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Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

Jake D. Huang1,2,*, Meagan C. Papac2, Davi M. Fébba2, Ryan P. O’Hayre1, and Andriy Zakutayev2

  • *Contact author: jdhuang@mines.edu
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PRX Energy 5, 013011 – Published 13 March, 2026

DOI: https://doi.org/10.1103/nkhy-y2nz

Abstract

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to Ba(Co,Fe,Zr,Y)O3−δ combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

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Popular Summary

Electrode materials for next-generation solid oxide electrochemical cells must meet a complex set of requirements, making their discovery through traditional, time-consuming testing a major bottleneck. The rapidly emerging field of autonomous experimentation has the potential to expedite optimization of these materials, but reliable methods for characterization and analysis must be established. Using a benchtop instrument for spatially resolved measurements of thin-film microelectrode libraries, the authors establish a foundation for autonomous electrochemical characterization. By combining rapid impedance measurements with Bayesian optimization algorithms, this modular, laboratory-scale setup accelerates material characterization by an order of magnitude. This study contributes to the development and understanding of autonomous experimentation in solid-state electrochemistry, strengthening connections with robotics and data science. The findings also provide generalizable insights for developing and evaluating autonomous characterization systems.

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PRX Energy 5, 013011– Published 13 March, 2026
  • Received 29 May 2025
  • Revised 27 January 2026
  • Accepted 9 February 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by National Renewable Energy Laboratory (NREL) Library, part of a national laboratory of the U.S. Department of Energy.

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