High-entropy Alloy Composites Research Articles

"One-stone, Two-Birds": Zinc-Rich Metal-Organic Frameworks as Precursors for High-Entropy Zn-Air Battery Electrocatalysts with Hierarchical Pore Structures.

The active sites of inexpensive transition metal electrocatalysts are sparse and singular, thus high-entropy alloys composed of non-precious metals have attracted considerable attention due to their multi-component synergistic effects. However, the facile synthesis of high-entropy alloy composites remains a challenge. Herein, we report a "one-stone, two-birds" method utilizing zinc (Zn)-rich metal-organic frameworks as precursors, by virtue of the low boiling point of Zn (907 °C) and its high volatility in alloys, high-entropy alloy carbon nanocomposite with a layered pore structure was ultimately synthesized. The experimental results demonstrate that the volatilization of zinc can prevent metal agglomeration and contribute to the formation of uniformly dispersed high-entropy alloy nanoparticles at slower pyrolysis and cooling rates. Simultaneously, the volatilization of Zn plays a crucial role in creating the hierarchical porous structure. Compared to the zinc-free HEA/NC-1, the HEA/NC-5 derived from the precursor containing 0.8 Zn exhibit massive micropores and mesopores. The resulting nanocomposites represent a synergistic effect between highly dispersed metal catalytic centers and hierarchical adsorption sites, thus achieving excellent electrocatalytic oxygen reduction performance with low catalyst loading compared to commercial Pt/C. This convenient zinc-rich precursor method can be extended to the production of more high-entropy alloys and various application fields.

Design of Experiment Is All You Need: Utilizing AI for High-Entropy Alloy Water Splitting Catalyst

High-entropy alloy (HEA) is a new group of material that have excited both fields of electrochemistry and computational chemistry, based on its intriguing concept and outstanding catalytic activity. Nevertheless, there exists a clear limitation to the 'trial-and-error' based research: its compositional space is too large. Therefore, AI-based high-throughput research is considered as an efficient way to optimize the composition of HEA electrocatalysts. To fully utilize AI's potential, the quality of data and rational Design of Experiment (DoE) is of importance. In this regard, we have 1) developed an automated one-step synthesis method of HEA with liquid handler and 2) made machine learning model with Gaussian process (GP) regression to minimize the overpotential (fig. 1a).DoE helps developing a new experiment by categorizing the variables and steps that are necessary. First step of DoE is to start with listing the variables. For the one-step synthesis of HEAs, independent variable was the composition of each elements, where the control variables were:A. Solvent (Amine/ethanol)B. Addition of CNT (O/X)C. Reaction temperature (high/low)D. Reaction time (long/short)E. Concentration of solution (5 mM / 10 mM)Each of control variables have two options and the selection is represented by writing an alphabet. For example, if amine (A) and high reaction temperature (C) condition was selected while the second option is used for the rest, it is written as AC. Based on confounding method theory, by conducting AC experiment, we also get information about BDE experiment by assuming a negative correlation to each other. It is written as: AC = -BDE. Similarly, an equivalent combinatorial list can be made for every possible combination (=25) , while the number of experiment is reduced from 32 to 16. Among these combinations, ABCE was found to be optimal synthesis condition (fig.1b).Afterward, we searched the optimal composition of HEAs, starting with grid searching the six-dimensional compositional field. MnFeCoNiCuMoPdPt - octonary alloy was the targeted alloy where the compositions of noble metal were fixed. Binomial distribution design was used to make the distance between neighboring points in 6-dimension compositional space equal (fig. 1c-d). 8C3 = 56 combinations were selected and consequently, the corresponding amounts of precursor solutions were transferred to 56 heat-inert vials and underwent heat treatment of 200 °C. Overpotential of 56 samples were measured and simple GP regression model was constructed based on these datasets.GP regression is composed of two parts: exploration and exploitation. During exploration, the optimal combination is recommended by searching the point that has the largest deviation. Interestingly, by comparing the Shapley Additive explanations (SHAP) value of each elements, it is clearly proved that Cu was harmful and Ni was beneficial to water splitting performance. Thus, Cu was screened out during the end of the exploration step. After finishing exploration, the next combination was recommended by searching near the point with least overpotential (exploitation step). By using GP regression, the optimal composition among HEAs was discovered in less than 200 experiments. It showed the overpotential less than 30mV for HER and less than 250mV for OER, making the overall water-splitting potential less than 1.5 V. In summary, a GP regression model showed synergistic effect with DoE, and HEA with the optimal composition showed an outstanding water splitting performance, reducing overall overpotential by 50% compared to the first step. Figure 1

Microstructure and mechanical properties of ZrB2 ceramic particle reinforced AlCoCrFeNi high entropy alloy composite materials prepared by spark plasma sintering

In this study, xZrB2-AlCoCrFeNi (x=0,5,10,15) (wt%) based high entropy alloy (HEA) composites were prepared by Spark Plasma Sintering (SPS). The influence of ceramic particle ZrB2 content on the densification behavior, microstructural evolution, and mechanical properties of the composites was investigated through scanning electron microscopy, X-ray diffraction, and mechanical performance testing. The results indicate that the HEA composites undergo a phase transformation, from (FCC+BCC+B2) structure to (FCC+BCC+B2+Laves) structure with the addition of ZrB2. The incorporation of ZrB2 facilitates the emergence of the Cr precipitate phase, and the grain size of this phase enlarges as the ZrB2 content rises. Additionally, when the ZrB2 content is 10%, the maximum compressive strength reached 2071 MPa. Furthermore, at the ZrB2 content of 15%, the composite material achieves a maximum densification of 99.13% and a maximum hardness of 1222.2 HV.

Wireless driven and self-sensing flexible gripper based on piezoelectric elastomer/high-entropy alloy magnetoelectric composite

The mechanical gripper shows significant importance as an alternative to manual work, but presents certain limitations in its gas or electric power with complex wire connection, presents limited available area and loud working noise, and lacks sensing function for feedback to the environment. In the present work, a novel magnetically driven and self-sensing flexible gripper was prepared based on piezoelectric elastomer and high-entropy alloy composite. The high-entropy alloy is prepared as FeCoNi(AlGa)0.5 to realize high permeability, while the piezoelectric elastomer is synthesized with superior elasticity and low modulus. The incorporation of FeCoNi(AlGa)0.5 into the piezoelectric elastomer exhibits high flexibility, magnetostriction performances and piezoelectric responses, reaching maximum strain of 1534.5 %, maximum magnetostrictive coefficient of 320 ppm and maximum piezoelectric response of 0.28 V/N. Meanwhile, the flexible gripper achieves synchronous wireless driven gripping function with a maximum lifting ratio to dead load over 41.38, and self-sensing function of target physical size with accuracy over 95 %. The flexible gripper shows maximum work power as low as 5 W. Thus, the magnetically driven and self-sensing flexible gripper shows great potential in intelligent industrial equipment.

On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.

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.

Hot oscillating pressing sintered AlCoCrFeNi/nanodiamond high-entropy alloy composites

AlCoCrFeNi/nanodiamond high-entropy alloy composites were successfully prepared by hot oscillating pressing (HOP) at different sintering temperatures in this work. The microstructures of the HOPed composites consisted of the FCC phase, BCC phase and nanocarbide phase uniformly distributed in the particles interstices. With the temperature increasing, the original powder particle boundaries gradually disappeared, the density progressively increased, and the microstructure defects decreased. At 1100 °C, its maximum density reached to 99.7 %. Moreover, the FCC phase volume fraction and carbide content further increased without significant microstructure coarsening. The hardness and corrosion resistance of the HOPed samples were better than the hot pressing (HP) samples because the original particle boundaries and pores became smaller, and a small number of nanocarbides were uniformly distributed. Then its maximum hardness reached to 566.48 HV1, and the minimum corrosion current density and corrosion rate was 2.916 μm/cm2 and 0.013 mm/year, respectively, which was better than other alloys reported.

A strategy for high-entropy copper alloys composition design assisted by deep learning based on data reconstruction and network structure optimization

Complex mapping relationships between high-entropy alloy compositions and performances struggle to precisely elucidate by traditional machine learning models, hindering the accurate and efficient design of high-performance alloys. In this work, a novel alloy design strategy combined self-decision deep neural network, data reconstruction with network structure optimization was proposed, which effectively enhanced the model's ability to predict the mapping relationships between high-dimensional composition spaces and mechanical properties of alloy. Compared to other machine learning methods, significantly improves model prediction accuracy (hardness model: 97.98%; strength model: 94.40%). Through above model, the mechanical properties of 308 new (CuNiMn)-X high-entropy copper alloys was studied added other elements such as Al, Ti, Cr, and Fe, which designed and produced a novel (CuNiMn)60Al20Cr20 alloy with high hardness of 474 HV, high compressive strength of 2322 MPa, and high fracture strain of 25.20%. The primary factors influencing alloy performance and their respective thresholds were also quantitatively revealed (hardness: electronegativity deviation (0.02, 0.1) and nuclear charge deviation (0, 0.25); compressive strength: mean nuclear charge (25, 30) and mean atomic radius (134 p.m.)). These findings provide a new perspective and approach for the design and mechanism understanding of high-performance alloys with complex compositions.

Tailoring high-entropy alloys via commodity powders for metal injection moulding: A feasibility study

High entropy alloys (HEAs) represent a novel frontier in metallurgical advancements, offering exceptional mechanical properties owing to their unique multicomponent nature. This study explores a novel strategy utilising commodity powders - Ni625, Invar36, and CoCrF75 - to tailor HEAs via metal injection moulding (MIM). The objective is to achieve cost-effective manufacturing while maintaining desired properties. The research involves mixing these powders in a specific proportion, integrating them with a sustainable polyethylene glycol-cellulose acetate butyrate binder system, and characterising the resulting feedstocks for MIM processing. Subsequent debinding and sintering steps were executed to densify and form a single face-centered cubic (FCC) phase HEA, followed by comprehensive analyses to evaluate the suitability of the developed HEA compositions. In addition, all MIM stages were thoroughly characterised to control the porosity of the final parts and to ensure a single FCC solid solution with promising mechanical properties in the developed non-equiatomic CoCrFeNiMox-type HEAs.

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.