scholarly journals Utilization of structural high entropy alloy for CO oxidation to CO2

2021 ◽  
Author(s):  
Chandra Sekhar Tiwary ◽  
Anjali Sharma ◽  
Nirmal Kumar Katiyar ◽  
ARKO PARUI ◽  
Rakesh Das ◽  
...  
Keyword(s):  
Co Oxidation ◽  
Scientific Community ◽  
Low Cost ◽  
Electrochemical Reactions ◽  
High Entropy Alloy ◽  
High Conversion Efficiency ◽  
High Entropy ◽  
Rate Determining Step ◽  
Wide Range ◽  
Structural High

The nanoengineered high entropy alloy (HEAs) catalysts have attracted the attention of the scientific community due to their exceptional characteristics; wide range of compositional tunability and the utilization of low-cost transition metals. During various electrochemical reactions, the oxidation of carbon-mono-oxide (CO) is an intermediate and it acts as a poison to reduce the efficiency of the reactions. A nanocrystalline HEA catalyst (CoFeNiGaZn) is prepared by easily scalable cast-cum-crush method, providing pristine catalyst surfaces. It is capable of catalyzing the CO-oxidation to CO2 with high conversion efficiency (99.8%). DFT calculations show that the high activity of the HEA can be attributed to the presence of a considerable amount of filled states of dxz and dyz orbital near the Fermi level for Ni atoms over the surface. Due to the favourable transfer of electrons from this orbital to the LUMO of reactant molecules, the endothermicity of the rate-determining step is 1.13 eV.

scholarly journals Influence of Aluminum and Molybdenum on the Microstructure and Corrosion Behavior of Thermally Sprayed High-Entropy Alloy Coatings

2021 ◽  
Author(s):  
Martin Löbel ◽  
Thomas Lindner ◽  
Maximilian Grimm ◽  
Lisa-Marie Rymer ◽  
Thomas Lampke
Keyword(s):  
Corrosion Resistance ◽  
Protective Coatings ◽  
Base Alloy ◽  
Alloy System ◽  
High Entropy Alloy ◽  
Oxide Content ◽  
Face Centered Cubic ◽  
High Entropy ◽  
Structural And Functional Properties ◽  
Wide Range

AbstractHigh-entropy alloys (HEAs) have shown a wide range of promising structural and functional properties. By the application of coating technology, an economical exploitation can be achieved. The high wear and corrosion resistance of HEAs make them particularly interesting for the application as protective coatings. Especially for alloys with a high chromium content, a high corrosion resistance has been revealed. For the current investigations, the equimolar HEA CrFeCoNi with a single-phase face centered cubic structure is considered as a base alloy system. To increase the corrosion resistance as well as the hardness and strength, the influence of the alloying elements aluminum and molybdenum is analyzed. For the current investigations, the high kinetic process high-velocity oxygen fuel thermal spraying (HVOF) has been considered to produce coatings with a low porosity and oxide content. Feedstock is produced by inert gas atomization. The influence of the alloy composition on the microstructure, phase formation and resulting property profile is studied in detail. A detailed analysis of the corrosion resistance and underlying mechanisms is conducted. The pitting and passivation behavior are investigated by potentiodynamic polarization measurements in NaCl and H2SO4 electrolyte. A distinct improvement of the corrosion resistance can be achieved for the alloy Al0.3CrFeCoNiMo0.2.

Low-cost high entropy alloy (HEA) for high-efficiency oxygen evolution reaction (OER)

Nano Research ◽  
2021 ◽  
Author(s):  
Lalita Sharma ◽  
Nirmal Kumar Katiyar ◽  
Arko Parui ◽  
Rakesh Das ◽  
Ritesh Kumar ◽  
...  
Keyword(s):  
Oxygen Evolution ◽  
Oxygen Evolution Reaction ◽  
High Efficiency ◽  
Low Cost ◽  
High Entropy Alloy ◽  
High Entropy

scholarly journals Exploring the Relationship between the CO Oxidation Reaction and the Elemental Composition in Electrocatalysts with Machine Learning - a Case Study of PtRuPdRhAu High Entropy Alloys

2021 ◽  
Author(s):  
Vladislav Mints ◽  
Jack Pedersen ◽  
Alexander Bagger ◽  
Jonathan Quinson ◽  
Andy Anker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Co Oxidation ◽  
Oxidation Reaction ◽  
Density Functional ◽  
Hydrogen Oxidation ◽  
Bayesian Optimization ◽  
High Entropy Alloy ◽  
Onset Potential ◽  
High Entropy ◽  
Co Oxidation Reaction

In recent years, the development of complex multi-metallic nanomaterials like high entropy alloy (HEA) catalysts has gained popularity. Composed of 5 or more metals, the compositions of HEAs exhibit extreme diversity. This is both a promising avenue to identify new catalysts and a severe constraint on their preparation and study. To address the challenges related to the preparation, study and optimization of HEAs, machine learning solutions are attractive. In this paper, the composition of PtRuPdRhAu hydrogen oxidation catalysts is optimized for the CO oxidation reaction. This is achieved by constructing a dataset using Bayesian optimization as guidance. For this quinary nanomaterial, the best performing composition was found within the first 35 experiments. However, the dataset was expanded until a total of 68 samples were investigated. This final dataset was used to construct a random forest regression model and a linear model. These machine learned models were used to assess the relationships between the concentrations of the consituent elements and the CO oxidation reaction onset potential. The onset potentials were found to correlate with the composition dependent adsorption energy of *OH obtained from density functional theory. This study demonstrates, how machine learning can be employed in an experimental setting to investigate the vast compositional space of HEAs.

scholarly journals Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

2021 ◽  
Author(s):  
Matthew Witman ◽  
Gustav Ek ◽  
Sanliang Ling ◽  
Jeffery Chames ◽  
Sapan Agarwal ◽  
...  
Keyword(s):  
Hydrogen Storage ◽  
Data Driven ◽  
High Entropy Alloy ◽  
Hydrogen Storage Materials ◽  
Equilibrium Pressure ◽  
High Entropy ◽  
Storage Materials ◽  
Composition Space ◽  
Wide Range ◽  
Selection For

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials' discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH<sub>2</sub> decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfH<sub>x</sub>. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps.

scholarly journals Laser-Ignited Self-Propagating Sintering of AlCrFeNiSi High-Entropy Alloys: An Improved Technique for Preparing High-Entropy Alloys

Metals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 438 ◽  
Author(s):  
Ya-Jun An ◽  
Li Zhu ◽  
Si-Han Jin ◽  
Jing-Jing Lu ◽  
Xian-Yang Liu
Keyword(s):  
Low Cost ◽  
Raw Material ◽  
High Entropy Alloy ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Effective Components ◽  
Alloy Compound ◽  
Si Content ◽  
Chromite Powder ◽  
Improved Technique

AlCrFeNiSi system porous high-entropy alloy material is manufactured by laser ignition and self-propagating sintering of natural chromite powder, which provides the idea of breaking the traditional synthesis procedure of high-entropy alloy compound material. The raw material powder obtained by ball milling is compacted into cylindrical compacts, and the self-propagating reaction comes from the ignition caused by the laser on the surface of compacts, the high-entropy alloy composite of chrome iron powder synthesized by laser sintering, is obtained as well. The raw material is prepared from Al, Cr, Fe, Ni and Si elements with similar effective components of natural chromite powder. The selected chromite powder is energy-saving and environment-friendly, so the preparation of high-entropy alloy by the low-cost short-process can be made for processing for pre-theoretical reserve and process design. The effect of Si content on microstructure and properties of AlCrFeNiSi high-entropy alloy is investigated.

Molecular Dynamics Study on the Diffusion Process of AuAgCuNiPd High-entropy Alloy Metallurgy Induced by Pulsed Laser Heating

2021 ◽  
Author(s):  
Gen Lin ◽  
Jianwu Guo ◽  
Pengfei Ji
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Laser Heating ◽  
Pulsed Laser ◽  
High Entropy Alloy ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Alloy Material ◽  
Wide Range ◽  
Pulsed Laser Heating

As a novel alloy material with outstanding mechanical properties, high-entropy alloys have a wide range of promising applications. By establishing individual Au, Ag, Cu, Ni, and Pd nanolaminates with faced-centered-cubic...

scholarly journals Broad-scale Applications of the Raspberry Pi: A Review and Guide for Biologists

2021 ◽  
Author(s):  
Jolle Wolter Jolles
Keyword(s):  
Scientific Community ◽  
Large Scale ◽  
Low Cost ◽  
Ease Of Use ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
Ecosystem Monitoring ◽  
Diverse Range ◽  
Wide Range ◽  
Macro Scale

The field of biology has seen tremendous technological progress in recent years, fuelled by the exponential growth in processing power and high-level computing, and the rise of global information sharing. Low-cost single-board computers are predicted to be one of the key technological advancements to further revolutionise this field. So far, an overview of current uptake of these devices and a general guide to help researchers integrate them in their work has been missing. In this paper I focus on the most widely used single board computer, the Raspberry Pi. Reviewing its broad applications and uses across the biological domain shows that since its release in 2012 the Raspberry Pi has been increasingly taken up by biologists, both in the lab, the field, and in the classroom, and across a wide range of disciplines. A hugely diverse range of applications already exist that range from simple solutions to dedicated custom-build devices, including nest-box monitoring, wildlife camera trapping, high-throughput behavioural recordings, large-scale plant phenotyping, underwater video surveillance, closed-loop operant learning experiments, and autonomous ecosystem monitoring. Despite the breadth of its implementations, the depth of uptake of the Raspberry Pi by the scientific community is still limited. The broad capabilities of the Raspberry Pi, combined with its low cost, ease of use, and large user community make it a great research tool for almost any project. To help accelerate the uptake of Raspberry Pi’s by the scientific community, I provide detailed guidelines, recommendations, and considerations, and 30+ step-by-step guides on a dedicated accompanying website (raspberrypi-guide.github.io). I hope with this paper to generate more awareness about the Raspberry Pi and thereby fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro- to the macro-scale.

scholarly journals Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

2021 ◽  
Author(s):  
Matthew Witman ◽  
Gustav Ek ◽  
Sanliang Ling ◽  
Jeffery Chames ◽  
Sapan Agarwal ◽  
...  
Keyword(s):  
Hydrogen Storage ◽  
Data Driven ◽  
High Entropy Alloy ◽  
Hydrogen Storage Materials ◽  
Equilibrium Pressure ◽  
High Entropy ◽  
Storage Materials ◽  
Composition Space ◽  
Wide Range ◽  
Selection For

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials' discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH<sub>2</sub> decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfH<sub>x</sub>. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps.

High-Entropy Alloys – A New Era of Exploitation

2007 ◽  
Vol 560 ◽  
pp. 1-9 ◽  
Author(s):  
Jien Wei Yeh ◽  
Yu Liang Chen ◽  
Su Jien Lin ◽  
Swe Kai Chen
Keyword(s):  
Elevated Temperatures ◽  
Second Law Of Thermodynamics ◽  
Simple Relation ◽  
Atomic Diffusion ◽  
High Entropy Alloy ◽  
New Era ◽  
High Entropy ◽  
Wide Range ◽  
Mechanisms Of Formation ◽  
And Control

A high-entropy alloy (HEA) has been defined by us to have at least five principal elements, each of which has an atomic concentration between 5% and 35%. In the exploration on this new alloy field, we find that HEAs are quite simple to analyze and control, and they might be processed as traditional alloys. There exist many opportunities to create novel alloys, better than traditional ones in a wide range of applications. In this paper, we review the basic microstructural features of HEAs and discuss the mechanisms of formation. Instead of multiple intermetallic phases, the HEAs tend to form simple solid solution phases mainly of cubic crystal structure, especially at elevated temperatures. This tendency is explained by the high entropy effect based on the simple relation: (Gmix = (Hmix – T(Smix, and the second law of thermodynamics. Moreover, nanostructures and amorphous phases are easily formed in HEAs. This tendency is explained by kinetics theory as due to slow atomic diffusion.

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