RETRACTED: Study on the Alloying Elements Competition Mechanism of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ Alloys Based on High-Throughput Computation and Numerical Analysis

Abstract

Previous studies on the physical properties of alloy materials often focus solely on analyzing the impact of individual alloying element content, overlooking the underlying mechanism behind the synergistic action of multiple alloying elements. Therefore, in this study, we propose a combination of high-throughput computation and numerical analysis to conduct single-element (SE) analysis and multi-element (ME) analysis on the internal relationships between alloying element content and physical properties for the multi-component Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys, aiming to elucidate the competition mechanism among the Ni, Cr, and Co elements. The analysis of SE reveals how the physical properties of alloys are affected by the content of each individual alloying element, and the ME analysis further unveils the underlying competitive relationships among multiple alloying elements. The order of competitive intensity for the formation of lattice constant is Cr > Co > Ni, whereas for the formation of elastic constants and elastic moduli it is Ni > Co > Cr. At the same time, there are contradictory conclusions, such as the SE analysis showing that the Ni content is positively correlated with elastic constant $C_{11}$, while the ME analysis demonstrates that the Ni element produces a negative competitive direction. This outcome arises from the omission of considering the combined impacts of various alloying elements in SE analysis. Therefore, the ME analysis can compensate for the limitations of SE analysis, and the integration of these two analytical methods is more conducive to elucidating the competition mechanism among various alloying elements in shaping the physical properties of alloys, which provides a promising avenue for theoretical research.

1. Introduction

A wide range of alloy materials, such as steel, aluminum alloy, titanium alloy, magnesium alloy, superalloy, etc., are extensively employed in diverse fields owing to their exceptional physical properties encompassing superior strength [1,2], hardness [3,4], toughness [5,6], corrosion resistance [7,8], and high-temperature resistance [9,10]. The aforementioned characteristics are intricately interconnected with the elemental composition of alloy materials, and modifications in the proportion of alloying elements will induce alterations in the physical properties of materials. For example, Zn strengthened the deformation resistance of an Al alloy [11], Cu improved the hardness of a Zn-8Al alloy [12], Mn increased the ductility of a Mg extrusion alloy [13], Si enhanced the corrosion resistance of steel [14], Nb increased the high-temperature strength of a Mg alloy [15], C added to the wear resistance of a CrHfNbTaTiC_xN_y high-entropy alloy [16], and so on. Obviously, the utilization of alloying technology to regulate the elemental composition will directly or indirectly impact the physical properties of alloy materials, and such alterations in content may exhibit certain discernible patterns, which facilitates the discovery of high-performance alloys. Therefore, it is imperative to investigate the potential impact of alloying element content in alloy materials.

Extensive research has been conducted by scholars to investigate the correlation between alloying element contents and physical performances, elucidating the underlying principles that can be employed for developing alloy materials with excellent physical characteristics. Huang et al. [17] revealed a parabolic correlation between the Ca content and the ultimate tensile strength of Mg alloy, with the maximum value observed at 0.8 wt % Ca content. The incorporation of the B element into a Ti alloy by Ma et al. [18] led to a monotonic increase in strength with increasing B content. The grain refinement of an Al alloy becomes increasingly pronounced with increasing Zr content, as observed by Yang et al. [19], while the mechanical properties exhibit an initial improvement followed by a subsequent decline. Zhai et al. [20] found a gradual decrease in the elongation of an Al-7Si-Cu alloy as the Cu content increased, while simultaneously observing a corresponding increase in tribological properties. Mirzaei et al. [21] observed an enhancement in the W-B bond fraction of a ternary W-B-C coating with increasing W content, resulting in improved mechanical properties of the coating. The hardness and elastic moduli of a Fe alloy exhibited a consistent decreasing trend as the Mn content increased, as calculated by Luo et al. [22]. Yang et al. [23] observed that the addition of Si to the AlCrFeMoTiSi_x high-entropy alloy facilitated the formation of continuous Cr₂O₃ and Al₂O₃, thereby enhancing its corrosion resistance. The research conducted by Liu et al. [24] demonstrated that an increase in the Mn content within the CrFeCoNiMn_x high-entropy alloys would effectively enhance the fracture energy required for their crystal cell structure. Xu et al. [25] found that the incorporation of Gd and Nd elements exerted a positive influence on the hardness, yield strength, and Young’s modulus of a Mg alloy. Moreover, the tensile strength exhibited an upward trend with increasing Gd content but decreased with rising Nd content. Meanwhile, the effects of increasing the Zr and Hf content on the microstructure and mechanical properties of Nb-Si-based ultrahigh temperature alloys were compared by Wang et al. [26]; the results demonstrate that augmenting the Zr content is more conducive to grain size refinement and enhancement of alloy fracture toughness. It is evident that alterations in the content of alloy elements exhibit a regular impact on the physical characteristics. Gaining insights into the underlying mechanisms governing this impact will facilitate the development of high-performance alloy materials.

Evidently, the aforementioned studies overlook the impact of competition among multiple alloying elements on the physical properties of alloys. To investigate the competitive mechanism, the Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys, exhibiting remarkable potential in biosensor and mechanical applications [27,28], have been chosen as the focal point of our research, and we propose a novel approach that integrates first-principles calculations in conjunction with partial least squares (PLS) regression. Firstly, a single-element (SE) analysis is conducted, wherein the content of Ni, Cr, and Co in the alloy is controlled through the first-principles calculations, and the subsequent calculation and analysis are performed to study the effect of alloying element content on the intrinsic properties of the alloys. Then, a multi-element (ME) analysis is conducted by integrating theoretical calculation data with PLS regression analysis to establish the standardized regression equation, thereby elucidating the corresponding standardized regression coefficients for each alloying element. Finally, a comparative analysis is conducted between the results of SE analysis and ME analysis to elucidate the competitive mechanism. This approach effectively demonstrates the impact of alloying element content on the physical properties of alloys, while also elucidating the competitive relationship among multiple alloying elements, thereby unveiling the underlying mechanisms governing the formation of alloy material physical properties in depth.

2. Computational Details

The Ni, Cr, and Co contents were controlled using the first-principles calculations, and then the lattice constant, elastic constants, and elastic moduli of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys were computed [24,29,30], in which the total energy and single-electron equations are calculated using the full charge density technique and Kohn–Sham equations, respectively [31,32,33]. The Perdew–Burke–Ernzerhof generalized gradient approximation is employed to represent the specific formalism of exchange–correlation functional [34]. Meanwhile, the disordered local moment model is employed to describe the paramagnetic state of alloys [35], and the Brillouin zone integrations are performed on a 25 × 25 × 25 mesh of non-equivalent k-points [36]. The optimization of basis set convergence is achieved for the s, p, d, and f orbitals, and the calculation method is enhanced by incorporating the electrostatic correction using a screened impurity model with a screening parameter of 0.7 [36,37,38]. The Green’s function is computed for 16 complex energy points precisely located on the Fermi surface to ensure the accuracy of the calculated results [39]. Moreover, the state equation is formulated based on the energy-volume data that has been fitted using a Morse-type function [40], facilitating the determination of the equilibrium volume and equilibrium lattice constant of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys [41].

Based on the first-principles calculation data, standardized regression equations are then constructed using the PLS regression to establish the relationships between multiple alloying elements and the physical properties of alloy materials [42,43], and the corresponding standardized regression coefficients are obtained to elucidate the interplay among various alloying elements in shaping the physical properties of alloys [36,38]. The regression analysis method is a robust multivariate statistical method that can effectively address the issues of multicollinearity, multiple dependent variables, and small sample sizes [44], and it integrates the power of multiple linear regression analysis [45], principal component analysis [46], and canonical correlation analysis [47]. To ensure a robust comparison of the regression analysis results, the contents of alloying elements are set in this study, in which the Al and Ti elements are set as matrix components with fixed contents of 15 at% and 10 at%, respectively. The Ni, Cr, and Co contents are regarded as the variables that can be independently controlled, while the sum of contents is constrained to a fixed value of 75 at%, denoted as $C_{Ni}+C_{Cr}+C_{Co}=75 \mathrm{at}\%$. Obviously, any alteration in the content of one alloying element would inevitably affect the contents of the other two alloying elements. Hence, in order to mitigate the ambiguity of computational variables and facilitate dependable comparisons, a master control (MC) element is incorporated into each physical performance calculation process, with the remaining two elements serving as subordinate control (SC) elements. In which, the content of MC element, $C_{MC}$, increases from 0 at% to 75 at% in increments of 15 at% per step, while the content of both SC elements is equal, both are $\left(75 at\%-C_{MC}\right)/2$, and the specific numerical variations in the content of alloying elements for each calculation step are presented in

Table 1. The contents of Ni, Cr, and Co for different MC elements.

MC Element Ni			MC Element Cr			MC Element Co
CNi (at%)	CCr (at%)	CCo (at%)	CNi (at%)	CCr (at%)	CCo (at%)	CNi (at%)	CCr (at%)	CCo (at%)
0	37.5	37.5	37.5	0	37.5	37.5	37.5	0
15	30	30	30	15	30	30	30	15
30	22.5	22.5	22.5	30	22.5	22.5	22.5	30
45	15	15	15	45	15	15	15	45
60	7.5	7.5	7.5	60	7.5	7.5	7.5	60
75	0	0	0	75	0	0	0	75

. The values of $C_{Ni}$, $C_{Cr}$, and $C_{Co}$ in the table are utilized as the independent variables in regression analysis, while the lattice constant, elastic constants, and elastic moduli are considered as the dependent variables. It is evident that the sample size analyzed in this study is relatively small, encompassing multiple independent and dependent variables with a discernible correlation among the independent variables. Therefore, based on the aforementioned conditions, the utilization of the PLS method for constructing a regression model is deemed appropriate for addressing the numerical analysis problem.

3. Results and Discussions

The equilibrium lattice constant $a_0$ of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys is initially determined through first-principles calculations for the SE analysis, and the corresponding curve correlations are illustrated in Figure 1. The noteworthy aspect is that each curve in the graph corresponds to an alloying element designated as the MC element. The curve solely depicts the influence of MC element content on the lattice constant, without enumerating the corresponding impact of SC elements. Hence, the analysis focusing on individual alloying element is referred to as SE analysis. According to the curve relationships, it is evident that the $a_0$ exhibits distinct variations with increasing contents of the three MC elements, in which the $a_0$ gradually enhances with increasing Cr content, whereas the effect of Ni and Co elements is converse, with a faster decline observed for the Co element. The results show that an increase in the Cr content of the Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys promotes a corresponding increase in the $a_0$, whereas increasing the Ni or Co content does not contribute to such an expansion. Moreover, it is noteworthy that the adverse effect of Co on the $a_0$ is particularly significant. Therefore, according to the SE analysis, it can be predicted that Cr element will exhibit a positive competitive advantage in terms of lattice constant growth, while Ni and Co elements should exhibit a negative competitive advantage.

As depicted in Table 1, alterations in the content of any one of the MC element Ni, Cr, or Co will inevitably impact the concentrations of the other two SC elements. Consequently, the effect on the lattice constant $a_0$ should be regarded as a collective outcome resulting from the concurrent modification in the content of these three alloying elements. Therefore, the PLS regression is conducted to gain deeper insights into the competition mechanism among the multi-alloying elements, and the calculated results of regression analysis are listed in

Table 2. The PLS regression results of independent variables $C_{Ni}$, $C_{Cr}$, and $C_{Co}$ on the dependent variable $a_0$.

Independent Variables	Dependent Variable	Standardized Regression Coefficients	Projected Importance Indexes	${\mathit{R}}^2$ Value (%)
$C_{Ni}$	$a_0$	−0.163	0.348	98.4
$C_{Cr}$		0.636	1.361
$C_{Co}$		−0.474	1.013

.

The table includes three crucial analysis parameters, namely the standardized regression coefficient, projected importance index, and $R^2$ value, in which the impact exerted by the independent variable on the dependent variable is measured by the regression coefficient; the higher the absolute value of the coefficient, the stronger its impact, and the positive and negative sign of coefficient indicates the directivity of impact. The projected importance index represents the explanatory power of the independent variable on the dependent variable during regression model construction, with a higher value denoting enhanced explicative capability. Moreover, the $R^2$ value indicates the fit degree of regression model, and a higher value shows a stronger alignment between the independent variable and the dependent variable. The results indicate that the standard regression coefficients corresponding to $C_{Ni}$, $C_{Cr}$, and $C_{Co}$, respectively, are −0.163, 0.636, and −0.474; therefore, the standardized regression equation can be expressed as $a_0=-0.163C_{Ni}+0.636C_{Cr}-0.474C_{Co}$. The equation clearly elucidates the competitive interplay among the Ni, Cr, and Co elements in determining the $a_0$ of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys. Consequently, the competitive intensity decreases in the sequence of Cr, Co, and Ni, and the element Cr exhibits a notable and positive competitive edge, while the elements Ni and Co demonstrate a negative competitive advantage. The standard regression coefficients are presented in a bar chart, as depicted in Figure 2, which intuitively shows the competitive intensity and direction of the three alloying elements. At the same time, the corresponding projected importance indexes are 0.348, 1.361, and 1.013, respectively. The findings indicate that $C_{Cr}$ and $C_{Co}$ make significant contribution to the construction of regression expression, while $C_{Ni}$ exerts a relatively minimal influence. The regression, however, exhibits a strong fit to the data with the $R^2$ value of 98.4%. Therefore, the ME analysis quantifies the latent competitive relationships in Figure 1, thereby providing a more profound understanding of the mechanism underlying the formation of lattice constants in alloy materials.

Simultaneously, the elastic constants $C_{11}$, $C_{12}$, and $C_{44}$ are calculated, and the corresponding curve correlations are displayed in Figure 3. In Figure 3a, the $C_{11}$ value tends to rise as the content of Ni, Cr, or Co element increases, except for a slight decrease in Cr content within the range of 0–15 at%. The SE analysis seem to indicate that the three alloying elements has a positive promotional effect on the $C_{11}$ value. The promotion of Cr and Co to the $C_{11}$ is relatively fast and fairly equivalent in the interval 15–75 at%. Meanwhile, the irregular fluctuations in the curve may be attributed to the structural phase transition or a change in the coordination of atoms induced by severe lattice distortion in alloys. In Figure 3b, the $C_{12}$ value decreases with increasing Ni content, and the value shows a gentle fluctuation as the Co content increases. Hence, the increase in Ni content does not contribute to the enhancement of $C_{12}$ in the Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys, while the impact of increasing Cr or Co content exhibits a more intricate trend. Moreover, the $C_{44}$ demonstrates a progressive elevation as the Cr or Co content increases, and the growth effect of the Co element surpasses that of the Cr element. Meanwhile, the $C_{44}$ value initially increases and then decreases with the increment of Ni content, as depicted in Figure 3c. The SE analysis suggests that the competitive strength of the three alloying elements for the elastic constants $C_{11}$ and $C_{44}$ follows the order Co > Cr > Ni. However, it is challenging to determine the competitive strength for the elastic constant $C_{12}$ due to its chaotic curve relationship.

Obviously, the intricate curve illustrated in Figure 3 does not directly elucidate the competitive relationship among the three alloying elements. Therefore, a further analysis is conducted using the PLS regression to derive insights from the data, and the corresponding results are presented in

Table 3. The PLS regression results of independent variables $C_{Ni}$, $C_{Cr}$, and $C_{Co}$ on the dependent variables $C_{11}$, $C_{12}$, and $C_{44}$.

Independent Variables	Dependent Variables			Standardized Regression Coefficients			Projected Importance Indexes			${\mathit{R}}^2$ Values (%)
$C_{Ni}$	$C_{11}$	$C_{12}$	$C_{44}$	−0.436	−0.408	−0.492	1.126	1.082	1.163	71.1	68.2	83.4
$C_{Cr}$				−0.161	0.135	0.145	0.294	0.232	0.331
$C_{Co}$				0.395	0.343	0.467	0.972	0.950	1.082

. The corresponding standardized regression equation can be expressed as follows:

$$\left\{\begin{array}{l}{C}_{11}=-0.436C_{Ni}-0.161C_{Cr}+0.395C_{Co}\\ C_{12}=-0.408C_{Ni}+0.135C_{Cr}+0.343C_{Co}\\ C_{44}=-0.492C_{Ni}+0.145C_{Cr}+0.467C_{Co}\end{array}\right.$$

(1)

where the standardized regression coefficients presented in Table 3 represent the constant coefficients associated with the independent variables, and the absolute value and sign of coefficient values reflect the competitive intensity and direction of Ni, Cr and Co alloying elements.

The histogram in Figure 4 illustrates the regression coefficients, which will provide a more insightful understanding of the competitive dynamics, encompassing both intensity and direction. Clearly, for the formation of $C_{11}$, $C_{12}$, and $C_{44}$, the orders of competitive intensity are $\mathrm{Ni}>\mathrm{Co}>\mathrm{Cr}$, and the intensity of the Co element is only marginally inferior to that of the Ni element. The results demonstrate that the Ni and Co elements exhibit significant advantage in influencing the elastic constants of alloys, whereas the Cr element displays the least favorable competitive advantage. Meanwhile, it can be seen that the regression coefficients corresponding to the Ni element are negative, whereas those corresponding to the Co element are all positive, indicating that the competitive directions of the Ni element are all negative for the $C_{11}$, $C_{12}$, and $C_{44}$, while those of the Co element are all positive. Hence, increasing the Ni content is detrimental to enhancing the elastic constants of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys, whereas an increase in the Co content is advantageous. Herein, the ME analysis yields divergent conclusions compared to the SE analysis, particularly regarding the impact of Ni elements on the elastic constant $C_{11}$, as depicted in Figure 3a. However, considering the overall trend in the curves, it can be inferred that the competitive impact of Ni elements is likely to be negative, because the black curve exhibits a more gradual variation in comparison to the other two curves. Consequently, the obtained results further support the notion that the ME analysis can effectively unveil the underlying competition mechanism among alloying elements in the SE analysis. Meanwhile, the values of projected importance indexes make it evident that both $C_{Ni}$ and $C_{Co}$ display robust explanatory capabilities in Equation (1). However, the regression equation lacks reliability due to the relatively low $R^2$ values; the $R^2$ value for the independent variables $C_{Ni}$, $C_{Cr}$, $C_{Co}$, especially, with respect to the dependent variable $C_{12}$, is only 68.2%. This result may be attributed to the intricate curves depicted in Figure 3b and the limited number of experimental samples. In general, the hidden competitive relationships among the multiple alloying elements in Figure 3 is presented through the ME analysis, and the erroneous research findings in the SE analysis have been rectified, thereby rendering further the ME analysis more advantageous for refining the conclusions of the SE analysis.

At the same time, the bulk modulus B, shear modulus G, and Young’s modulus E are computed, and the corresponding curve correlations are illustrated in Figure 3. For the shear modulus G and Young’s modulus E, it can be found that the corresponding curves of both moduli exhibit similar variations, as shown in Figure 5b,c. The increasing content of the MC elements Ni, Cr, or Co exhibits a positive correlation with the G and E values, except for a slight decline in the content of Ni at 75 at%, indicating that the augmentation of Ni, Cr, or Co content facilitates an increase in the G and E of alloys. The promoting effect of the Co element is particularly pronounced, as evidenced by the significantly steeper growth curve observed at a higher Co content. Meanwhile, for the bulk modulus B, the curves in Figure 5a exhibit intricate patterns, indicating a non-linear relationship between the B value and the Ni or Cr content, with an initial decrease followed by an increase. Conversely, the B value generally increases as the Co content rises. Hence, the SE analysis suggests that increasing the Co content is advantageous in enhancing the bulk modulus B of alloys, while an accurate prediction of the impact of Ni and Cr elements remains elusive.

Based on the data pertaining to the independent and dependent variables presented in Table 1 and Figure 5, the corresponding PLS regression is conducted, and the results are listed in

Table 4. The PLS regression results of independent variables $C_{Ni}$, $C_{Cr}$, and $C_{Co}$ on the dependent variables B, G, and E.

Independent Variables	Dependent Variables			Standardized Regression Coefficients			Projected Importance Indexes			${\mathit{R}}^2$ Values (%)
$C_{Ni}$	B	G	E	−0.458	−0.404	−0.415	1.289	1.101	1.128	76.2	71.7	72.3
$C_{Cr}$				0.219	0.140	0.158	0.463	0.170	0.244
$C_{Co}$				0.339	0.365	0.357	0.926	1.031	1.025

. Hence, the corresponding standardized regression equation between the content of alloying elements $C_{Ni}$, $C_{Cr}$, $C_{Co}$ and the B, G, E can be expressed as follows:

$$\left\{\begin{array}{l}B=-0.458C_{Ni}+0.219C_{Cr}+0.339C_{Co}\\ G=-0.404C_{Ni}+0.140C_{Cr}+0.365C_{Co}\\ E=-0.415C_{Ni}+0.158C_{Cr}+0.357C_{Co}\end{array}\right.$$

(2)

in which the constant coefficients preceding the independent variables $C_{Ni}$, $C_{Cr}$, and $C_{Co}$ correspond to the standardized regression coefficients.

To intuitively understand the competitive relationship, we present the corresponding bar chart illustrating standardized regression coefficients in Figure 6. For the competitive direction, the coefficients of the Ni element are all negative, while those associated with the Cr and Co elements consistently demonstrate positive values. The findings show a negative contribution of the Ni element to the formation of elastic moduli in the Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys, whereas Cr and Co exhibit positive contributions. Therefore, to enhance the elastic moduli of alloys, the concentrations of the Cr and Co elements should be increased, reducing the content of the Ni element. This result appears to contradict the influential relationship depicted in the corresponding curves of the Ni element in Figure 5b,c. However, the underlying competitive dynamics of Ni element can be inferred from the overall trend observed in the respective curves of all three alloying elements. Therefore, the conclusion of the MME analysis is reliable. At the same time, the standardized regression coefficients exhibit a consistent pattern, with the absolute values ranked as Ni > Co > Cr for each elastic modulus parameter. The result indicates that Ni element shows the most significant competitive advantage, while the Cr element displays the least favorable competitive advantage in influencing the formation of elastic moduli in the alloys. Hence, the most effective approach to enhance the elastic moduli of Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys is by increasing the Co content, as determined through the ME analysis of competitive intensity and direction. Meanwhile, the corresponding $R^2$ values are 76.2%, 71.7%, and 72.3%, respectively, suggesting that the regression equation is not well fitted, which can be attributed to the intricate data relationship and the limited sample size. However, the ME analysis method effectively uncovers latent competition relationships among multi-alloying elements in intricate data, bridging the gap left by the SE analysis method and thereby further accentuating its theoretical research significance.

4. Conclusions

The competitive relationship of multiple alloying elements on the formation of physical properties in Ni_x1Cr_x2Co_x3Al₁₅Ti₁₀ alloys has been extensively investigated in this paper, employing a combination of SE analysis and ME analysis. The main findings are as follows:

For the lattice constant, the SE analysis demonstrates a positive correlation between the Cr content and the $a_0$, while simultaneously demonstrating a negative correlation with the Ni or Co content. The ME analysis further reveals the competitive relationships, indicating that the relative competitive intensity of the three alloying elements is ranked as follows: Cr > Co > Ni, and the competitive direction of the Cr element is positive, whereas both Ni and Co elements display a negative competitive orientation.

For the elastic constants, the SE analysis elucidates the impact of variation in individual alloying element content on the elastic constants, in which the Ni content exhibits a positive correlation with the $C_{11}$, and the Cr content displays an almost negative correlation with the $C_{12}$. However, the corresponding ME analysis reveals a negative competitive direction for the Ni element, while indicating a positive competitive direction for the Cr element. Meanwhile, the Ni element shows a pronounced negative competitive advantage, while the Co element demonstrates a significant positive competitive advantage.

For the elastic moduli, the SE analysis suggests that the G and E values show a positive correlation with the increasing content of the MC element Ni, Cr, or Co, except for a slight decline in the content of Ni at 75 at%; the ME analysis uncovers latent competition relationships among alloy elements from intricate data, demonstrating that the Ni consistently demonstrates a more pronounced disadvantage in influencing the formation of elastic moduli in the alloys, while the Co element consistently demonstrates a stronger positive competitive advantage.