Composition Space Research Articles

Machine learning filters out efficient electrocatalysts in the massive ternary alloy space for fuel cells

Despite their potential promise, multicomponent materials have not been actively considered as catalyst materials to date, mainly due to the massive compositional space. Here, targeting ternary electrocatalysts for fuel cells, we present a machine learning (ML)-driven catalyst screening protocol with the criteria of structural stability, catalytic performance, and cost-effectiveness. This process filters out only 10 and 37 candidates out of over three thousand test materials in the alloy core@shell (X3Y@Z) for each cathode and anode of fuel cells. These candidates are potentially synthesizable, lower-cost and higher-performance than conventional Pt. A thin film of Cu3Au@Pt, one of the final candidates for oxygen reduction reactions, was experimentally fabricated, which indeed outperformed a Pt film as confirmed by the approximately 2-fold increase in kinetic current density with the 2.7-fold reduction in the Pt usage. This demonstration supports that our ML-driven design strategy would be useful for exploring general multicomponent systems and catalysis problems.

Synthesis of High-Entropy-Alloy Nanoparticles by a Step-Alloying Strategy as a Superior Multifunctional Electrocatalyst.

High-entropy-alloy nanoparticles (HEA-NPs) have attracted great attention because of their unique complex compositions and tailorable properties. Further expanding the compositional space is of great significance for enriching the material library. Here, a step-alloying strategy is developed to synthesis HEA-NPs containing a range of strongly repellent elements (e.g., Bi-W) by using the rich-Pt cores formed during the first liquid phase reaction as the seed of the second thermal diffusion. Remarkably, the representative HEA-NPs-(14) with up to 14 elements exhibits extremely excellent multifunctional electrocatalytic performance for pH-universal hydrogen evolution reaction (HER), alkaline methanol oxidation reaction (MOR), and oxygen reduction reaction (ORR). Briefly, HEA-NPs-(14) only requires the ultralow overpotentials of 11 and 18mV to deliver 10 mA cm-2 and exhibits ultralong durability for 400 and 264h under 100mAcm-2 in 0.5m H2 SO4 and 1m KOH, respectively, which surpasses most advanced pH-universal HER catalysts. Moreover, HEA-NPs-(14) also exhibits an impressive peak current density of 12.6 A mg-1 Pt in 1m KOH+1m MeOH and a half-wave potential of 0.86V (vs RHE.) in 0.1m KOH. The work further expands the spectrum of possible metal alloys, which is important for the broad compositional space and future data-driven material discovery.

Enhanced energy‐storage properties in Zr4+‐modified (Bi0.4Ba0.2K0.2Na0.2)TiO3 high‐entropy ceramics

AbstractRecently, high‐entropy perovskite oxides (HEPOs) have received increasing interest for energy storage applications owing to their unique structure, huge composition space, and promising properties. However, designing HEPOs with improved energy storage performance remains a challenge. In this study, various HEPOs were designed by partially replacing Zr4+ for Ti4+ in (Bi0.4Ba0.2K0.2Na0.2)TiO3 medium‐entropy ferroelectric ceramics. The resulting ceramics exhibited a pseudo‐cubic structure. With increasing Zr4+ content, the ceramics gradually transformed into relaxor ferroelectrics. The energy storage performance of the ceramics depended on the Zr4+ content. The sample with 20 mol% Zr4+ showed the best energy storage performance with a maximum reversible energy density of 2.47 J/cm3 and an energy storage efficiency of 82.3% at a low applied electric field (224 kV/cm). This study obtained a promising material for the new generation dielectric energy storage capacitors and provided a novel method for enhancing energy storage performance.

Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction

Creep rupture life is a key target for designing Ni-based single crystal (SX) superalloys. Machine learning (ML) has the potential to accelerate alloy design by accurately predicting the creep rupture life of potential superalloys. In this work, a physical-constrained neural network named Superalloy Transformer-based Network for Creep (SaTNC) is established, and the representation of superalloy is automatically learned based on deep representation learning. As core physical bias in the SaTNC model, the information associated with interaction among alloying elements achieved by Transformer-based neural network is introduced to constrain the representation of the superalloy. The SaTNC model exhibits competitive generalization performance and the results have verified the high capability of the SaTNC model to capture the sensitivities of creep rupture life on alloy composition and service conditions. Moreover, Transformer-based deep representation for superalloy with both effectiveness and interpretability is obtained. The material insights including the interactions among alloying elements and their combined effects on the creep rupture life of superalloy are extracted from this deep representation. Finally, with the aid of the SaTNC model, a potential alloy composition space with low density and excellent creep resistance at high temperatures is identified, which will be experimentally verified in the future.

First principles prediction of yield strength of body centered cubic structured high entropy alloys

High entropy alloys (HEAs) with a body centered cubic (bcc) crystal structure have emerged as potential high-performance structural materials due to their high strength at room and elevated temperatures. In this study, a computational approach, based on the revised Peierls-Nabarro model and using the inputs solely calculated from the first principles density functional theory, has been developed to predict the yield strength of bcc HEAs. Examining its accuracy and reliability, the developed computational approach was applied to four different types of bcc HEAs. The yield strength was predicted to be 1034 MPa for MoNbTaW, 1489 MPa for MoNbTaV, 1356 MPa for AlCoCrFeNi, and 1740 MPa for AlCoCrFeNiZr0.3 alloy, respectively. These computational predictions are found to agree well with available experimental data. Moreover, the developed computational approach accurately quantifies the changes in the yield strength from MoNbTaW to MoNbTaV with a change of constituent W to V, and from AlCoCrFeNi to AlCoCrFeNiZr0.3 with addition of 6 at% Zr. Therefore, this first-principles based computational approach provides a way for expedite optimization on the mechanical properties of bcc HEAs across vast composition space.

A self-consistent extension of flamelet theory for partially premixed combustion

Orthogonal coordinate systems represent an attractive alternative for the formulation of two-dimensional composition space equations for partially premixed combustion: They avoid the need of closure models for a cross scalar dissipation rate and allow for a direct recovery of the corresponding flamelet equations in the asymptotic limits of non-premixed and premixed combustion. Despite these remarkable features, this kind of coordinate system still presents some important unsolved issues, which have limited their application so far. These difficulties are mainly associated with i) the lack of an appropriate formalism for the definition of two variables having orthogonal gradients in the entire flame domain and ii) the absence of corresponding closure models for the gradients of these variables in two-dimensional composition space. In the present work, it is shown how a Lagrangian interpretation of the flamelet derivative allows solving both problems. More specifically, after the mixture fraction, Z, is adopted as first coordinate, the proposed approach allows to derive i) two-dimensional composition space equations for all reactive scalars, ii) a transport equation for a modified reaction progress variable, φ, satisfying the desired orthogonality condition, ∇Z·∇φ=0, and iii) two-dimensional composition space equations for gZ=|∇Z| and gφ=|∇φ|. The obtained set of two-dimensional equations in orthogonal composition space is general and it can describe different flames of interest. In order to illustrate the capabilities of the formulation, the equations are then specialized for planar flames with unity Lewis number, obtaining in this way a solvable set of composition space equations in orthogonal coordinates. After an appropriate numerical approach is introduced, the resulting 2D equations are solved to analyze the interaction between premixed flamelets with a strain rate prescribed along the Z-dimension controlling the interaction. Both flame structures and budgets of the scalar gradient equations are studied and the results provide new insights into the physics of scalar gradients in two-dimensional composition space. Finally, conceivable coupling strategies of the present formulation with CFD codes for the simulation of turbulent flames are discussed.

Multielementary Alloy Chiral Nanoparticles with Strong Optical Activities

AbstractStructural chirality has been imposed onto nano‐alloys to introduce diverse new properties. Chiral nanoparticles (CNPs) with the atomic scale chirality are composed of metastable chiral lattices having low thermal stability, so multielementary (>2 elements) CNPs are very challenging to produce using traditional high‐energy fabrication methods. Herein, layer‐by‐layer glancing angle deposition (LbL‐GLAD) at a low substrate temperature of ≈−40 °C, consisting of GLAD of the host CNPs and the subsequent GLAD of guests, is devised to extend the alloy compositional space to the ternary (e.g., Ag:Al:Cu and Ag:Al:Au) and quaternary (e.g., Ag:Al:Cu:Au and Al:In:Sn:Ti). The low‐temperature GLAD‐induced alloying facilitates the formation of the metastable multi‐layer chiral twisting of achiral facets, chiral defects due to the diffusion of the guests, and chiral electronic bands, leading to a significant amplification of chiroplasmonic optical activities in 20–30 folds. The LbL‐GLAD can be generally adapted to fabricate multielementary alloy CNPs composed of a wide range of elements, with a prospective application of asymmetric catalysts to synthesize an enantiomer with designable chirality, one of the most important topics in modern chemistry and biochemistry to solve the problems of health and environmental pollution.

The design of compositionally modulated lead-free ferroelectrics with large electromechanical response via high-throughput phase-field simulations and machine learning

Compositionally graded ferroelectrics (CGFEs) have attracted great interest due to their exceptional and tunable electromechanical properties, which are anticipated to be superior to traditional ferroelectrics. However, an effective design of CGFEs with desired properties from a huge compositional space remains an enormous challenge. In this study, we present an efficient design strategy for CGFEs through a combination of high-throughput phase-field simulation and machine learning (ML) algorithm. Systematic phase-field simulations are first performed to establish a dataset of electromechanical properties for various CGFEs, which are characterized by three degrees of freedom, including compositions of two end materials and gradient index. A ML-assisted optimization process is then conducted on this dataset to yield optimal electromechanical properties, which are further analyzed by phase-field simulations. The ML-guided simulations lead us to discover that the large optimal electromechanical responses can be achieved in CGFEs with average composition near the morphotropic phase boundary yet large compositional gradient. These CGFEs accommodate a coexistence of tetragonal, orthorhombic, and cubic phases in their domain structures, which flatten the energy barrier landscape and facilitate the rotation of polarization under mechanical field. Our proposed highly efficient design strategy can be readily adapted to identify CGFEs possessing other principal elements and properties, which will accelerate the discovery of new high-performance CGFE materials.

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Fabrication and Investigation of Graphite-Flake-Composite-Based Non-Invasive Flex Multi-Functional Force, Acceleration, and Thermal Sensor.

This work examines the physics of a non-invasive multi-functional elastic thin-film graphite flake-isoprene sulfone composite sensor. The strain design and electrical characterization of the stretching force, acceleration, and temperature were performed. The rub-in technique was used to fabricate graphite flakes and isoprene sulfone into sensors, which were then analyzed for their morphology using methods such as SEM, AFM, X-ray diffraction, and Fourier transform infrared spectroscopy to examine the device's surface and structure. Sensor impedance was measured from DC to 200 kHz at up to 20 gf, 20 m/s2, and 26-60 °C. Sensor resistance and impedance to stretching force and acceleration at DC and 200 Hz rose 2.4- and 2.6-fold and 2.01- and 2.06-fold, respectively. Temperature-measuring devices demonstrated 2.65- and 2.8-fold decreases in resistance and impedance at DC and 200 kHz, respectively. First, altering the graphite flake composite particle spacing may modify electronic parameters in the suggested multi-functional sensors under stress and acceleration. Second, the temperature impacts particle and isoprene sulfone properties. Due to their fabrication using an inexpensive deposition technique, these devices are environmentally friendly, are simple to build, and may be used in university research in international poverty-line nations. In scientific laboratories, such devices can be used to teach students how various materials respond to varying environmental circumstances. They may also monitor individuals undergoing physiotherapy and vibrating surfaces in a controlled setting to prevent public health risks.

Ball milling process variables optimization for high-entropy alloy development using design of experiment and genetic algorithm

Besides developing new high-entropy alloys (HEAs) from vast compositional space, developing HEA powders with good control of grain- and- particle sizes (GPS) for improved flowability/sinterability in additive-manufacturing and sintering processes are of growing interest. Using a mechanical alloying process, the effect of parameters—process control agent (PCA) type and amount, and milling speed and time—on solid-solution onset and GPS of a model HEA (AlCoCrFe) is systematically investigated. The optimum PCA-type is first experimentally determined; followed by: a Taguchi design of experiment to plan and study the effect of parameters on the response variables—GPS, regression model to develop predictive process models, and model multi-objective optimization using genetic algorithm. Milling speed and PCA amount have the most effect on the grain- and particle- sizes, respectively. Two mathematical models for predicting mechanically-alloyed HEA GPS are developed. This work develops a framework for appropriate processing parameter selection for HEA powder development.

VIKTOR GONTARIV: THE SEARCH FOR NATIONAL STYLE AND NATIONAL FORM

The article is devoted to the problem of national style and national form in the work of the modern Ukrainian painter Viktor Gontariv. Today, this problem is acutely relevant due to the threat of losing national uniqueness due to the spread of globalization processes in the world, and the high spiritual values preserved in the centuries-old traditions of national culture are often rejected in favor of the market, fashion, the advance of information technologies, mass culture. This leads to the spread of eclecticism, clichés and simply bad taste. For the first time, the painting of V. Gontariv is considered in the context of the direction of creativity of representatives of the Ukrainian line in Ukrainian fine art of the late 20th – early 21st centuries – A. Antonyuk, F. Humanyuk, I. Marchuk, M. Popov, etc. It is emphasized that the basis of the personal variants of the modern Ukrainian national style of these masters is an appeal to the traditions of Ukrainian folk art, the use of images-symbols and images-metaphors. During the analysis of Gontariv’s paintings, special attention was paid to their close connection with traditional Ukrainian mythology and peasant artistic naivety in the works of folk painters of the 1940s – 1950s in the village Sotnytsky Kozachok, in Slobozhanshchyna, where V. Gontariv was born and spent his childhood, and at the same time his national mentality was formed, which ultimately determined the main line of his work. For the first time, common and distinctive features of the variants of the national style of M. Boichuk’s school and V. Gontariv’s school, though these artists worked in different periods of Ukrainian history, were analyzed. Attention is also drawn to the specifics of the direction of the workshops of V. Gontariv at KSADA and M. Storozhenko at the NAFAA. The article reveals that the peculiarities of the national form in the work of V. Gontariv consist not only in the appeal to the principle of convention in the reflecting the shape of objects and building the compositional space, but also in creating a new type of images of the Ukrainian peasantry of the Soviet era.

Large Decrease in the Melting Point of Benzoquinones via High-n Eutectic Mixing Predicted by a Regular Solution Model.

Decreasing the melting point (Tm) of a mixture is of interest in cryopreservatives, molten salts, and battery electrolytes. One general strategy to decrease Tm, exemplified by deep eutectic solvents, is to mix components with favorable (negative) enthalpic interactions. We demonstrate a complementary strategy to decrease Tm by mixing many components with neutral or slightly positive enthalpic interactions, using the number of components (n) to increase the entropy of mixing and decrease Tm. In theory, under certain conditions this approach could achieve an arbitrarily low Tm. Furthermore, if the components are small redox-active molecules, such as the benzoquinones studied here, this approach could lead to high energy density flow battery electrolytes. Finding the eutectic composition of a high-n mixture can be challenging due to the large compositional space yet is essential for ensuring the existence of a purely liquid phase. We reformulate and apply fundamental thermodynamic equations to describe high-n eutectic mixtures of small redox-active molecules (benzoquinones and hydroquinones). We illustrate a novel application of this theory by tuning the entropy of melting, rather than the enthalpy, in systems highly relevant to energy storage. We demonstrate with differential scanning calorimetry measurements that 1,4-benzoquinone derivatives exhibit eutectic mixing that decreases their Tm, despite slightly positive enthalpies of mixing (0-5 kJ/mol). By rigorously investigating all 21 binary mixtures of a set of seven 1,4-benzoquinone derivatives with alkyl substituents (Tm's between 44 and 120 °C), we find that the eutectic melting point of a mixture of all seven achieves a large decrease in Tm to -6 °C. We further determine that the regular solution model shows improvement over an ideal solution model in predicting the eutectic properties for this newly investigated type of mixture composed of many small redox-active organic molecules.

A pairwise mixing model with kernel constraint and its appraisal in transported PDF simulations of ethylene flames

Modeling micro-mixing remains a primary challenge for the transported probability density function (TPDF) method. In this study, the pairwise mixing with kernel constraint (KerM) model is proposed and evaluated in TPDF simulations of a turbulent non-premixed ethylene flame. The new model features controllable localness by selecting mixing particle pairs with a probability that depends on their distance in composition space, normalized by a characteristic kernel size. To evaluate the model in a controlled setting, TPDF simulations were carried out with the mean velocity, turbulent diffusivity and scalar mixing frequencies extracted from a direct numerical simulation (DNS) dataset. The predictions of the mean statistics and the conditional PDFs illustrate that the KerM model exhibits asymptotic behavior to the modified Curl (MC) and the Euclidean minimum spanning trees (EMST) model, respectively, depending on the kernel size. For the flame considered, results for KerM approach those for EMST when the kernel size is less than 0.01, while they approach those for MC when the kernel size is larger than 1.0. Given a kernel size of 0.1, KerM yields a notably better prediction of the overall combustion process than the conventional MC model. The predictions of the mean temperature and species mass fractions by KerM are similar to those by EMST, while KerM better predicts the conditional PDF of temperature than EMST that overdamps the conditional fluctuations. In addition, its complexity with the number of particles per cell is instead of O(Np2) for EMST. By incorporating different mixing timescales among species, the KerM-DD model better predicts the mean temperature field at the time instant close to local extinction than the KerM and EMST models. This work demonstrates that the KerM model, apart from being simple and robust in terms of implementation, features the advantages of being flexible in localness and being capable of incorporating differential diffusion.

Effect of Art Therapy on Changes in Psycho-Emotional State for Women Who Have Attempted Suicide

Background. Research is directed towards female suicides and the solution of the postvention problems in Lithuania. Suicidal behavior of women is conditioned by their psychological sensitivity to different psycho-social factors. The-psycho-emotional state of women who have attempted suicide is worsened, different negative emotions prevail, they lack self-esteem and are disappointed. The methods used in the research aim to answer the same question: does art therapy have an effect on the psycho-emotional state of women who have attempted suicide? It uses different methods of data analysis, i.e. quantitative and qualitative analysis of a small sample.
 Research aim. The aim of the research is to present effects of art therapy on changes in the psycho-emotional state of women who have attempted suicide. The aim of the research is supported by the following tasks and questions. Tasks: 1) to determine changes of depression symptoms; 2) to determine change of anxiety symptoms. Questions: 1) how do visually dominant iconographic motifs in patients’ drawings change during art therapy? 2) how do visually dominant formal motifs in patients’ drawings change during art therapy? 3) does the change of iconographic and formal motifs relate to changes of the emotional state of a selected group of women? 4) how interrelated are results of quantitative and qualitative research methods?
 Methods. Mixed research was conducted using the HADS scale, with analysis of visually dominant iconographic and formal elements.
 Results. The art therapy intervention significantly reduced the level of depression in the subjects. Art therapy had no significant effect on subjects’ anxiety levels. Selected iconographic motifs in the subjectsʼ drawings during the process of art therapy changed: from visually unindentifiable to recognizable objects; from an abstract symbolic meaning to a more concrete and clearly defined meaning; from a smaller to a larger number of iconographic elements; from an iconographically blurred to a more explicit plot. Selected formal motifs in the patients’ drawings during the process of art therapy changed: from incomplete to more detailed depiction of the object; from closed to more open silhouette type; from an isolated object opposed to the surrounding space to an object increasingly infiltrating into that space; from a closed to a progressively more open composition; from linearity to pictoriality; from darker to lighter color scale; from poorer to richer color palettes. The change of iconographic and formal motifs is related to the change of the emotional state of the subjects: the reduced isolation of the depicted objects and the strict separation from the surrounding space show a reduced opposition of the subjects, and the beginning of the social and psychological connection with the surrounding world. The shift from an incomplete to a more detailed depiction of an object shows that the former uncontrollability of the subjects’ self, their feelings, and their connection with the environment turns into clear and rational controllability; more concrete and realistic drawings show the increased awareness of the subjects and better connection with the surrounding world.
 Conclusions. The change in depression levels after art therapy intervention is statistically significant (p<0.05). The change in anxiety levels after art therapy intervention is not statistically significant (p>0.05). The shift of the drawings is from abstract to specific iconography. The formal motifs of the drawings become more detailed and their type of representation become more varied; the depiction of the objects have become more integrated in the composite space, and the composition become more open; the paintings became more artistic, and the color range became wider. The psycho-emotional state of the subjects in the sessions varied from detachment, withdrawal, hiding, fear of looking at oneself, to opening up, trying to notice and make sense of all one’s own negativity, to manage negative emotions and to project one’s future.
 Keywords: art therapy, woman suicide, anxiety, depression.

A ductility metric for refractory-based multi-principal-element alloys

We propose a quantum-mechanical dimensionless metric, the local-lattice distortion (LLD), as a reliable predictor of ductility in refractory multi-principal-element alloys (RMPEAs). The LLD metric is based on electronegativity differences in localized chemical environments and combines atomic-scale displacements due to local lattice distortions with a weighted average of valence-electron count. To evaluate the effectiveness of this metric, we examined body-centered cubic (bcc) refractory alloys that exhibit ductile-to-brittle behavior. Our findings demonstrate that local-charge behavior can be tuned via composition to enhance ductility in RMPEAs. With finite-sized cell effects eliminated, the LLD metric accurately predicted the ductility of arbitrary alloys, which compares well with existing tensile-elongation experiments. To validate further, we qualitatively evaluated the ductility of two refractory RMPEAs, i.e., NbTaMoW and Mo72W13Ta10Ti2.5Zr2.5, through the observation of crack formation under indentation, again showing excellent agreement with LLD predictions. A comparative study of three refractory alloys provides further insights into the electronic-structure origin of ductility in refractory RMPEAs. This proposed metric enables rapid and accurate assessment of ductility behavior in the vast RMPEA composition space.

Composition optimization of cobalt-free Fe-Cr-Ni-Al/Ti multi-principal element alloys for strength-ductility trade-off based on machine learning

Multi-principal element alloys (MPEAs) represent a novel class of advanced structural materials. However, due to the vast composition space, traditional experimental methods and first-principles calculation are not efficient for exploring this space. In recent years, machine learning (ML) has emerged as a promising tool for alloy design, offering an effective approach to develop high-strength and high-toughness MPEAs. In this work, we proposed a random-exhaustive feature selection (REFS) method to select an appropriate feature subset from a high-dimensional feature for model training. Moreover, we compared the metrics of models trained by various ML algorithms using either composition input or parameter input strategies. The results indicated that the models trained by using the Gradient Boosting Regression (GBR) algorithm with the parameter input strategy exhibited the high R2 values of yield strength (0.947), ultimate tensile strength (0.922), and fracture elongation (0.844). Finally, we conducted a two-step composition design for cobalt-free Fe-Cr-Ni-Al/Ti MPEAs. The optimized models designed Al5(Fe10Cr35Ni55)95 and Al2Ti1(Fe10Cr35Ni55)97 MPEAs, which demonstrated the significant improvements of 56.42%, 29.45%, and 5.82% in yield strength (YS), ultimate tensile strength (UTS), and fracture elongation (FE), respectively, compared to equiatomic FeCrNi MPEA. In addition, by using the association rule mining (ARM) on the model predictions, the compositional dependence of mechanical properties and the possibility of data-driven alloy design were revealed.

Hybrid calphad DFT modelling of the Mg–Al–Ca system

The Mg–Al–Ca system is an important system for Mg alloys. Al is the most common alloying element for Mg alloys, and additions of Ca can be used to produce ductile alloys and alloys with good creep properties. The Mg–Al–Ca system has been investigated extensively in the past, in particular the Mg-rich corner. Also, calculations using density functional theory (DFT) have been extensively applied in the past. DFT calculations generally show high accuracy and consistency in this system. The Mg–Al–Ca system contains all three Laves phases (C14, C15 and C36), but existing thermodynamic models of this system only contain rather simplified and limited models for the Laves phases. In this work, DFT calculations are used to provide energies of formation for all possible end-members of the Laves phases and the Mg17Al12 (A12) phase. This is combined with a thermodynamic modelling of the system to provide a thermodynamic description of the system where the Laves phases and the Mg17Al12 phase have reasonable Gibbs energies for the complete composition space. The new model description also provides a good representation of most existing experimental data, in particular in the important Mg-rich corner.

Parameterized shape adaptive materials for sportswear

PurposeConventional sportswear design does not take into account body size changes that many individuals experience (e.g. through pregnancy, puberty, menstruation, etc.). This paper aims to detail both the construction of a novel wearable shape-adaptive composite and a new meso-scale material design method, which enables the optimal creation of these structures.Design/methodology/approachThis work reports the development of a predictive computational model and a corresponding design tool, including results of a tensile testing protocol to validate their outputs. A mathematical model was developed to explore the geometric parameter space of a bi-stable composite system, which then feeds into an optimization design tool.FindingsThe authors found that it is possible to fabricate shape-adaptive composites via 3D printing bi-stable structures, and adhering them to a base textile. Experimental mechanical tensile testing showed good agreement with the predictive model in mid-range unit cell amplitude designs. To illustrate how the optimization design tool works this paper details two design examples, one for expected shape change during pregnancy and one for targeted compression for high performance swimwear. The optimized design parameters are shown to replicate the target parameters, however there is potential for further improvement with a lower stiffness base textile.Originality/valueAlthough there is a wealth of research on multi-stable mechanisms, there is a dearth of studies that apply these structures in the wearable composite space. Additionally, there is a need for design methods which leverage the structurally-programmable capabilities of multi-stable structures to create optimized, high-performance functional composites.

High-Throughput Exploration of Triple-Cation Perovskites via All-in-One Compositionally-Graded Films.

Many devices heavily rely on combinatorial material optimization. However, new material alloys are classically developed by studying only a fraction of giant chemical space, while many intermediate compositions remain unmade in light of the lack of methods to synthesize gapless material libraries. Here report a high-throughput all-in-one material platform to obtain and study compositionally-tunable alloys from solution is reported. This strategy is applied to make all Csx MAy FAz PbI3 perovskite alloys (MA and FA stand for methylammonium and formamidinium, respectively), in less than 10min, on a single film, on which 520 unique alloys are then studied. Through stability mapping of all these alloys in air supersaturated with moisture, a range of targeted perovskites are found, which are then chosen to make efficient and stable solar cells in relaxed fabrication conditions, in ambient air. This all-in-one platform provides access to an unprecedented library of compositional space with no unmade alloys, and hence aids in a comprehensive accelerated discovery of efficient energy materials.