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The wide range of alloy composition controllability for multi-principal element alloys (MPEAs) may provide a great opportunity for discovering special forms of surface oxides to improve the corrosion and oxidation resistance in extreme environments. Changing the type and content of promoting passivation elements would not only change the microstructure of the alloy but also significantly affect the composition and structure of the surface passive film, resulting in a strong impact on the corrosion and oxidation resistance of the alloy. This article reviews recent research on the effects of alloying elements on the passivation properties, the contribution of each alloying element, and the synergistic effect between the elements on the passivation mechanisms and electrochemical dissolution characteristics of surface passive films that form on some MPEAs. In addition, the composition and structural characteristics of surface oxides relevant to the selective oxidation of elements are elaborated upon. Finally, several open questions and recommendations for research directions regarding the passivation and selective oxidation of MPEAs were provided to guide future exploration. [ABSTRACT FROM AUTHOR]

Alloy synthesis and processing determine the design of alloys with desired microstructure and properties. However, using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data acquisition. Text mining has made it possible to convert scientific text into structured data collections. Still, the complexity, diversity, and flexibility of synthesis and processing expressions, and the lack of annotated corpora with a gold standard severely hinder accurate and efficient extraction. Here we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and processing. We automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to 2022. These have then been used to capture an explicitly expressed synthesis factor for predicting γ′ phase coarsening. The synthesis factor derived from text mining significantly improves the performance of the data-driven γ′ size prediction model. The method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures. [ABSTRACT FROM AUTHOR]

Purpose: Cobalt-based alloys exhibit a unique combination of wear resistance, strength and corrosion resistance. Localized corrosion of such alloys in seawater system can be several orders of magnitude faster than general corrosion, and direct experimental evidence of the local activation process is still lacking, which makes the accurate prediction for properties difficult, especially for long-term corrosion. The purpose of this study is revealing the relationship between multiple environments and corrosion properties to predict the corrosion of cobalt-based alloys. Design/methodology/approach: A data-driven method for the prediction of the corrosion behavior of cast and hot isostatic-pressed CoCrMo/W alloys in seawater is proposed. The gradient boosting regression models calculate mean relative errors (MREs) of 0.160 and 0.435 by evaluating a hold-out set for breakdown potential (Eb) and maximum current density (imax), respectively, considering various compositions, synthesis methods and corrosion environments. Findings: The models can be used to estimate the "unseen" cobalt-based alloy after immersion in 3.5 Wt.% NaCl solution for one, two, four and eight months to obtain high precision with MREs of 7.8% and 9.8% for Eb and imax, respectively. Originality/value: Machine learning method provides novel and promising insights for the prediction of localized corrosion properties. [ABSTRACT FROM AUTHOR]

In this study, for the first time, the evolution of geometrically necessary dislocation (GND) and statistically stored dislocation (SSD) densities, as well as their roles in strain hardening during mechanical twinning, was experimentally investigated in a tensile-deformed Fe-22Mn-0.6C twinning-induced plasticity (TWIP) steel. GND and SSD densities were estimated via EBSD-acquired orientation data and a modified strain hardening model, respectively. The analysis demonstrates that the GND density increases non-linearly due to mechanical twinning. The SSD density increases much faster than the GND density, which shows that multiplication of the SSDs is heavily dependent on the imposed strain level. It is revealed that the GND density is higher at early strain stages (below 0.14 true strain), dominating dislocation hardening, but thereafter the SSD density contributes more. It is also found that the GND density is several times higher in this TWIP steel than in metals or alloys, which deform through dislocation slip only. We attribute this difference to the planar slip of dislocations and the occurrence of mechanical twinning, which leads to much more pile-ups of the GNDs at/near boundaries. Mechanical twinning directly contributes less than 100 MPa to flow stress increment in the studied true strain range of 0 to 0.34. Consequently, depending on dislocation types, dislocation multiplication governs strain hardening at all deformation ranges. The findings provide insight into the evolution behaviors of GNDs and SSDs in TWIP steels, which are particularly important for further understanding of the dynamic Hall-Petch effect and useful for TWIP alloy design efforts. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Surface modification is a crucial method for regulating the degradation behavior of biomedical zinc-based alloys in consideration of clinical safety. In this work, Cu ion implantation was introduced for Zn–4Cu–0.02Li. The results indicated that a gradient modified layer formed, which was composed of three regions, namely, a 70-nm element implanted layer, a 600-nm nanocrystalline layer, and a 2-μm columnar crystal layer. The modified layer reduced corrosion within one month and transformed the localized corrosion into the uniform corrosion mode. Its hierarchical degradation process induced the controlled degradation. Additionally, Cu ion implantation enhanced the inhibitory effect on S. aureus. [Display omitted] • Gradient surface structure is achieved by ion implantation for biodegradable Zn4Cu0.02Li. • Clear two stages of degradation rates with CuX+ implanted alloys are realized. • The modified layer transforms the localized corrosion into uniform corrosion mode. [ABSTRACT FROM AUTHOR]

We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties. In the present study, we proposed a data-driven approach combining machine learning, experimental design and feedback from experiment to accelerate the search for multi-component alloys with target properties. We demonstrated the efficiency of our approach by rapidly obtaining several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. In Iteration Loop I, a machine learning surrogate model is trained to learn the property-composition, relationship, y i = f (c i) , with associated uncertainties. The model is applied to the vast unexplored space and a utility function is employed to recommend the most informative candidate for the next experiment, which balances the exploitation and exploration. Feedback from experimental synthesis and characterization allows the subsequent iterative improvement of the surrogate model. Iteration Loop II is essentially same as Iteration Loop I, except that a features pool was introduced to the Iteration Loop I and a surrogate model is trained from composition (c i) and the preselected physical features (p i), y i = f (c i , p i). We found that the approach using both the composition and the descriptors based on domain knowledge can more effectively accelerate material optimization compared to the approach using only the compositions. Image 1 [ABSTRACT FROM AUTHOR]

The complex element composition and enormous composition space of high entropy alloys (HEAs) adds more uncertainty to the material properties. Here, a series of nanocrystalline Al x Cu y (FeCrNiCo) 100−x-y (x = 6.44–50.51 at%, y = 8.49–40.31 at%) HEAs were high-throughput synthesized. Co-alloying Al and Cu effects on the corrosion resistance of FeCrNiCo HEAs were systematically investigated in a two-dimensional composition space, and the microstructure and mechanical properties of typical alloy compositions were examined. Experimental results show that nanocrystalline Al 16.29 Cu 13.04 (FeCrNiCo) 70.67 and Al 19.59 Cu 17.08 (FeCrNiCo) 63.33 alloys have balanced corrosion resistance, hardness and plasticity. The underlying mechanisms for co-alloying Al and Cu effects on the corrosion resistance and mechanical properties were analyzed. • Nanocrystalline Al x Cu y (FeCrNiCo) 100−x-y high entropy alloys were high-throughput synthesized. • The corrosion behavior, microstructure and mechanical properties were investigated. • Pitting corrosion is the main corrosion form of nanocrystalline Al x Cu y (FeCrNiCo) 100−x-y alloys in 3.5 wt% NaCl solution. • Nanocrystalline Al 16.29 Cu 13.04 (FeCrNiCo) 70.67 and Al 19.59 Cu 17.08 (FeCrNiCo) 63.33 alloys have good comprehensive properties • The passive film of the alloys with better corrosion resistance contains more unoxidized metal. [ABSTRACT FROM AUTHOR]

Traditional forging and emerging additive manufacturing techniques have been widely used to prepare Ti-6Al-4V (TC4). However, their wear and tribocorrosion properties have not been compared systematically. We simulated the body fluid environment for laboratory comparative analysis. The results revealed that forged TC4 samples exhibited better corrosion resistance than SLM-TC4, and SLM-TC4 exhibited better wear resistance than TC4. Tribocorrosion resistance was significantly affected under conditions of corrosion. The extent of damage realized for the samples was less at a high applied potential (1V) than the extent of damage realized at a low applied potential (0V) under conditions of low-frequency friction (0.083 Hz). It is believed that the oxide formed on the surface plays a protective and lubricative role. [Display omitted] • The acicular martensite phase provides higher hardness, resulting in better wear resistance of SLM-TC4. • Forged TC4 has better corrosion resistance due to the oxide layer. • Corrosion factors play a decisive role in tribocorrosion, and oxides provide lubrication and protection. [ABSTRACT FROM AUTHOR]

Equiatomic TiZr(Hf, Ta, Nb) medium entropy alloys (MEAs) were developed to achieve higher corrosion resistance than pure Ti. Nb and Ta alloying can greatly improve the corrosion resistance of Ti-50Zr (at%) alloys by reducing point defects in passive films. The oxide thickness for elements in passive films on TiZr(Hf, Ta, Nb) alloys increase significantly, following Hf > Zr > Ti > Nb > Ta, which was determined by the ability of elements to form oxides. Serious ploughing and delamination wear result in a higher tribocorrosion rate for TiZrNb alloy. The wear mechanisms for the three MEAs was discussed. [Display omitted] • Nb and Ta can remarkably reduce point defects in passive films of Ti-50Zr alloys. • OOxide thickness for each element on studied alloys decrease significantly following Hg > Zr > Ti > Nb > Ta. • Tribofilm and absence of delamination result in a higher wear resistance for TIZRHf than TiZr(Ta, Nb). [ABSTRACT FROM AUTHOR]

• The repassivation rate of three equiatomic MEAs was in the order: TiZrHf > TiZrTa > TiZrNb. • Tribofilm and material transfer exist in all MEAs, which was prominent in TiZrNb. • The TiZrNb exhibits the highest wear rate, followed by the TiZrTa and TiZrHf. The repassivation behavior of equiatomic TiZr (Hf, Ta, Nb) alloys was studied by an in-situ scratch method. Among the three alloys, TiZrHf had the highest repassivation rate, followed by TiZrTa and TiZrNb. In addition, only micro-abrasive wear was found in TiZrHf, and the wear mechanism was mainly micro-abrasion and delamination in TiZrTa, whereas severe ploughing, delamination, and plastic deformation was found in TiZrNb, due to its lower hardness, resulting in TiZrNb having the highest coefficient of friction (COF) and wear rate. Tribofilm and material transfer were found in all medium entropy alloys, which was prominent in TiZrNb. The extent of work hardening induced by friction was in the following order: TiZrHf > TiZrTa > TiZrNb. [ABSTRACT FROM AUTHOR]

Magnetron co-sputtering has been widely used for high-throughput preparation of combinatorial material library. However, a large number of alloy compositions in the fabricated material libraries are interconnected with each other, and their element distribution is uneven. Here, we present a high-throughput strategy where 97 independent nanocrystalline alloy samples with homogeneous element distribution can be prepared simultaneously through multi-station rotary sample stage. Using this approach, we synthesized 22 nanocrystalline Fe-Cr-Ni alloy samples covering 10.65–28.36 wt% Cr and 7.47–24.57 wt% Ni and studied their pitting corrosion resistance by electrochemical measurements, thereby quickly establishing the relationship between chemical compositions and corrosion resistance. • A method for preparing homogeneous multi-component material libraries is presented. • Fe-Cr-Ni alloys covering 10.65–28.36 wt% Cr, and 7.47–24.57 wt% Ni were synthesized. • The composition range with good pitting corrosion resistance was rapidly demarcated. • Fe 60.89 Cr 17.90 Ni 21.21 alloy has high-density planar defects and nanometer-sized pores. [ABSTRACT FROM AUTHOR]

Solid solution strengthening (SSS) influences the exceptional mechanical properties of single-phase high entropy alloys (HEAs). Thus, given the vast compositional space, identifying the underlying factors that control SSS to accelerate property-oriented design of HEAs is an outstanding challenge. In the present work, we demonstrate a relationship derived in terms of the electronegative difference of elements to characterize SSS for HEAs. We propose a model which shows superior performance in predicting solid-solution strength/hardness of HEAs compared to existing physics-based models. We discuss applications of our SSS model to HEA design and predict alloys with potentially high SSS in the four alloy systems AlCoCrFeNi, CoCrFeNiMn, HfNbTaTiZr and MoNbTaWV. Our findings are based on the use of machine learning (ML) methods involving feature construction and feature selection, which we employ to capture salient descriptors. We identify descriptors and propose a model for solid solution strengthening (SSS) in high entropy alloys (HEAs). After data collection and feature engineering using diverse machine learning models, we identify salient descriptors, including electronegativity difference and shear modulus, influencing SSS in HEAs. The descriptors serve to formulate an SSS model with a simpler form to predict the strength of single-phase HEAs with greater accuracy compared to existing physics based SSS models. The descriptors also offer a perspective on SSS in terms of electron-associated interactions amongst the elements of the HEA. Finally, we predict HEAs with high SSS in four typical alloy systems. To motivate experiments, specific composition ranges are predicted which have high strengthening and not previously studied. They span a broad space of elements and their combinations [Display omitted]. [ABSTRACT FROM AUTHOR]

• Surface energy of the implants is key to protein adsorption. • Various surface energies were achieved by different structure of oxides. • BAS absorption was affected more by polar component of surface energy. • Electrostatic and hydrophobic interactions control protein adsorption. CoCrMo alloys have been used as biomedical materials for decades. During long-term service in the body environment, however, proteins adsorbing onto the surface of implant can cause their spontaneous corrosion and even the release of toxic ions, which may cause long-term damage to patients. It is essential to understand the key factors affecting protein adsorption and how it can alter corrosion reactions. In this study, the surface energy of a CoCrMo alloy was changed through various oxidation processes. Surface analysis techniques, such as XPS, AES, and GIXRD were used to characterise the chemical properties of the surfaces of modified CoCrMo alloys. Surface topography and potential images of samples after bovine serum albumin (BSA) adsorption were acquired by AFM/SKPFM. The results show that with the increase of the oxidation temperature, the hydrophilicity of the samples was enhanced. With the increase in oxidation time at the same temperature, the surface energy of the samples increased, the dispersion component increased, and the polar component decreased. As a result, the amount of BSA adsorbed on the surfaces decreased, and this was affected more by the polar component than by other components, which was due to the electrostatic and hydrophobic interactions. [ABSTRACT FROM AUTHOR]