Academic literature on the topic 'Multi principal element alloys'

Purpose The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases. Design/methodology/approach In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm. Findings The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set. Originality/value In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

The corrosion resistance of an alloy in most environments will depend on its ability to spontaneously passivate at the corrosion potential. This is especially true for localized forms of corrosion such as occur in acidic, occluded environments during pitting and crevice corrosion. In the laboratory however, the kinetics of passivation are mainly investigated using electrochemical methods that require polarization of the material via an external power source. Spontaneous passivation cannot directly be observed by this approach. It is therefore of interest to investigate the repassivation phenomena as it occurs at open circuit, driven by the oxidizing agents present in the electrolyte. To this end, we have recently developed a methodology to determine the kinetics of spontaneous passivation using element-resolved electrochemistry (atomic spectroelechemistry, or ASEC) [1-3]. Passivation may be measured by first disrupting the original passive film using an electrochemical perturbation and then monitoring the corrosion rate as a function of time on an element-by-element basis. As the passive film reforms, the corrosion rate decreases allowing a real time monitoring of film formation. The perturbation may be either a cathodic pulse to reduce the passive film as in a conventional polarization curve experiment, or it may be an anodic pulse into the transpassive domain. In addition, the contribution of the individual alloying elements to dissolution and to passive film formation may be quantitatively accessed thereby yielding insight into one of the fundamental questions for engineering new alloys - what is the specific role of the different alloying elements? For example, it is widely recognized that for the Cr containing alloys, Cr is the primary constituent of the passive film, at least when Cr is above about 12%. However, the presence of other elements may affect the efficiency of Cr-oxide film formation, some like Mo in a beneficial way [2], others like Mn in a negative way [3]. Via a simple mass balance, the elemental dissolution rate profiles may be transformed into a time resolved elemental surface enrichment profile. This allows a direct look into the role of the alloying elements. The Figure gives an example of this approach based on the results from Ref. 3. The system under investigation was the high entropy Cantor alloy containing alloyed nitrogen in a sulfuric acid solution. The left-hand side gives the open dissolution rate for an experimental sequence of (a) open circuit, (b) cathodic activation (300 s at -0.8 V vs. SCE), (c) repassivation at open circuit (300 s). Repassivation is indicated by the initially large corrosion rate (active state) followed by the decrease of the corrosion rate as the passive film reforms. The contribution of the individual alloying elements is shown all of which dissolved congruently with the exception of Cr which was below the congruent level (black line) indicative of Cr surface enrichment. The right hand side gives the quantity of Cr enriched on the surface during the sequence calculated by mass balance. Cr dissolves during the cathodic activation but reforms as soon as the potential is released. Also shown is the Cr enrichment during an anodic step to 0.4 V which leads to a more rapid and significant build-up of surface Cr. This presentation will review the methodology of spontaneous passivation measurements for both austenitic stainless steel (304L) and for the high entropy Cantor alloy (equimolar NiFeCrCoMn) with variable Mn content. In particular, we will focus on the differences between repassivation following cathodic activation and transpassive activation. The mechanisms of spontaneous repassivation will be discussed with an emphasis on how the alloying elements influence repassivation under these two conditions. 1) K Ogle “Atomic emission spectroelectrochemistry: real-time rate measurements of dissolution, corrosion, and passivation”, Corrosion 75 (2019)1398-1419. Open access. 2) X Li, J D Henderson, F P Filice, D Zagidulin, M C Biesinger, F Sun, B Qian, D W Shoesmith, J J Noël, K Ogle, “The contribution of Cr and Mo to the passivation of Ni22Cr and Ni22Cr10Mo alloys in sulfuric acid”, Corrosion Science 176, (2020) 109015. 3) X Li, P Zhou, H Feng, Z Jiang, H Li, K Ogle, “Spontaneous passivation of the CoCrFeMnNi high entropy alloy in sulfuric acid solution: The effects of alloyed nitrogen and dissolved oxygen”, Corrosion Science 196(2022)110016. Figure 1