Rapid mapping of electrochemical processes in energy-conversion devices

Huang, Jake D.; Meisel, Charlie; Sullivan, Neal P.; Zakutayev, Andriy; O’Hayre, Ryan

doi:10.1016/j.joule.2024.05.003

Rapid mapping of electrochemical processes in energy-conversion devices

Journal Article · Thu May 02 00:00:00 EDT 2024 · Joule

DOI:https://doi.org/10.1016/j.joule.2024.05.003· OSTI ID:2376842

^[1]; Meisel, Charlie ^[2]; Sullivan, Neal P. ^[2]; Zakutayev, Andriy ^[3]; O’Hayre, Ryan ^[2]

Colorado School of Mines, Golden, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Colorado School of Mines, Golden, CO (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Electrochemical impedance spectroscopy (EIS) is ubiquitously applied to identify physicochemical processes governing the performance of energy-conversion devices. However, deconvolution and interpretation of impedance phenomena are limited by measurement throughput and a dearth of scalable analysis methods. Here, we demonstrate an approach to quickly collect and coherently analyze large volumes of electrochemical data. In this study, we accelerate impedance characterization by combining rapid measurements in time and frequency domains, which are interpretably transformed using the distribution of relaxation times (DRT) and a new distribution of phasances (DOP) model. This method provides excellent agreement with EIS and decreases measurement time by an order of magnitude. High-throughput spectra are then distilled into detailed electrochemical maps. This approach is applied to a Li-ion battery and a protonic ceramic electrochemical cell as practical case studies, demonstrating how mapping can richly characterize physicochemical relationships that are difficult to decipher with conventional measurement and analysis methods.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Basic Energy Sciences (BES); US Army Research Office (ARO)

Grant/Contract Number:: AC36-08GO28308; AR0000808; SC0023450

OSTI ID:: 2376842

Alternate ID(s):: OSTI ID: 2559003

Report Number(s):: NREL/JA--5K00-89975; MainId:91753; UUID:9764d5ec-d868-445f-87af-0405fe9a96df; MainAdminId:72898

Journal Information:: Joule, Journal Name: Joule Journal Issue: 7 Vol. 8; ISSN 2542-4351

Publisher:: Elsevier - Cell PressCopyright Statement

Country of Publication:: United States

Language:: English

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The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

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