Competing adsorption of H and CO on Pd-alloy surfaces: Mechanistic insight into the mitigating effect of Cu on CO poisoning
Pernilla Ekborg-Tanner, Paul Erhart

TL;DR
This study develops a machine learning-based framework to analyze multicomponent Pd-Au-Cu alloy surfaces, revealing how alloy composition and H coverage influence CO poisoning resistance, with Cu playing a key role in facilitating H absorption.
Contribution
The paper introduces an efficient machine learning approach combining interatomic potentials and cluster expansions to model complex alloy surface chemistry under realistic conditions.
Findings
Au-rich surfaces suppress CO and H adsorption under H-poor conditions.
H-rich conditions favor Pd-rich surfaces with higher H coverage, improving CO resistance.
Cu provides pathways for H absorption, aiding in mitigating CO poisoning.
Abstract
Multi-component alloys offer broad tunability for addressing challenges in materials science, but their vast configurational space makes their surface chemistry highly sensitive to operating conditions, for example through adsorption and segregation. Here, we study Pd-Au-Cu alloy surfaces in H and CO environments motivated by their use in H technologies, in particular plasmonic H sensing, where alloying can mitigate limitations intrinsic to Pd such as hysteresis and CO poisoning. Modeling multicomponent surfaces with multiple adsorbate species under realistic conditions is challenging. To this end, we establish an accurate and efficient framework that combines machine-learned interatomic potentials trained on density functional theory data to generate training data for cluster expansions with effectively no limitations on training set size. By constructing continuous surface…
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Taxonomy
TopicsCatalysts for Methane Reforming · Advanced Chemical Physics Studies · Machine Learning in Materials Science
