even above 1600 H V (17 GPa).

This qualitative study explores the process of designing and adapting materials for differentiated instruction in English language classrooms. The study aims to identify the factors that influence this process, the strategies that teachers use, and the challenges and opportunities they face. The study finds that the process of designing and adapting materials for differentiated instruction is complex and multifaceted, and is influenced by factors such as the needs and characteristics of learners, the curriculum and assessment requirements, and the available resources and technologies. Teachers use a variety of strategies to design and adapt materials, including modifying existing materials, creating new materials, and using digital tools and resources. The findings can inform future research and practice in the field of English language teaching, and contribute to the development of effective and inclusive approaches to language learning and teaching.

Models of ceramic-graphene nanocomposites are used to study how the manufacturing process-dependent arrangement of reduced graphene oxide (rGO) inclusions governs nano-crack network development. The work builds upon recent studies of such composites where a novel combinatorial approach was used to investigate the effect of rGO arrangements on electrical conductivity and porosity. This approach considers explicitly the discrete structure of the composite and represents it as a collection of entities of different dimensions - grains, grain boundaries, triple junctions, and quadruple points. Here, the combinatorial approach is developed further by considering the effects of rGO agglomerations, stress concentrators and adhesion energies on intergranular cracking. The results show that the fracture networks can be effectively controlled by the local ordering of rGO inclusions to allow for a concurrent increase in the strength and conductivity of the ceramic composites. It is shown that the ratio of local stress concentrators related to rGO inclusions and cracks is the most significant factor affecting the nano-crack network topology. The local spatial arrangement of rGO inclusions becomes an effective tool for controlling nano-crack network topology only when this ratio approaches one. It is anticipated that these results will inform future design of toughness-enhanced composites.

The Amazon region is home to great biodiversity and a population with traditional knowledge deeply linked to the management of natural resources. Despite the plurality of its territory, large spaces are in a constant process of urbanization, with a growing demand for alternatives in the matter of waste. The Açaí production chain (Euterpe oleracea Mart.) is one of the most important for the region, its importance is related to the cultural identity, economic value, and health benefits that the consumption of the fruit promotes. Although, the high production, derived from industrial processing, is responsible for environmental and social problems in the region's main cities. Developed by Camera and Karana (2018), Ma2E4 is a set of tools that aims to help understand how people evaluate a given material, at four different experiential levels: sensorial, interpretative, affective, and performative. Therefore, the present work aims to evaluate composite materials, developed with PLA/Açaí fibers and PLA/Açaí seeds, using Ma2E4 tools. Through a qualitative assessment, it was possible to identify sensory patterns among the materials developed.

Ceramic electrochemical cells (CECs) are promising devices for clean and efficient energy conversion and storage due to their high energy efficiency, more extended system durability, and less expensive materials. However, the search for suitable materials with desired properties, including high ionic and electronic conductivity, thermal stability, and chemical compatibility, presents ongoing challenges that impede widespread adoption and further advancement in the field. Artificial intelligence (AI) has emerged as a versatile tool capable of enhancing and expediting the materials discovery cycle in CECs through data-driven modeling, simulation, and optimization techniques. Herein, we comprehensively review the state-of-the-art AI applications for materials design and optimization for CECs, covering various material aspects, database construction, data pre-processing, and AI methods. We also present some representative case studies of AI-predicted and synthesized materials for CECs and provide insightful highlights about their approaches. We emphasize the main implications and contributions of the AI approach for advancing the CEC technology, such as reducing the trial-and-error experiments, exploring the vast materials space, discovering novel and optimal materials, and enhancing the understanding of the materials-performance relationships. We also discuss the AI approach's main limitations and future directions for CECs, such as addressing the data and model challenges, improving and extending the AI models and methods, and integrating with other computational and experimental techniques. We conclude by suggesting some potential applications and collaborations for AI in materials design for CECs.