High-entropy Alloy Composites Research Articles

Correlating Nitrate Adsorption with the Local Environments of FeCoNiCuZn High-Entropy Alloy Catalysts Using Machine Learning.

The electrocatalytic nitrate reduction to ammonia holds significant values for water remediations and energy applications, which quests for the development of highly effective catalysts with considerable stability and selectivity. Recently, high-entropy alloys (HEAs) are attracting growing attention for electrocatalytic processes. Nonetheless, studies of HEA-based nitrate reduction to ammonia are still at the early stage, and it remains unclear how the HEA compositions affect the adsorption and activation of the reaction intermediates. Herein, high-throughput density functional theory (DFT) calculations were integrated with machine learning to investigate the dependence of nitrate adsorption on the FeCoNiCuZn HEA structures. In particular, a total of 1268 different structures were sampled and constructed from the multidimensional configuration space, followed by the DFT calculations to investigate the Gibbs free energy of nitrate adsorption (i.e., ΔGNO3) on different surface microstructures. Four regression models were successfully developed, which can accurately predict ΔGNO3 using the HEA structures as the input features. Through the analysis of the feature importance, it was found that the active sites are crucial for nitrate adsorption; meanwhile, the local environments also play a considerable role. The dependence of the ΔGNO3 and adsorption geometries on the HEA compositions demonstrates that the compositional modulation of the HEA catalysts could be a promising avenue for facile adsorption and activation of reaction intermediates. Overall, this work will contribute to the probabilistic optimization of the HEA microstructures for enhanced electrochemical nitrate reduction.

Convolutional neural network based methodology for flexible phase prediction of high entropy alloys

ABSTRACT The advent of high entropy alloys has created a design space that is unfeasible to explore solely through experimentation, thus necessitating the use of computational methods, such as artificial neural networks. This study proposes a new architecture utilizing a convolutional layer to extract relevant elemental features from the alloy composition without limiting the model to specific elements by treating the elemental composition of the high entropy alloy in a similar manner to a row of pixels, using relevant elemental properties in lieu of color values. This convolutional model was able to predict the crystal structure of solid solutions in a high entropy alloy composition with an accuracy of 89.3% in a six-way classification and the formation of intermetallics with an accuracy of 91.4% during holdout validation, while being capable of accurately predicting the primary crystal structure of high entropy alloys containing elements the model was not trained on. Significant space still exists for further experimentation and improvement with this methodology, including augmenting the available datasets and implementation of additional convolutional layers. Due to the limited interpretability of the model architecture, care should be taken when inferring trends from the model prior to validation through experimental, or well-established computational methods.

Active learning of ternary alloy structures and energies

Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemical dealloying derived nanoporous FeCoNiCuTi high-entropy bifunctional electrocatalysts for highly efficient overall water splitting under alkaline conditions

In response to society’s urgent drive to significantly decrease its reliance on fossil fuels in the future, there has been continuing efforts to utilize hydrogen (H2) generated by water electrolysis as clean and sustainable substitute for the traditional fossil fuels. A significant challenge in water electrolysis revolves around creating affordable and efficient electrocatalysts to speed up the chemical reactions involved. Our new study introduces a bifunctional catalyst made from a high-entropy alloy (HEA), capable of efficiently catalyzing both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The findings reveal that a complex chemical composition of FeCoNiCuTi HEA can significantly modify its electronic structure, resulting in an impressive electrocatalytic performance. Specifically, FeCoNiCuTi HEA catalyst exhibits an overpotential of 64.9 mV at a current density of 10 mA cm−2 for hydrogen evolution with a Tafel slope of 36.81 mV dec−1 and double-layer capacitance (Cdl) of 34.1 mF cm−2. For oxygen evolution, HEA achieves an overpotential of 175 mV at 10 mA cm−2, a Tafel slope of 44.31 mV dec−1, exhibiting the performance superior to noble catalysts. Theoretical calculations support that the intricate chemical composition of high-entropy alloy plays vital role to enhance its electrocatalytic capabilities. This research represents a straightforward efficient method to create electrocatalysts for water electrolysis using non-noble metals by leveraging high-entropy effect.

Optimized deep learning regression assisted wear rate analysis of Cu–AlCoCrCuFe HEA composite

At present, composites have brought materials to a new era with superior characteristics. The proper selection of materials with optimal processing conditions is one of the prime factors in determining the efficiency of composites. This article introduces a new composite by fabricating a copper matrix with reinforced AlCoCrCuFe High Entropy Alloy (HEA). The ultimate theme of this research is to forecast the wear rate of the Cu–AlCoCrCuFe HEA composite. Using the pin on the disk, the wear test is carried out on varying combinations of parameters. Then Taguchi analysis is carried out to get the precise fit parameters. The wear rate mainly depends on the percentage of HEA, sliding distance, sliding velocity, and the load applied. Correlation analysis is performed to determine the exact parameter determining the optimal wear rate and the coefficient of friction. After feature extraction, the parameters are optimized, and the neural network regression is given the optimized parameters. The network has been trained, and predictions are made using it. The model successfully predicts the wear rate, as evidenced by the declining RMSE of 0.28 and rising R2 values up to 92%. The analysis shows a significant decline in the wear rate with HEA additions. In addition, the wear rate increases with a rise in load, sliding velocity, and sliding distance.

Strengthening Fe1.4Co0.8Ni0.8Cr high-entropy alloy composites via in-situ synthesis of TiC: Perspective from experiments and first principles calculations

Achieving a harmonious balance between strength and plasticity is a long-standing problem in single-phase face-centered-cubic (fcc) high-entropy alloys (HEAs). Herein, in-situ TiC ceramic particle reinforced fcc-structured Fe1.4Co0.8Ni0.8Cr HEAs is designed and investigated by combining the experiments and first-principate calculations. The results show that the y (TiC)–Fe1.4Co0.8Ni0.8Cr (y = 0, 2.5, 5.0, 7.5 and 10.0 %, y: volume fraction) composites consist of fcc phase with submicron/micron TiC after the co-addition of Ti and C element, and the formation of TiC facilitates to refine the microstructure of the composites. Such microstructural evolutions significantly enhance the mechanical performances of the composites. Particularly, the 7.5 % (TiC)–Fe1.4Co0.8Ni0.8Cr composite exhibits a high yield strength of ∼562.7 ± 7.7 MPa and tensile strength of ∼971.3 ± 5.4 MPa, which are respectively increased by 169.0 % and 88.3 % as compared with the Fe1.4Co0.8Ni0.8Cr based alloy, yet with the fracture elongation decreases to 35.9 ± 0.3 %. The increment in yield strength is mainly driven from the combined effect of the grain refinement strengthening, dislocation strengthening and precipitation strengthening. Density functional theory (DFT) results reveal that the increase of C–Ti bonds and the formation of covalent bonds in the 7.5 % (TiC)–Fe1.4Co0.8Ni0.8Cr composite lead to relatively higher strength and lower ductility, which are well agreed with the experimental results.

The Effect of Laser Remelting during SLM on Microstructure and Mechanical Properties of CoCrFeNiNb0.25.

A sub-eutectic high-entropy alloy composed of CoCrFeNiNb0.25 was prepared using a combination of mechanical powder mixing and selective laser melting (SLM). The mechanical properties of the alloy were enhanced by employing an interlayer laser remelting process. This study demonstrates the feasibility of using mechanical mixing and SLM to form an CoCrFeNiNb0.25 alloy. The interlayer laser remelting process can effectively promote the melting of Nb particles introduced by mechanical mixing, release the stresses near the unfused Nb particles, and reduce their degradation of the specimen properties. The results indicate that the CoCrFeNiNb0.25 alloy, prepared using the interlayer laser remelting process, had an average microhardness of 376 HV, a tensile strength of 974 MPa, and an elongation at break of 10.51%. This process offers a viable approach for rapidly adjusting the composition of high-entropy alloys for SLM forming.

Phase transformation of AB5 to AB2 type phase on substitution of Mn with Zr in TiVCoNi (ZrxMn2-x) (x = 0, 0.3, 0.6, 1.0) high entropy alloys

In this present investigation, we discussed the synthesis, microstructure, and phase transformation from BCC (AB5) to C14 Laves (AB2) phase in TiVCoNi (ZrxMn2-x) (x = 0, 0.3, 0.6, 1.0) high entropy alloys on substituting Mn with Zr. These high entropy alloys were synthesized using a 35 kW radio frequency induction furnace under an argon atmosphere. At x = 0 (i.e. in quinary HEA), the single BCC phase was observed, whereas at x = 1 (i.e. in hexanery HEA), the single C14 Laves phase was found to be stable. The calculations of the thermodynamic parameters were done with the help of Miedema's semi-empirical model and other models available in the literature. TiVCoNi (ZrxMn2-x) high entropy alloys exhibited mixing enthalpy at x = 0, 0.3, 0.6, 1 as −15.6 kJ/mol, −18.1 kJ/mol, −22.8 kJ/mol, −25.3 kJ/mol respectively. The corresponding atomic size mismatch was found to be 10.03%, 11.85%, 10.18%, and 9.86%. It is established that on replacing Mn with Zr, the mixture of the BCC and Laves phase was observed, which was converted into a single C14 intermetallic Laves phase at equiatomic composition, i.e., x = 1. Further, the study of surface morphology and elemental composition of these as-cast high entropy alloys gives us the idea that hardness increases on substitution of Mn in BCC (at x=0) phased-based; TiVCoNi (ZrxMn2-x) (x = 0, 0.3, 0.6, 1.0) high entropy alloys due to the evolution of harder C14 Laves phase.

The Effect of Heat Treatment and Tungsten Content on the Structure, Phase Composition, and Corrosion Resistance of High-Entropy Alloys of the Fe–Cr–Ni–Mo–W System

The Effect of Heat Treatment and Tungsten Content on the Structure, Phase Composition, and Corrosion Resistance of High-Entropy Alloys of the Fe–Cr–Ni–Mo–W System

Microstructure, mechanical and tribological properties of Mg/CoCrFeNiMoTi high entropy alloy composites produced via FSP

In this study, multi-pass friction stir processing (FSP) was used to produce magnesium (Mg) matrix composites reinforced with CoCrFeNiMoTi high entropy alloy (HEA) particles. The effect of the HEA reinforcements on microstructure evaluation, mechanical properties (hardness and tensile) and wear performance was investigated. The composites were produced with similar pass number (three passes) and tool rotation speed (1250 rpm) and different tool transverse speeds (25 and 50 mm/min) and compared with the Mg matrix without reinforcing particles. XRD results indicated no significant reaction at the interface of the HEA and the matrix during FSP. Microstructure results showed the distribution improvement of the HEA reinforcements and elimination of lamellar structure (inhomogeneous structure) with the decrease of the tool transverse speed from 50 to 25 mm/min. Microstructures of all specimens illustrated four microstructural regions including stirring zone, thermo-mechanical affected zone, heat-affected zone and base metal. The average grain size of the stir zone reduced significantly from 12 µm for the Mg-50 to 2.5 µm and 1.6 µm for the Mg/HEAs-50 and Mg/HEAs-25 composites. The hardness value was increased from 34 HV for the base alloy to 113 HV for the FSPed composite at 25 mm/min (232 % improvement). The maximum yield and tensile strengths were obtained as 86.4 MPa and 114 MPa for the FSPed composite at 25 mm/min which presented enhancement of 92 % and 43 %, respectively as compared with the FSPed Mg alloy. The wear resistance results showed the reduction of the friction coefficient and wear rate with the addition of the HEA reinforcements with a considerable decrease in wear debris, groove depth, micro-cracks and delamination. Comparative plots confirmed that the hardening and strengthening effects of the HEA reinforcements were significantly higher than other reinforcements in Mg matrix composites.

Effect of YSZ Particle Size and Content on Microstructure, Mechanical and Tribological Properties of (CoCrFeNiAl)1−x(YSZ)x High Entropy Alloy Composites

Effect of YSZ Particle Size and Content on Microstructure, Mechanical and Tribological Properties of (CoCrFeNiAl)1−x(YSZ)x High Entropy Alloy Composites

Mechanical and tribological characteristics of nickel-rich CoCrCuxFeNi2 high entropy-alloys

This research explores the potential to enhance the copper solubility limit in high-entropy alloys (HEAs) within the CoCrCuFeNi system by increasing the nickel content twofold and applying additional heat treatment. The CoCrCuxFeNi2 HEAs were synthesized through mechanical alloying of elemental powders followed by hot pressing. The study investigated the microstructure and phase composition of CoCrCuxFeNi2 HEAs in relation to varying copper concentrations (x = 0; 0.25; 0.5; 0.75; 1.0). The evaluation of the alloy matrix's chemical composition, which is based on the FCC solid solution, enabled the determination of copper solubility. It was found that doubling the nickel content, relative to the equiatomic ratio, facilitated the formation of HEAs with a homogenous FCC structure for copper concentrations up to x ≤ 0.75. Further heat treatment of these HEAs resulted in an enhanced copper solubility of up to 17.5 at.%. The mechanical and tribological properties of CoCrCuxFeNiy HEAs were also assessed, revealing significant improvements in tensile strength (ranging from 910 to 1045 MPa) and hardness (285–395 HV) for the CoCrCuxFeNi2 alloys. Despite the increased copper solubility limit, the heat treatment process caused a decline in mechanical properties by 35–50 %, attributed to grain size enlargement to 5.5 μm. The CoCrCu0.75FeNi2 and CoCrCuFeNi2 alloys exhibited the lowest wear rates when tested against Al2O3 counterbody, with wear rates of 1,58·10–5 and 1,48·10–5 mm3/(N·m), respectively.

Robust wear performance of graphene-reinforced high entropy alloy composites

The expected excellent tribological performance of high-entropy alloys (HEAs) is rarely achieved due to the absence of self-lubricating ability on the worn surface. In particular, the situation is more challenging in HEAs with face-centered cubic structure because of the inferior yield strength when used in dry sliding conditions. Unlike conventional soft-solid lubricants, which deteriorate the mechanical properties of HEA matrix, in this study we demonstrate in a protocol model alloy that the proper incorporation of few-layer graphene as reinforcement in CoCrFeNiMn can lead to both surface strengthening and lubrication. This is achieved by a partially chemical interface reaction between graphene and HEA via a tunable fabrication process, resulting in a reduction of wear rate and friction coefficient of 86.03% and 23.87%, respectively. As a result of the interfacial in-situ carbide strengthening together with the self-lubrication of graphene, this tailored composite structure also exhibits superior tribological properties over most HEA-based self-lubrication composites. Besides, the subsurface structure evolution and deformation mechanisms that influence the wear resistance are systematically clarified through microscopic exploration and atomistic simulation. The present study presents an effective strategy for the development of innovative HEA composites suitable for safety-critical applications, which overcomes the inherent compromise and achieves exceptional tribological performance, i.e., self-lubricating and anti-wear.

Characterization of FeCoNiCr high-entropy alloys manufactured by powder metallurgy technique

This study explores the phase constitution, thermal behavior, magnetic characteristics, and mechanical properties of FeCoNiCrCux high-entropy alloys (HEAs) produced via powder metallurgy, focusing on varying copper ratios (0–20%). The investigation seeks to understand how the introduction of copper influences the phase composition and properties of FeCoNiCr HEAs, particularly regarding changes in phase structure and mechanical behavior. The phase constitution was analyzed using field emission scanning electron microscopy (SEM) and X-ray diffraction (XRD). Thermal expansion and differential scanning calorimetry (DSC) were employed to study thermal behavior, while vibrating sample magnetometers (VSM) were utilized to assess magnetic properties. Mechanical properties were evaluated through hardness testing. The analysis reveals a shift in phase composition from a single-phase FCC structure to a dual-phase configuration comprising FCC and copper-rich FCC phases as copper content increases. Thermal and magnetic properties were characterized, demonstrating the alloy's behavior under varying conditions. Mechanical tests showed a decrease in hardness with increasing copper content. This study contributes to the understanding of how copper addition affects the phase constitution, thermal behavior, magnetic properties, and mechanical strength of FeCoNiCr HEAs. The observed changes in phase composition and mechanical properties offer insights for tailoring the properties of HEAs for specific applications.

Elucidating the tensile work hardening behaviour of precipitate containing Al0.3Co1.5CrFeNi1.5Ti0.2 high entropy alloy

A significant amount of work has been performed, since the last decade, on designing novel high entropy alloy (HEA) compositions. However, the effort on concluding a universal mechanism on the strain hardening behavior of these alloys with precipitates is limited. The current study is designed with an aim to comprehend the mechanical behavior of Al0.3Co1.5CrFeNi1.5Ti0.2 HEA in solution treated and aged conditions. The results show that the peak aged condition of sample contains two types of precipitates – B2 and L12 (γ′) of approximately 800 nm and 40 nm size, respectively. They are found to be responsible for a significant increase in strength in aged samples than solution treated ones. Deformation was found to occur mainly by planar slip on the {111} primary slip planes. Noticeable strain hardening behavior was attributed to the formation of stacking faults (SFs) and Lomer-Cottrell (LC) locks, inhibiting the dislocation motion. Numerous paired dislocations formed due to inhibition of slip by the ordered γ′ precipitates, coexist with a few Orowan loops in the peak-aged alloy after deformation. However, the primary strengthening mechanism was found to be the shearing of γ′ precipitates by partial dislocations. Shearing of nanosized γ′ precipitates by partial dislocations is energetically favourable for the system than shearing by full dislocations. Overall, this study enhances the understanding of the precipitation strengthening and strain hardening mechanisms in high entropy alloys.

An efficient approach to develop and screen out high-entropy alloy composition with high performance for biomedical application

High-entropy alloys (HEAs) have great prospects for applications as biomedical materials due to their excellent properties of high strength and corrosion resistance. In this study, a co-deposition technology with two targets was used to prepare 16 independent Ti-Zr-Nb-Ta HEA samples with composition gradient distribution. Afterwards, these samples' chemical compositions, structures, and properties were systematically studied, and it was found that there was a non-linear relationship between the compositions, structures, and properties. The chemical composition of Ti12.50Zr11.56Nb12.71Ta46.62O16.61 HEA sample with superior comprehensive properties was efficiently screened out, and its nanohardness, open circuit potential (OCP) value, and corrosion current density were identified to be 12.91 GPa, −0.37 VSCE, and 3.91 × 10−9 A·cm−2, respectively. This parallel co-deposition method offered a new efficient approach to develop advanced biomedical HEAs.

Enhancing tensile properties of laser additive manufactured FeCoNiCrMn high entropy alloy via adding Fe-based metallic glass

It has always been difficult to significantly improve the tensile strength of FeCoNiCrMn high entropy alloy (HEA) by traditional technical methods. Here, high-strength Fe-based metallic glass (MG) was precisely selected as the strengthening phase to reinforce the mechanical properties of FeCoNiCrMn HEA fabricated by laser additive manufacturing (LAM) technology. The results showed that although the addition of Fe-based MG with 5 wt% did not change the original phase composition of HEA, it facilitated grain refinement to a great extent. Of special significance was that the strength and ductility of HEA-MG exhibited a synergistic enhancement (σy, σUTS and εf were increased by 23.8 %, 28.4 % and 31.0 %, respectively) after Fe-based MG addition, deriving from the grain refinement, increased the dislocation density and formation of deformation twins.

Recent progress in carbide‐based composite materials fabricated by laser additive manufacturing processes

AbstractFabricating and introducing new materials with new features is one of the most important goals for many scientific groups. Today, additive manufacturing (AM) is known as one of the new production methods, which can create an excellent opportunity for achieving this goal. As one of the ongoing production processes, the laser AM (LAM) methods have the proper capacity for producing new advanced materials like carbide, nitride, and boride‐based materials. Hence, this work aims to review carbide‐based materials produced by LAM techniques with the composite viewpoint. These materials can act as a matrix, reinforcement, or tertiary component in advanced composites. With this viewpoint, these composite materials were introduced in eight separate sections, which are tungsten carbide, titanium carbide, silicon carbide, niobium carbide, chrome carbide, vanadium carbide, and boron carbide, as well as high entropy alloy composites.

Tailoring Local Chemical Ordering via Elemental Tuning in High-Entropy Alloys.

Due to the large multi-elemental space desired for property screening and optimization, high-entropy alloys (HEAs) hold greater potential over conventional alloys for a range of applications, such as structural materials, energy conversion, and catalysis. However, the relationship between the HEA composition and its local structural/elemental configuration is not well understood, particularly in noble-metal-based HEA nanomaterials, hindering the design and development of nano-HEAs in energy conversion and catalysis applications. Herein, we determined precise atomic-level structural and elemental arrangements in model HEAs composed of RhPtPdFeCo and RuPtPdFeCo to unveil their local characteristics. Notably, by changing just one constituent element in the HEA (Rh to Ru), we found dramatic changes in the elemental arrangement from complete random mixing to a local single elemental ordering feature. Additionally, we demonstrate that the local ordering in RuPtPdFeCo can be further controlled by varying the Ru concentration, allowing us to toggle between local Ru clustering and distinct heterostructures in multicomponent systems. Overall, our study presents a practical approach for manipulating local atomic structures and elemental arrangements in noble-metal-based HEA systems, which could provide in-depth knowledge to mechanistically understand the functionality of noble-metal-based HEA nanomaterials in practical applications.

Machine learning assisted design of high-entropy alloys with ultra-high microhardness and unexpected low density

High-entropy alloys (HEAs) have attracted considerable attention for their exceptional microstructures and properties. Discovering new HEAs with desirable properties is crucial, but traditional design methods are laborious and time-consuming. Fortunately, the emerging Machine Learning (ML) offers an efficient solution. In this study, composition-microhardness data pairs from various alloy systems were collected and expanded using a Generative Adversarial Network (GAN). These data pairs were converted into empirical parameter-microhardness pairs. Then Active Learning (AL) was employed to screen the Al-Co-Cr-Cu-Fe-Ni system and identify the eXtreme Gradient Boosting (XGBoost) as the optimal ML master model. Millions of data training iterations employing the XGBoost sub-model and accuracy evaluations using the Expected Improvement (EI) algorithm establish the relationship between HEA compositions and microhardness. The proposed sub-model aligns well with experimental data, wherein four Al-rich compositions exhibit ultra-high microhardness (>740 HV, with a maximum of ∼780.3 HV) and low density (<5.9 g/cm3) in the as-cast bulk state. The hardening increment originates from the precipitation of disordered BCC nanoparticles in the ordered AlCo-rich B2 matrix compared to the dilute B2 AlCo intermetallics. This lightweight, high-performance alloy shows potential for engineering applications as thin films or coatings.