Multicomponent Materials Research Articles

Design and Computational Validation of γ-Ray Shielding Effectiveness in Heavy Metal/Rare Earth Oxide-Natural Rubber Composites.

This study involved the preparation of natural rubber-based composites incorporating varying proportions of heavy metals and rare earth oxides (Sm2O3, Ta2O5, and Bi2O3). The investigation analyzed several parameters of the samples, including mass attenuation coefficients (general, photoelectric absorption, and scattering), linear attenuation coefficients (μ), half-value layers (HVLs), tenth-value layers (TVLs), mean free paths (MFPs), and radiation protection efficiencies (RPEs), utilizing the Monte Carlo simulation software Geant4 and the WinXCom database across a gamma-ray energy spectrum of 40-150 keV. The study also compared the computational discrepancies among these measurements. Compared to rubber composites doped with single-component fillers, multi-component mixed shielding materials significantly mitigate the shielding deficiencies observed with single-component materials, thereby broadening the γ-ray energy spectrum for which the composites provide effective shielding. Subsequently, the simulation outcomes were juxtaposed with experimental data derived from a 133Ba (80 keV) γ-source. The findings reveal that the simulated results align closely with the experimental observations. When compared to the WinXCom database, the Geant4 software demonstrates superior accuracy in deriving radiation shielding parameters and notably enhances experimental efficiency.

Accelerated Microstructure-Mechanical Property Mapping of Multi-Component Structural Materials

Accelerated Microstructure-Mechanical Property Mapping of Multi-Component Structural Materials

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.

First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

Charge transfer effects in (HfNbTiVZr)C—Shown by ab initio calculations and X‐ray photoelectron spectroscopy

AbstractConsidering charge transfer effects and the variability of the bonding between elements with different electronegativity opens up a deeper understanding of the electronic structure and as a result many of the properties in high entropy‐related materials. This study investigates the importance of the diverse bonding and chemical environments when discussing multicomponent carbide materials. A combination of ab initio calculations and X‐ray photoelectron spectroscopy (XPS) was used to investigate the electronic structure of multicomponent thin films based on the (HfNbTiVZr)C system. The charge transfer was quantified theoretically using relaxed and nonrelaxed multicomponent as well as binary carbide reference structures, employing a fixed sphere model. High‐resolution XPS spectra from (HfNbTiVZr)C magnetron‐sputtered thin films displayed core‐level binding energy shifts and broadening effects as a result of the complex chemical environment. Charge transfer effects and a changed electronic structure in the multicomponent material, compared with the reference binary carbides, are observed both experimentally and in the density functional theory (DFT) simulations. The observed effects loosely follow electronegativity considerations, leading to a deviation from an ideal solid solution structure assuming nondistinguishable chemically equivalent environments.

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach.

Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long research cycles for discovering new materials. Herein, a two-step machine learning (ML) assisted approach is proposed to design heat-resistant co-cured resins composed of polyimide (PI) and silicon-containing arylacetylene (PSA), that is, poly(silicon-alkyne imide) (PSI). First, two ML prediction models are established to evaluate the processability of PIs and their compatibility with PSA. Then, another two ML models are developed to predict the thermal decomposition temperature and flexural strength of the co-cured PSI resins. The optimal molecular structures and compositions of PSI resins are high-throughput screened. The screened PSI resins are experimentally verified to exhibit enhanced heat resistance, toughness, and processability. The research framework established in this work can be generalized to the rational design of other advanced multi-component polymeric materials.

Active learning streamlines development of high performance catalysts for higher alcohol synthesis

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h−1 gcat−1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

Development of novel protein-rich foods: Studies of composite hydrogels formed from potato proteins and gellan gums with different degrees of acylation

The textural attributes of mixed hydrogels formed from potato protein (PP) and gellan gum (GG) were examined for their potential application as novel protein-rich foods (NPFs). Initially, the properties of gels formed by the individual biopolymers were characterized using dynamic shear rheology, compression testing, and confocal fluorescence microscopy. Rheological analysis revealed that PP dispersions formed irreversible heat-set gels, whereas GG formed reversible cold-set gels at lower temperatures. The effects of adding PP to high acyl gellan gum (HA-GG) or low acyl gellan gum (LA-GG) solutions on the microstructure and rheology of the mixed gels formed were then examined. The PP/LA-GG composite gels had a highly heterogeneous microstructure consisting of protein-rich and polysaccharide-rich regions. In contrast, the PP/HA-GG composite gels had a more uniform microstructure. Interestingly, the incorporation of the PP weakened the PP/LA-GG composite gels (antagonism) but strengthened the PP/HA-GG composite gels (synergism), which was mainly attributed to their different impacts on gel microstructure. This study shows that semi-solid plant protein-dietary fiber composites can be formed with a variety of textural attributes by altering the nature and concentration of biopolymers used to formulate them. These mixed gels may be useful for the creation of NPFs. This study also highlights the complexity of the interactions between different food biopolymers and their impacts on food properties, highlighting the need for further research on understanding structure-function relationships in multicomponent biopolymer materials.

Study on the design method of multi-component industrial solid waste low carbon cementitious material with cement as the activator

The relationship between microstructure and mechanical properties of multi-component solid waste low-carbon cementitious materials has been widely pay attention to. However, industrial solid waste is a complex multi-component system with many variable factors, which makes it difficult to design the formulation of cementitious materials. This paper pioneered the application of machine learning (ML) models, algorithms and error rates to analyze the compressive and flexural strength of fly ash-based pastes. Coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and a20-index were used to evaluate robustness. X-ray diffraction (XRD), scanning electron microscope (SEM) and Brunauer-Emmett-Taylor (BET) were carried out to analyze evolution of cementitious materials. The evaluation results of ML models exhibited that the Gradient boosting regression (GBR) model had the best determination parameters and a steep normal distribution fitting curve with an a20-index of 0.861. GBR model exhibited the best robustness. The key factors of fly ash-based cementitious materials were identified by Pearson's coefficient, which was benefit to determine the formulation of multi-component solid waste low-carbon cementitious materials. Furthermore, experiments also demonstrated that the optimum ratio of multi-component solid waste low carbon cementitious material was 10 % gypsum, 10 % metakaolin, 45 % fly ash, 15 % slag and 20 % cement, respectively. It was worth noting that the compressive strength of this kind of multi-component solid waste low-carbon cementitious materials reached 35 MPa, which was superior to the mechanical properties of P·O 32.5 cement. The results of phase, SEM images and pore structure distribution showed that the synergistic effect of the multi-component solid waste materials effectively filled the material voids and also facilitated the formation of a variety of gelatinous materials through gelling reactions in the late stage (14–28 d). This work will promote the resource utilization of industrial solid waste, contribute to carbon reduction, and can accelerate the green revolution of concrete.

Engineering Single Component Luminogens to Multicomponent Charge‐transfer Co‐crystal Substrate as New Frontiers for Sensitive SERS Detection

AbstractOrganic charge‐transfer (CT) co‐crystals have demonstrated remarkable physical properties and have found applications in numerous fields. Yet their utility as a Surface‐Enhanced Raman Spectroscopy (SERS) substrate, a powerful and versatile analytical tool, has never been explored. Herein, three twisted molecular donors are synthesized, that exhibit well‐controlled switchable optical properties including aggregation‐induced emission (AIE), mechanochromic luminescence (MCL), and color‐specific polymorphism. Rapid production of charge‐transfer co‐crystals is also established with a π‐acceptor TCNQ and utilized conceptually as a SERS substrate for methylene blue (MB) detection, exhibiting a very high enhancement factor of 109 and limit of detection of 10−13 m, respectively, due to the presence of low‐lying excited state, exhibit an 80% CT character, originating from the HOMO of the co‐crystal and interacting with the LUMO of the MB molecule. This approach using CT co‐crystals as a SERS substrate presents newer frontiers that require minuscule levels of rapid detection and impact allied areas, helping us understand and optimize the fascinating properties of such multicomponent materials for newer technologies.

Investigation of the CMAS corrosion resistance of high-entropy oxides through B-site cation doping

High-temperature erosion caused by calcium magnesium aluminum silicate (CMAS) poses a significant challenge to the advancement of thermal barrier coatings (TBCs). In this investigation, four multi-component A2B2O7 type oxide TBC materials, namely Eu2(Y0.2Ce0.2Zr0.2Hf0.2Ta0.2)2O7, Eu2(Y0.2Yb0.2Ce0.2Nb0.2Ta0.2)2O7, Eu2(Y0.2Yb0.2Zr0.2Nb0.2Ta0.2)2O7, and Eu2(Y0.2Yb0.2Hf0.2Nb0.2Ta0.2)2O7, were synthesized using the solid-state method. We investigated the corrosion behavior of molten CMAS on the samples at 1300 °C for durations of 1 h, 20 h, and 50 h. The resulting corrosion products consist of apatite and cubic fluorite structures. Our analysis reveals that as the ionic radius of RE3+ increases, the stability of the apatite structure improves. Furthermore, molten CMAS exhibits varied wetting behaviors on different substrates at the same temperature. A higher contact angle suggests that the CMAS melt will encounter difficulty in diffusing and penetrating the substrate surface. This research will contribute to the development of TBC materials with enhanced resistance to CMAS corrosion.

Hybrid laser-ECM processing of advanced materials in neutral electrolyte without aggressive reagents: Machining performance, removal characteristics and sustainability aspects

The growing need for more energy efficient systems has led to advent of hard and corrosion resistant materials which are difficult to machine with both conventional and non-conventional machining techniques. Although ECM offers athermal and force-free dissolution, the workpiece surface integrity in multicomponent materials is limited with ECM due to several multi-physical phenomena occurring on the surface like selective phase dissolution, passivation, pitting, particle breakout, etc. To tackle these challenges, aggressive acidic and basic reagents are added to the electrolytes. Although these reagents improve the machining performance, they are not sustainable for the environment and human life as they generate harmful waste which is difficult to dispose and recycle. Furthermore, the handling of these aggressive electrolytes requires specialized coatings, resins, filters, catalysts, etc. to protect the machine tools and equipment from damage.Hybrid laser-ECM (LECM) is a relatively novel drill-mill processing technique to process these advanced materials while using neutral electrolytes, thereby potentially reducing the negative impact of using acidic/basic reagents in electrolytes. Under controlled conditions and proper laser alignment, LECM can simultaneously apply laser and EC process energies in the same machining zone to provide synergistic benefits manifesting in terms of dominance of kinetics and thermodynamics over pure electrodynamics in ECM. This provides multi-fold benefits in improving passivation weakening and homogenize multiphase dissolution. Therefore, this study investigates for the first time the machining performance, removal behaviour, reaction products and power/resource consumption of LECM in neutral electrolyte in comparison to ECM in various neutral and basic electrolytes. The NbC–Ni cermet was used as the workpiece material, which is a cobalt-free alternative to conventional WC-Co. The results revealed that LECM in neutral NaNO3 compared to ECM with NaOH addition not only provided better Sa (2.02 μm, 24% ↓), M.R.R. (153 μg/s, 31% ↑), aspect ratio (0.048, 14% ↑) and similar passivation weakening capability while maintaining a nearly neutral pH (8) of the reaction products, but also reduced electrolyte usage by 23.6% with a 16.4% increase in power consumption. These aspects make LECM a sustainable and cost-effective candidate for processing modern passivating multiphase materials like newer cermets, super alloys, high entropy alloys, etc. with nearly same performance as achieved by using ECM with acidic/basic electrolytes. Further fundamental and applied research is needed in LECM to meet the industrial specifications.

Local Nanostructure in Multicomponent High-Entropy Materials

Multicomponent phase space contains extended regions of random or near-random multicomponent solid-solution single phases, stabilised by a relatively large configurational entropy of mixing that can often (though not always) suppress compound formation between the different atomic species. The present paper shows that there are very extensive variations of local nanostructure, local atomic clusters and associated local lattice strains within multicomponent high-entropy solid-solution single phases such as the fcc Cantor alloys, bcc Senkov alloys and rock-salt-structured Rost mono-oxides, even when there is no short-range ordering, i.e. even when the solid solution is completely random or ideal. There are, for instance, many billions of different local nanostructures and different local atomic clusters in equiatomic five-component fully random solid-solution single-phase materials such as the original fcc Cantor alloy CrMnFeCoNi and the original bcc Senkov alloy VNbMoTaW, extending over distances of many microns, with associated fluctuating hydrostatic and shear lattice strains of several percent. The number and extent of the variations in local nanostructure, atomic clusters and lattice strains increase dramatically to even higher values with increasing number of components in the material. The present paper also shows that there are similar variations in local nanostructure, local atomic clusters and associated local lattice strains surrounding point defects such as vacancies, line defects such as dislocations and planar defects such as grain boundaries and external surfaces, influencing many important material properties such as diffusion, plastic flow, recrystallisation, grain growth and catalysis. The number and extent of the variations in local nanostructure, atomic clusters and lattice strains make it difficult to have too much confidence in structures and properties of multicomponent high-entropy materials calculated using ab initio and other atomistic computer modelling techniques, since these techniques are restricted to relatively small numbers of atoms and are unable to sample effectively the full range of local structures and properties.

Strategy for the Preparation of ZnS/ZnO Composites Derived from Metal-Organic Frameworks toward Lithium Storage.

The synergistic effect between multicomponent electrode materials often makes them have better lithium storage performance than single-component electrode materials. Therefore, to enhance surface reaction kinetics and encourage electron transfer, using multicomponent anode materials is a useful tactic for achieving high lithium-ion battery performance. In this article, ZnS/ZnO composites were synthesized by solvothermal sulfidation and calcination, with the utilization of metal-organic frameworks acting as sacrificial templates. From the point of material design, both ZnS and ZnO have high theoretical specific capacities, and the synergistic effect of ZnS and ZnO can promote charge transport. From the perspective of electrode engineering, the loose porous carbon skeleton that results from the calcination of metal-organic frameworks can enhance composite material conductivity as well as full electrolyte penetration and the area of contact between the electrolyte and active material, all of which are beneficial to enhancing lithium storage performance. As expected, ZnS/ZnO anode materials displayed remarkably high specific capacities and outstanding performance at different rates. Combining material design and electrode engineering, this paper provides another idea for preparing anode materials with excellent lithium storage properties.

Quantitative selection of sample structures in small-angle scattering using Bayesian methods.

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

High-Entropy Transition Metal Phosphorus Trichalcogenides for Rapid Sodium Ion Diffusion.

High-entropy strategies are regarded as a powerful means to enhance performance in energy storage fields. The improved properties are invariably ascribed to entropy stabilization or synergistic cocktail effect. Therefore, the manifested properties in such multicomponent materials are usually unpredictable. Elucidating the precise correlations between atomic structures and properties remains a challenge in high-entropy materials (HEMs). Herein, atomic-resolution scanning transmission electron microscopy annular dark field (STEM-ADF) imaging and four dimensions (4D)-STEM are combined to directly visualize atomic-scale structural and electric information in high-entropy FeMnNiVZnPS3. Aperiodic stacking is found in FeMnNiVZnPS3 accompanied by high-density strain soliton boundaries (SSBs). Theoretical calculation suggests that the formation of such structures is attributed to the imbalanced stress of distinct metal-sulfur bonds in FeMnNiVZnPS3. Interestingly, the electric field concentrates along the two sides of SSBs and gradually diminishes toward the two-dimensional (2D) plane to generate a unique electric field gradient, strongly promoting the ion-diffusion rate. Accordingly, high-entropy FeMnNiVZnPS3 demonstrates superior ion-diffusion coefficients of 10-9.7-10-8.3 cm2 s-1 and high-rate performance (311.5 mAh g-1 at 30 A g-1). This work provides an alternative way for the atomic-scale understanding and design of sophisticated HEMs, paving the way for property engineering in multi-component materials.

Visual Computation of Material Microstructure and Deformation.

The experimentally obtained material microstructure can be used to calculate a material's properties and identify microstructure-property relationships. The key procedure to enable this is to interpret the observed microstructure accurately. This work reports on a newly developed computational method to serve such a purpose. The method is based on cubic spline interpolation and a simple search algorithm. Parameterisation was accomplished via the comparison between its preliminary statistical results and the information in a phase diagram. The method was applied to analyse the quenched microstructure of multicomponent and multiphase metallic-oxide materials. The importance of adequate parameterisation is demonstrated. The results provide a good explanation for the experimentally measured electric conductance behaviour. Further application of the method to the deformation of materials is discussed. The algorithms are directly available for the analysis of the three-dimensional microstructure of materials.

Stepwise Construction of MoS2@CoAl-LDH/NF 3D Core-Shell Nanoarrays with High Hole Mobility for High-Performance Asymmetric Supercapacitors.

Supercapacitors (SCs) have received widespread attention as excellent energy storage devices, and the design of multicomponent electrode materials and the construction of ingenious structures are the keys to enhancing the performance of SCs. In this paper, MoS2 nanorods were used as the carrier structure to induce the anchoring of CoAl-LDH nanosheets and grow on the surface of nickel foam (NF) in situ, thus obtaining a uniformly distributed MoS2 nanorod@CoAl-LDH nanosheet core-shell nanoarray material (MoS2@CoAl-LDH/NF). On the one hand, the nanorod-structured MoS2 as the core provides high conductivity and support, accelerates electron transfer, and avoids agglomeration of CoAl-LDH nanosheets. On the other hand, CoAl-LDH nanosheet arrays have abundant interfacially active sites, which accelerate the electrolyte penetration and enhance the electrochemical activity. The synergistic effect of the two components and the unique core-shell nanostructure give MoS2@CoAl-LDH/NF a high capacity (14,888.8 mF cm-2 at 2 mA cm-2) and long-term cycling performance (104.4% retention after 5000 charge/discharge cycles). The integrated MoS2@CoAl-LDH/NF//AC device boasts a voltage range spanning from 0 to 1.5 V, achieving a peak energy density of 0.19 mW h cm-2 at 1.5 mW cm-2. Impressively, it maintains a capacitance retention rate of 84.6% after enduring 10,000 cycles, demonstrating exceptional durability and stability.

Evaluation of the Energy Efficiency of the Process of Vibratory Mixing of Multicomponent Bulk Raw Material of Food Industries

Evaluation of the Energy Efficiency of the Process of Vibratory Mixing of Multicomponent Bulk Raw Material of Food Industries

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm−3 at an electric field of 5104 kV cm−1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Multifunctional ultralight magnetic Fe3O4@SiO2/Ti3C2Tx/rGO aerogel with efficient electromagnetic wave absorption and thermal management properties

The rational and effective fabrication of multicomponent materials, coupled with innovative microstructure design, remains a formidable challenge in achieving efficient electromagnetic wave absorption (EMA) behaviors to meet the requirement of rapid advances in electronics. Herein, a three-dimensional (3D) hierarchically porous Fe3O4/SiO2/Ti3C2Tx MXene/rGO (FSMRG) aerogels were obtained via a directional freeze-drying and subsequent thermal reduction strategy. It has been found that with the filling ratio of 5 wt%, the ultralight FSMRG-2 aerogel (7.42 mg cm−3) exhibits optimal EMA performances with a minimum reflection loss (RLmin) of −76.6 dB, and the corresponding maximal effective absorption bandwidth (EAB) is up to 5.36 GHz covering the frequency ranges from 12.48 GHz to 17.84 GHz. The outstanding EMA performances of FSMRG-2 aerogel can be attributed to the synergy effects from the multicomponent interfacial polarization, multiple loss pathways and favorable impedance matching as evidenced with the electromagnetic parameter analysis. In addition, the FSMRG-2 aerogel also displays excellent hydrophobicity and thermal insulation capabilities, rendering them suitable for application in complicated environments characterized by humid and high-temperature conditions. Therefore, this study presents a reliable and facile strategy for the precise construction of lightweight, efficient and multifunctional EMA materials.