arXiv subjects

Tomasz Prokop

Publications and source records attributed to Tomasz Prokop.

3 recordsLinked to original sources

arXiv2026

Measuring Interaction-Induced Energy Shifts of Rydberg Atoms in Hot Vapor

We demonstrate a method to measure energy shifts of the top level in a four-level ladder setup induced by atom interactions in thermal vapors. It utilizes the observation of two transmission minima corresponding to a split electromagnetically induced absorption (EIA) effect. We apply this method to measure mean Rydberg atom interactions in a hot vapor. We believe this approach could provide a valuable tool for accurately modeling mean-field Rydberg atom interactions, as well as sensing the occurrence of strong interactions.

physics.atom-ph↗

arXiv2024

Topology-Informed Machine Learning for Efficient Prediction of Solid Oxide Fuel Cell Electrode Polarization

Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming electrode microstructure into a format compatible with artificial neural networks. Input data may range from a comprehensive digital material representation of the electrode to a selected set of microstructural parameters. The chosen representation significantly influences the performance and results of the network. Here, we show a novel approach utilizing persistence representation derived from computational topology. Using 500 microstructures and current-voltage characteristics obtained with 3D first-principles simulations, we have prepared an artificial neural network model that can replicate current-voltage characteristics of unseen microstructures based on their persistent image representation. The artificial neural network can accurately predict the polarization curve of solid oxide fuel cell electrodes. The presented method incorporates complex microstructural information from the digital material representation while requiring substantially less computational resources (preprocessing and prediction time approximately 1 min) compared to our high-fidelity simulations (simulation time approximately 1 hour) to obtain a single current-potential characteristic for one microstructure.

cond-mat.mtrl-sci↗

arXiv2022

Microstructure Evolution of Solid Oxide Fuel Cell Anodes Characterized by Persistent Homology

Uncovering microstructure evolution mechanisms that accompany the long-term operation of solid oxide fuel cells is a fundamental challenge in designing a more durable energy system for the future. To date, the study of fuel cell stack degradation has focused mainly on electrochemical performance and, more rarely, on averaged microstructural parameters. Here we show an alternative approach in which an evolution of three-dimensional microstructural features is studied using electron tomography coupled with topological data analysis. The latter produces persistent images of microstructure before and after long-term operation of electrodes. Those images unveil a new insight into the degradation process of three involved phases: nickel, pores, and yttrium-stabilized zirconium.

cond-mat.mtrl-sci↗