Shape-controlled metal nanoparticles for fuel cells applications

Behera, Ajit

doi:10.1016/b978-0-323-85727-7.00014-x

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Although metallic nanoparticles are ideal for catalyzing a variety of industrially important reactions, − commercialization has been challenging as manufacturers struggle with persistent imperfection due to an inability to match the ideal conditions prescribed by decades of research. − Competition between growth kinetics and thermodynamics − can produce ordered nanocrystals, disordered nanoparticles, and variants in between with specific bulk and surface structures that improve or degrade the stability and catalytic efficiency. For example, the density of single crystal facets enriched with steps and kinks has been shown to enhance electrocatalytic reactivity, − and defect sites with very low coordinated numbers have been shown to increase electro-oxidation independently of the supporting electrolyte used. , In particular, catalysts for the oxygen evolution reaction (OER), which is critical for electrochemical water splitting, often show high overpotentials, which limit their efficiency and usually mean sacrificing stability. , Ruthenium (Ru) nanoparticles are the most effective OER catalyst , but degrade quickly due to poor stability , because the formation of Ru >4+ species under OER conditions are readily dissolved from nanoparticle surfaces.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference

2023

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Producing perfectly regulated nanoparticle samples on a large scale is challenging and costly for manufacturers, so the ability to define and reproduce classes of nanoparticles with similar characteristics is attractive. However, developing structure/class or process/structure/class relationships is not straightforward. In this study we propose a machine learning pipeline of grouping nanoparticles based on their similarity in a high-dimensional feature space via clustering, predicting the nanoparticle classes from their structural features via classification, and identifying the relevant features that should be tuned to produce a specific class via causal inference. Using a simulated ruthenium nanoparticles data set as an exemplar, a support vector machine trained on 22 structural features managed to achieve highly accurate classification of ruthenium nanoparticles into ordered crystalline, polycrystalline, and disordered noncrystalline nanoparticles with virtually no overfitting and underfitting and high precision and recall. A Bayesian network with domain knowledge incorporated via interactive learning was trained using a hill climbing algorithm to confirm which features are causing the classes, as opposed to being just correlated to them.

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