Surface segregation in Cr-Mn-Fe-Co-Ni high entropy alloys

Ferrari, Alberto; Körmann, Fritz

doi:10.1016/j.apsusc.2020.147471

Cited by 61 publications

(41 citation statements)

References 72 publications

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“…Here, it can be noted that previous ab initio calculations of alloy compounds have found that while Ni segregation in metallic alloys can be favorable at room temperature, this trend starts to reverse at higher temperatures 29 . Another similar study 30 has found that the presence of O strongly influences the segregation of Cr at room temperature, leading to the segregation and high abundance of Cr.…”

Section: Resultssupporting

confidence: 55%

Surface chemistry and diffusion of trace and alloying elements during in vacuum thermal deoxidation of stainless steel

Zhu

Al-Sakeeri

Lenrick

et al. 2021

Surface & Interface Analysis

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Removal of the native surface oxide from steel is an important initial step during vacuum brazing. Trace and alloying elements in steel, such as Mn, Si, and Ni, can diffuse to the surface and influence the deoxidation process. The detailed surface chemical composition and grain morphology of the common stainless‐steel grade 316L is imaged and spectroscopically analyzed at several stages of in‐vacuum annealing from room temperature up to 850°C. Measurements are performed using synchrotron‐based X‐ray photoemission and low‐energy electron microscopy (XPEEM/LEEM). The initial native Cr surface oxide is amorphous and unaffected by the underlying Fe grain morphology. After annealing to ~700°C, the grain morphology is seen at the surface, persisting also after the complete oxygen removal at 850°C. The surface concentration of first Mn and then Si increases significantly when annealing to 500°C and 700°C, respectively, while Ni and Cr concentrations do not change. Mn and Si are not located only in grain boundaries or clusters but are distributed across over the surface. Both Mn and Si appear as oxides, while Cr oxide becomes metallic Cr. Annealing from 500°C up to 850°C leads to the removal of first the Mn and then Si oxides from the surface, while Cr and Fe are completely reduced to metals. Deoxidation of Cr occurs faster at the grain boundaries, and the final Cr metal surface content varies between the grains. The findings are summarized in a general qualitative model, relevant for austenite steels.

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Section: Resultssupporting

confidence: 55%

Surface chemistry and diffusion of trace and alloying elements during in vacuum thermal deoxidation of stainless steel

Zhu

Al-Sakeeri

Lenrick

et al. 2021

Surface & Interface Analysis

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show abstract

“…[17] However,a st he number of constituent elements increases,t he number of possible compositions grows combinatorially large and individual point testing cannot be accomplished within realistic time scales (see Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only af ew studies conducted, [1,3,4,20,21] and modeling of other aspects relevant for catalysis,such as surface stability under reaction conditions,is also being investigated. [22,23] We propose away to estimate the number of experiments needed using am odel that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag-Ir-Pd-Pt-Ru system. [4] Because of that, we expect the model to reproduce the complexity of an equivalent experimental search, and therefore be likely suitable as ap roxy for substituting most of the necessary experiments by simulations.B ys ampling alloy compositions from the model, the number of experiments needed for future composition optimizations can thus be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only a few studies conducted,[ 1 , 3 , 4 , 20 , 21 ] and modeling of other aspects relevant for catalysis, such as surface stability under reaction conditions, is also being investigated. [ 22 , 23 ] We propose a way to estimate the number of experiments needed using a model that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag‐Ir‐Pd‐Pt‐Ru system. [4] Because of that, we expect the model to reproduce the complexity of an equivalent experimental search, and therefore be likely suitable as a proxy for substituting most of the necessary experiments by simulations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

et al. 2021

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Active,s elective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropya lloys (HEAs) offer av ast compositional space for tuning such properties.T oo vast, however,t ot raverse without the proper tools.H ere,w er eport the use of Bayesiano ptimization on am odel based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discoveredo ptima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys.This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys whichhas been determined to be on the order of 50 for ORR on these HEAs.

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“…Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only a few studies conducted, [1, 3, 4, 20, 21] and modeling of other aspects relevant for catalysis, such as surface stability under reaction conditions, is also being investigated [22, 23] . We propose a way to estimate the number of experiments needed using a model that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag‐Ir‐Pd‐Pt‐Ru system [4] .…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

et al. 2021

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Surface segregation in Cr-Mn-Fe-Co-Ni high entropy alloys

Cited by 61 publications

References 72 publications

Surface chemistry and diffusion of trace and alloying elements during in vacuum thermal deoxidation of stainless steel

Surface chemistry and diffusion of trace and alloying elements during in vacuum thermal deoxidation of stainless steel

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

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