Effects of Fuel Cell Operating Conditions on Electrochemical Pressure Impedance Spectroscopy (EPIS) Diagnostics
Bibliographic record
Abstract
Fuel cells are versatile, low-emission alternative energy sources, but their optimization and failure analyses are complex due to their black-box nature. Conventional in-situ diagnostic techniques such as electrochemical impedance spectroscopy (EIS), are widely used to deconvolute transient processes within operating fuel cells. EIS is invaluable to understanding some of these processes, but other processes within the cell, such as mass transport and individual water fluxes, get overshadowed and are hard to detect. In an effort to provide information that is currently unattainable with conventional fuel cell diagnostic techniques, this project focuses on the further development of electrochemical pressure impedance spectroscopy (EPIS). EPIS is based on a pressure alternating frequency response analysis (pFRA). This diagnostic method is similar to EIS, except that it implements mechanical perturbations in the form of gas pressure oscillations, rather than voltage or current oscillations, to achieve an electrochemical response. Since EIS is based on electrical perturbations, the results are primarily based on the transport of electrons and provides electron-based performance metrics, including charge transfer, adsorption, diffusion, and double-layer behaviors. Conversely, the mechanical perturbations in EPIS enable the investigation of non-electron-based mechanisms, such as flow and gas transport resistances. This project builds from the work of Zang et al., who developed an experimental setup for EPIS and showed that pressure perturbations affect local reaction rates and transport phenomena within the cell, providing a sinusoidal voltage response.1 This study focused on systematically analyzing the effects of varying fuel cell operating conditions to better understand the benefits and limitations of EPIS as a diagnostic technique. The EPIS response and subsequent flow resistances and gas transport resistances were monitored to understand the effects of variations in the cathode inlet relative humidity, the cell inlet temperatures, and the oxygen stoichiometry (stoic). The goal is to improve the metrics by which fuel cells are measured by providing new methods to assist in the real-time probing of processes and failure mechanisms within operating cells. 1 Zhang, Q., Homayouni, H., Gates, B. D., Eikerling, M. H., & Niroumand, A. M. (2022). Electrochemical Pressure Impedance Spectroscopy for Polymer Electrolyte Fuel Cells via Back-Pressure Control. Journal of The Electrochemical Society, 169(4), 044510.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".