Composition-property Relationships Research Articles

Explaining the spread in measurement of PDMS elastic properties: influence of test method and curing protocol.

Accuracy in the measurement of mechanical properties is essential for precision engineering and for the interrogation of composition-property relationships. Conventional methods of mechanical testing, such as uniaxial tension, compression, and nanoindentation, provide highly repeatable and reliable results for stiff materials, for which they were originally developed. However, when applied to the characterization of soft and biological materials, the same cannot be said, and the spread of reported properties of similar materials is vast. Polydimethylsiloxane (PDMS), commonly obtained from Dow as SYLGARD 184, is a ubiquitous such material, which has been integral to the rapid development of biocompatible microfluidic devices and flexible electronics in recent decades. However, reported shear moduli of this material range over 2 orders of magnitude for similar chemical compositions. Taking advantage of the increased mechanical scrutiny afforded to SYLGARD 184 in recent years, we combine both published and new experimental data obtained using 9 mechanical test methods. A statistical analysis then elucidates the significant bias induced by the test method itself, and distinguishes this bias from the influence of curing protocols on the mechanical properties. The goal of this work is thus two-fold: (i) it provides a quantitative understanding of the different factors that influence reported properties of this particular material, and (ii) it serves as a cautionary tale. As researchers in the field of mechanics strive to quantify the properties of increasingly complex soft and biological materials, converging on a standardized measurement of PDMS is a necessary first step.

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

Strained single crystal high entropy oxide manganite thin films

The ability to accommodate multiple principal cations within a single crystallographic structure makes high entropy oxides (HEOs) ideal systems for exploring new composition–property relationships. In this work, the high-entropy design strategy is extended to strained single-crystal HEO-manganite (HEO-Mn) thin films. Phase-pure orthorhombic films of (Gd0.2La0.2Nd0.2Sm0.2Sr0.2)MnO3 were deposited on three different single-crystal substrates: SrTiO3 (STO) (100), NdGaO3 (110), and LaAlO3 (LAO) (100), each inducing different degrees of epitaxial strain. Fully coherent growth of the thin films is observed in all cases, despite the high degree of lattice mismatch between HEO-Mn and LAO. Magnetometry measurements reveal distinct differences in the magnetic properties between epitaxially strained HEO-Mn thin films and their bulk crystalline HEO counterparts. In particular, the bulk polycrystalline HEO-Mn shows two magnetic transitions as opposed to a single one observed in epitaxial thin films. Moreover, the HEO-Mn film deposited on LAO exhibits a significant reduction in the Curie temperature, which is attributed to the strong variation of the in-plane lattice parameter along the thickness of the film and the resulting changes in the Mn–O–Mn bond geometry. Thus, this preliminary study demonstrates the potential of combining high entropy design with strain engineering to tailor the structure and functionality of perovskite manganites.

Study on the composition-property relationships of basalt fibers based on symbolic regression and physics-informed neural network

Despite the known influence of chemical composition on the mechanical properties of basalt fibers, a clear understanding of this relationship is lacking. Chemical composition analysis and mechanical property tests are performed on basalt fiber samples. Test data is collected from various countries and regions to expand the dataset. An improved Physics-Informed Neural Network (PINN) approach is specifically designed to address the complexities of this relationship. By incorporating physical models like the Makishima-Mackenzie model, Rocherulle model and a symbolic regression formula, the PINN leverages established physical principles to enhance its ability to understand the underlying mechanisms governing the influence of chemical composition on mechanical properties. This focus on physical mechanisms not only improves the interpretability of the model but also empowers it to make accurate predictions, as evidenced by the high squared correlation coefficients of 0.8767 and 0.8145 between predicted and experimental values of modulus and strength, respectively.

Unveiling the dynamic change in the ionic conductivity of a solid-state binary mixture comprising an organic ionic plastic crystal and LiBF4

The binary composites of organic ionic plastic crystals (OIPCs) and lithium salts are a promising new class of solid electrolytes for all-solid-state batteries (ASSBs). One of the primary areas of research to enable their application into battery devices is to understand their complex physicochemical features, in particular their phase behaviors in response to changing compositions and the ensuing correlation with ionic conductivities. Herein, the phase behaviors and transport properties of binary OIPC–lithium-salt composites are reported over the full compositional window using N-ethyl-N-methylpyrrolidinium tetrafluoroborate ([C2mpyr][BF4]) and lithium tetrafluoroborate (LiBF4). The phase diagram of the Lix[C2mpyr]1–x[BF4] electrolyte was constructed and used to not only provide valuable insights into the key composition–property relationships but also guide the selection of Li+ concentrations in OIPC composites for Li+-based ASSBs. Differential scanning calorimetry and 19F nuclear magnetic resonance (NMR) spectroscopy revealed the emergence of intermediate secondary phases as the composition was varied. Significant and sudden variations in ionic conductivity at or near the Li+-concentration-driven phase transitions were attributed to the presence of these intermediate phases which ultimately provide the dominant contribution to the ionic conductivity of the Lix[C2mpyr]1–x[BF4] electrolyte.

Insight into the nutritional, physicochemical, functional, antioxidative properties and in vitro gastrointestinal digestibility of selected Thai rice: Comparative and multivariate studies

Nutritional, physicochemical, functional, antioxidative and digestion properties of brown and white rice flours from four Thai rice varieties (Luangpatue, upland rice, RD43, and Hommali) were investigated and compared. Due to differences in grain color, the color parameters of flours varied significantly. Protein, fat, ash, carbohydrate, and moisture content, total dietary fiber, and calories of these rice flours were 6.94–10.21%, 1.68–3.16%. 0.554–1.442%, 71.20–79.68%, 9.79–10.53%, 1.07–3.64%, and 350.82–362.73 kcal/100g, respectively. RD43 brown rice (18.4%) and Luangpatue white rice (26.5%) respectively exhibited the lowest and highest amylose content. Luangpatue rice flours also showed higher swelling power, setback value, final viscosity, and thermal properties than other varieties. The variations in hydration properties and oil absorption index were noticeable among these rice flours. In addition, the highest level of total phenolic content and antioxidant activity led to the lowest estimated glycemic index (eGI = 62.92) found in upland brown rice. It was confirmed by the multivariate analysis results. This study reported the diverse physicochemical properties and composition-property relationships of two kinds of flours from four rice varieties collected from Thailand for the first time. It exhibited possible capabilities for the development of various rice-based products that promote health based on their characteristics on industrial scale.

Development of supported intermetallic compounds: advancing the Frontiers of heterogeneous catalysis.

Intermetallic compound (IMC) catalysts have garnered significant attention due to their unique surface and electronic properties, which can lead to enhanced catalytic performance compared to traditional monometallic catalysts. However, developing IMC materials as high-performance catalysts has been hindered by the inherent complexity of synthesizing nanoparticles with well-defined bulk and surface compositions. Achieving precise control over the composition of supported bimetallic IMC catalysts, especially those with high surface area and stability, has proven challenging. This review provides a comprehensive overview of the recent progress in developing supported IMC catalysts. We first examine the various synthetic approaches that have been explored to prepare supported IMC nanoparticles with phase-pure bulk structures and tailored surface compositions. Key factors influencing the formation kinetics and compositional control of these materials are discussed in detail. Then the strategies for manipulating the surface composition of supported IMCs are delved into. Applications of high-performance supported IMCs in important reactions such as selective hydrogenation, reforming, dehydrogenation, and deoxygenation are comprehensively reviewed, showcasing the unique advantages offered by these materials. Finally, the prevailing research challenges associated with supported IMCs are identified, including the need for a better understanding of the composition-property relationships and the development of scalable synthesis methods. The prospects for the practical implementation of these versatile catalysts in industrial processes are also highlighted, underscoring the importance of continued research in this field.

Composition–property relationships of choline based eutectic solvents: impact of the hydrogen bond donor and CO2 saturation

Choline based eutectic solvents are developed for CO2 capture. The physical properties as a function of the H-bond donor and acceptor components are examined. Accordingly, molecular descriptors are identified for future property predictions.

Rapid screening of ternary amorphous formulations by a spray drying robot

Spray drying is commonly used for producing amorphous solid dispersions to improve drug solubility. The development of such formulations typically relies on comprehensive excipient and composition screening, which requires the preparation of many spray-dried powder samples. This is both labour-intensive and time-consuming when carried out manually. In the present work, the formulation screening task was automated by coupling a laboratory spray dryer operated in a semi-continuous mode with custom-made add-ons, allowing for rapid, computer-controlled production of formulation samples with systematically varying composition. The practical use of the spray drying robot in formulation development was demonstrated on a case study of poorly water-soluble model drugs simvastatin and ezetimibe. Six different polymers and several drug:polymer ratios were screened for the enhancement of dissolution properties. From a pool of 28 spray-dried samples, ternary compositions containing Eudragit L100-55 were identified as the most suitable ones for further processing and characterisation. The ability to populate the formulation design space rapidly and automatically made it possible to construct maps of physico-chemical properties such as glass transition temperature or dissolution rate. The spray drying robot thus enables the acceleration of early formulation development and a deeper understanding of composition-property relationships for multi-component spray dried powders.

Effective subgrouping enhances machine learning prediction in complex materials science phenomena: Inoue's subgrouping in discovering bulk metallic glasses

Addressing complex materials science problems through machine learning (ML) is challenging. A primary reason for the challenge is that the underlying mechanisms may vary within the considered problem space. To quantify this, we divide alloy data into subgroups and construct ML models to predict metallic glass formation. We discover that subgrouping guided by physical insights into the problem leads to significantly higher prediction accuracy. Specifically, when applying Inoue's subgrouping, models specific to subgroups outperform those trained on the entire dataset. Moreover, our analysis uncovers distinct mechanisms and contributing factors that control the glass-forming ability in different subgroups, shedding light on the diverse nature of this phenomenon. Statistical methods for subgrouping prove less effective and constrained when compared to physics-informed subgrouping. Our results underscore the importance of leveraging physical insights for effective subgrouping or precise feature representation, to guide ML strategies when tackling complex materials science problems. Such an integrated approach has the potential to unlock new insights into material composition-property relationships and accelerate materials discovery in a wide range of applications beyond metallic glass formation.

Anionic Solid Solution MXene for Low‐Dosage NIR‐II Tumor Photothermal Therapy

AbstractDue to the deep biological penetrability and therapeutic depth, the photothermal therapy over second near‐infrared region (NIR‐II) is booming against deep‐seated tumors. Intensive endeavors are committed to looking for suitable photothermal agents (PTAs), but the progress seems not so satisfied toward the choice of PTA dosage. Herein, a comprehensive parameter, incident photon‐to‐thermal conversion coefficient (IPTCE), is used to evaluate the overall conversion of PTAs at different dosage, which will benefit for determining the optimized dosage of PTAs in pursuit of complete healing together with reduced long‐term damages of nanodrugs. To prove the possibility, a series of anionic solid solution MXenes are chosen as hosts due to their versatile chemical compositions and correspondingly tunable light response. By deconvoluting fundamental structure–composition–property relationships, anionic regulation with extra electron injection leads to tunable free carrier densities and enhanced NIR‐II harvesting. Ti3C1.23N0.77 with high‐level nitrogen exhibits extraordinary extinction coefficient (43.5 L g−1 cm−1) than other MXenes. The parameter of IPTCE can guide the choice of PTA concentration for complete photothermal healing in vitro and vivo. This proof‐of‐principle demonstration highlights synthetically tailoring of the light harvesting over NIR‐II biowindow for a given host material by anionic regulation and further optimizes tumor photothermal therapy at low dose.

Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors

Due to disordered structure, glasses present a unique challenge in predicting the composition–property relationships. Here, we present a low-complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set. By training on a large dataset (∼50,000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. In addition, we interpret the LCNN models using Shapely additive explanations to gain insights into the role played by the descriptors in governing the property. Finally, we demonstrate the universality of the LCNN models by predicting the properties of glasses with new components that were not present in the original training set. Altogether, the present approach provides a promising direction towards the accelerated discovery of novel glass compositions.

Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relationships remain poorly understood, impeding the pace of their discovery. Here, we use a large experimental dataset comprising ∼24 000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning (ML) models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Young’s modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models are the largest regarding the compositional space and the number of properties covered for ChGs. Further, we use Shapley additive explanations, a game theory-based algorithm, to explain the properties’ compositional control by quantifying each element’s role toward model predictions. This work provides a powerful tool for interpreting the model’s prediction and designing new ChG compositions with targeted properties. Finally, using the trained ML models, we develop several glass-selection charts that can potentially aid in the rational design of novel ChGs for various applications.

Two-Dimensional Cs2 AgInx Bi1-x Cl6 Alloyed Double Perovskite Nanoplatelets for Solution-Processed Light-Emitting Diodes.

Lead-free double perovskites have emerged as a promising class of materials with potential to be integrated into a wide range of optical and optoelectronic applications. Herein, we demonstrate the first synthesis of two-dimensional (2D) Cs2 AgInx Bi1-x Cl6 (0 ≤ x ≤ 1) alloyed double perovskite nanoplatelets (NPLs) with well controlled morphology and composition. The obtained NPLs show unique optical properties with the highest photoluminescence quantum yield of 40.1%. Both temperature dependent spectroscopic studies and density functional theory calculation results reveal that the morphological dimension reduction and In-Bi alloying effect together boost the radiative pathway of the self-trapped excitons of the alloyed double perovskite NPLs. Moreover, the NPLs exhibit good stability under ambient conditions and against polar solvents, which is ideal for all solution-processing of the materials in low-cost device manufacturing. We demonstrate the first solution-processed light-emitting diodes using the Cs2 AgIn0.9 Bi0.1 Cl6 alloyed double perovskite NPLs as the sole emitting component, showing luminance maximum of 58cd/m2 and peak current efficiency of 0.013cd/A. Our study sheds light on morphological control and composition-property relationships of double perovskite nanocrystals, paving the way toward ultimate utilizations of lead-free perovskite materials in diverse sets of real-life applications. This article is protected by copyright. All rights reserved.

Chiroptical studies on nanoparticle-liquid crystal composites

ABSTRACT Chiroptical material is an intriguing and rapidly evolving field in nanomaterial research to alter and improve the optical interaction of hybrid composites with incident light. In this regard, new molecular design strategies and new self-assembled structures of chiral nanoparticles (NPs) or nanorods (NRs) in composition with liquid crystal (LC) matrix have been developed for technological advancement in displays, sensors, development of hybrid materials for optical applications and others. With the emphasis on the chiroptical activities of NPs or NRs in LC systems, this review provides insight into the structure-property and composition-property relationships governing NP-LC interactions, which may point to new directions for major improvements of chiral nanomaterial research.

Composition-Property Relationships of pH-Responsive Poly[(2-vinylpyridine)-co-(butyl methacrylate)] Copolymers for Reverse Enteric Coatings.

The taste-masking of bitter-tasting active pharmaceutical ingredients is key to ensuring patient compliance when producing oral pharmaceutical formulations. This is generally achieved via the incorporation of pH-responsive, reverse enteric polymers, that prevent the dissolution of the formulation in the oral environment, but rapidly mediate it within the gastric environment. Reverse enteric polymers are commonly applied as coatings on oral dosage forms via spray atomisation (e.g., fluidised-bed spray coating), and generally exhibit the most efficient taste-masking. However, currently used reverse enteric coatings require high mass gains (% w/w) during coating to mediate taste-masking, and thereby exhibit delayed release within the gastric environment. Therefore, there remains a need for the development of new reverse enteric coatings, that can efficiently taste-mask at low mass gains and maintain rapid release characteristics within the gastric environment. Herein we report the synthesis and evaluation of a series of addition copolymers of 2-vinylpyridine and butyl methacrylate, methyl methacrylate and isobornyl methacrylate. The thermal, solubility, and water absorption properties of the copolymers were effectively tuned by altering the mol% fraction of the constitutive monomers. Based on their physical properties, selected copolymers were preliminarily evaluated for their compatibility with fluidised-bed spray coating, and effectiveness as taste-masking reverse enteric coatings. The copolymers poly[(2-vinylpyridine)-co-(butyl methacrylate)] (mol% ratio 40:60) and poly[(2-vinylpyridine)-co-(butyl methacrylate)-co-(methyl methacrylate)] (mol% ratio 40:50:10) were found to exhibit excellent taste-masking properties following fluidised-bed spray coating onto Suglets® sugar spheres. Suglets® bearing a film coating of either copolymer (5.2-6.5% w/w mass gain) were found to effectively impede the release of a model drug formulation for up to 72 h in a simulated salivary environment, and rapidly release it (<10 min) within a simulated gastric environment. The results demonstrated the potential of poly[(2-vinylpyridine)-co-(butyl methacrylate)] copolymers to form effectively taste-masked, reverse enteric dosage forms, and suggested that these copolymers may provide improved performance compared to currently available polymers.

Design of Atomic Ordering in Mo2Nb2C3Tx MXenes for Hydrogen Evolution Electrocatalysis

The need for novel materials for energy storage and generation calls for chemical control at the atomic scale in nanomaterials. Ordered double-transition-metal MXenes expanded the chemical diversity of the family of atomically layered 2D materials since their discovery in 2015. However, atomistic tunability of ordered MXenes to achieve ideal composition-property relationships has not been yet possible. In this study, we demonstrate the synthesis of Mo2+αNb2-αAlC3 MAX phases (0 ≤ α ≤ 0.3) and confirm the preferential ordering behavior of Mo and Nb in the outer and inner M layers, respectively, using density functional theory, Rietveld refinement, and electron microscopy methods. We also synthesize their 2D derivative Mo2+αNb2-αC3Tx MXenes and exemplify the effect of preferential ordering on their hydrogen evolution reaction electrocatalytic behavior. This study seeks to inspire further exploration of the ordered double-transition-metal MXene family and contribute composition-behavior tools toward application-driven design of 2D materials.

High-Entropy Materials: Controllable Synthesis, Deep Characterization, Electrochemical Energy Application, and Outlook

High-entropy materials (HEM) are expected to make new advances in electrocatalysis and energy storage by virtue of their broad compositional tunabilities, high-temperature stabilities, and high chemical stabilities. In recent years, HEM have been investigated extensively in these fields, prompting researchers to investigate the relationships among structure, composition, and electrochemical performance. This paper systematically reviews the recent advances of HEM, focusing on the controlled syntheses, atomic-scale structural analyses, progresses in electronic properties, and quantum calculations. The electrochemical performances of HEM in several representative reactions and batteries are then further discussed to demonstrate their structure–composition–property relationships. Finally, the development prospects of HEM in terms of controllable syntheses, deep characterizations and analyses, and applications are discussed and foreseen.

Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning

Despite the use of inorganic glasses for more than 4500 years, the composition–property relationships in these materials remain poorly understood. Here, exploiting largescale experimental data and machine learning, we develop composition–property models for twenty five properties, which are interpreted using game-theoretic concepts. Specifically, we use a dataset consisting of ∼275,000 glass compositions comprising of 221 different components and 25 properties. This is by far the largest model developed in the literature. The analysis reveals that the glass components, such as network formers, modifiers, and intermediates, play distinct roles in governing the optical, physical, electrical, and mechanical properties of glasses. Interestingly, these components exhibit interdependence, the magnitude of which is different for different properties. While the physical origins of some of these interdependencies could be explained using known phenomena, the majority of the remaining ones remain to be explored. Thus, our work paves the way for decoding the “glass genome”, which can provide the recipe for discovering novel glasses while also shedding light on the fundamental factors governing the composition–structure–property relationships.

Investigation of Multicomponent Fluoridated Borate Glasses through a Design of Mixtures Approach.

Due to their enhanced dissolution, solubility and reaction speed, borate glasses offer potential advantages for the design and development of therapeutic ion-release systems. However, the field remains poorly understood relative to traditional phosphosilicate and silicate bioglasses. The increased structural complexity and relative lack of published data relating to borates, particularly borofluorates, also decreases the accuracy of artificial intelligence models, which are used to predict glass properties. To develop predictive models for borofluorate networks, this paper uses a design of mixtures approach for rapid screening of composition–property relationships, including the development of polynomial equations that comprehensively establish the predictive capabilities for glass transition, density, mass loss and fluoride release. A broad range of glass compositions, extending through the boron anomaly range, were investigated, with the inclusion of 45 to 95 mol% B2O3 along with 1–50 mol% MgO, CaO and Na2O as well as 1–30% KF and NaF. This design space allows for the investigation of the impact of fluorine as well as mixed alkali–alkaline earth effects. Glass formation was found to extend past 30 mol% KF or NaF without a negative impact on glass degradation in contrast to the trends observed in phosphosilicates. The data demonstrates that fluoroborate materials offer an exceptional base for the development of fluoride-releasing materials.