Abstract The development of a low-cost, high-performance platinum-group-metal-free hydroxide exchange membrane fuel cell is hindered by the lack hydrogen oxidation reaction catalyst at anode. Here we report that composite catalyst, nickel nanoparticles supported on nitrogen-doped carbon nanotubes, has activity similar to platinum-group metals in alkaline electrolyte. Although nanotubes are very poor as support, it increases catalytic performance factor 33 (mass activity) or 21 (exchange...

Abstract Refractory high-entropy alloys (RHEAs) show significant elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. Exploring the vast RHEA compositional space experimentally is challenging, a small fraction of this has been explored date. This work demonstrates development state-of-the-art machine learning framework coupled with optimization methods intelligently explore drive search direction that improves high-temperature...

Abstract Proton conductivity of the polymer electrolyte membranes in fuel cells dictates their performance and requires sufficient water management. Here, we report a simple, scalable method to produce well-dispersed transition metal carbide nanoparticles. We demonstrate that these, when added as an additive proton exchange Nafion membrane, provide significant enhancement power density durability over 100 hours, surpassing both baseline platinum-containing recast membranes. Focused ion...

Increasing the activity of non-noble catalysts for hydrogen oxidation is crucial in enhancing efficiency hydroxide exchange membrane fuel cells. Herein, we study impact graphene and nitrogen- boron-doped supports on reaction occurring Ni, Cu, Ag nanoparticles using first-principles calculations published experimental data. We find that doping leads to a stronger interaction between nanoparticle support, consequently weakening adsorption. This increased supported Ni nanoparticles, but...

The oxygen evolution reaction (OER) is a critical in electrolysis and photoelectrolysis of water to generate store clean energy. Therefore, the development low-cost efficient electrocatalysts for OER great scientific technological importance. Although promising iron oxide-based have been recently developed OER, an in-depth experimental theoretical analysis mechanism on still needed provide guidelines optimize performance further. To address this need, we synthesized series monodisperse oxide...

Abstract Refractory high-entropy alloys (RHEAs) are of prime interest for their potential use as high-temperature materials in next-generation gas turbine engines. Improving the strength-plasticity trade-off has been a grand challenge RHEAs due to vast composition search space and non-availability reliable models. In this paper, we have developed machine learning-based plasticity model yield strength order define criteria trade-off. A robust probabilistic-based uncertainty quantification is...

Adsorption isotherms are a critical tool in determining the transport of environmental contaminants through particular media. Owing to inherent heterogeneity soils, determination which soil components responsible for adsorption and retention contaminant remains challenge. In current study, we consider several thermodynamic states order predict whether or not 2,4,6-trinitrotoluene (TNT) 2,4-dinitroanisole (DNAN) would be either ground-water contaminant. reduce complexity, only two metal...

The number and types of phases formed in high entropy alloys (HEAs) have significant impacts on the mechanical properties. While various machine learning approaches were developed for predicting whether an HEA is single or multiphase, changes chemistry and/or composition can lead to other across length scales, which affect material performance. To address this challenge, we introduce a graph-based approach, captures similarity these defines design pathways chemical modifications alloys. Our...

Abstract Refractory high-entropy alloys (RHEAs) are a promising class of that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. However, exploring the vast RHEA compositional space experimentally is challenging, only small fraction this has been explored date. This work demonstrates development state-of-the-art machine learning (ML) predictive framework coupled with optimization methods intelligently explore drive search...

Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved reduced time cost production. Refractory high entropy alloys (RHEAs) are a class that capable excellent hightemperature properties. However, due to their multi-component nature, RHEAs have vast composition space presents challenges for traditional experimental exploration. Here, quantitative models compressive yield strength room-temperature plasticity...

Providing safe breathable air to aircraft crew and passengers is of primary importance for commercial airlines. This paper discusses a laboratory furnace system its use analyze the behavior, degradation, combustion several aviation fluids that are potential contaminants jet engine bleed air. Visualization techniques discussed studies with suspended free-falling droplets in environments where temperature gas composition can be precisely controlled measured. Droplets were on tip graphite...

View Video Presentation: https://doi.org/10.2514/6.2021-1587.vid Aeroengines ingest foreign object debris such as sand, which eventually erode components through repeated impacts. Due to the wide feature space, modeling and simulations are needed rapidly assess erosion behavior of materials composites. Peridynamic were performed analyze SiC/SiC composite due sand impacts, gives direct insight into impact mechanism amounts. The data was strongly correlated velocity angle, providing predictive...

Refractory high-entropy alloys (RHEAs) are a promising class of that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. However, exploring the vast RHEA compositional space experimentally is challenging, only small fraction this has been explored date. The work demonstrates development state-of-the-art machine learning (ML) predictive framework coupled with optimization methods intelligently explore drive search direction...