Andrea Resta | Searcharxiv

arXiv2025

Transient Surface Oxides Form on Pt(111) - But Vanish During Ammonia Oxidation

Ammonia oxidation on platinum catalysts is pivotal for industrial nitric acid production and environmental abatement, yet the role of surface oxides in this process remains debated. Using operando surface X-ray diffraction (SXRD), crystal truncation rod (CTR) analysis, and near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS), we reveal that Pt(111) does not form stable surface oxides under ammonia oxidation conditions. Instead, transient hexagonal monolayers and a Pt(111)-(8x8) superstructure emerge under oxygen-rich atmospheres and above the catalyst light-off temperature, but vanish upon ammonia exposure. Real-time mass spectrometry and NAP-XPS demonstrate that the reaction proceeds via a Langmuir-Hinshelwood mechanism, where adsorbed NHx and O species availability dictate selectivity toward NO or N2. Reducing the oxygen pressure by an order of magnitude slows the kinetics of oxide growth, only detected after 24 hr, and facilitated by transient and precursor structures.

cond-mat.mtrl-sci↗

arXiv2021

A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

Coherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials.These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type(screw or edge). Our study paves the way for defect recognition in 3D coherent diffraction patterns for material science

cond-mat.mtrl-sci↗