Advanced Electrochemical Impedance Analysis Using Distribution of Relaxation Times for in Operando Mechanistic Insights of Fuel Cell and Water Electrolyzer Designs
Bibliographic record
Abstract
The development of sustainable and carbon-neutral alternative energy frameworks and chemical feedstocks requires rapid production of scalable water electrolyzer designs for hydrogen production.[1] Coupling electrolyzers to renewable energy supplies can provide a ‘green’ hydrogen production pathway, enabling clean production of chemical feedstocks as well as an energy storage framework. Current acid-based electrolyzer designs, however, integrate precious metals for stable operation, where drastic reductions in iridium use and increased cell durability are required for scalable deployment.[2] This requires the ability to monitor changes to the cell in operando for rapid diagnostics during initial and long-term operation under sustained or intermittent profiles. One technique proposed is electrochemical impedance spectroscopy (EIS), which can provide a breakdown of the cell resistances based on the timescale of the process.[3] Further analysis by circuit modeling, however, requires significant insight into the system for accurate interpretations. By coupling conventional EIS methods with distribution of relaxation times (DRT) analysis, the number of processes impacting cell operation can be determined without a priori knowledge of the system.[4] This has improved circuit modeling analysis of Li-ion batteries and solid oxide fuel cells.[5] Here, we demonstrate the power of EIS-coupled DRT analysis by analyzing the operation porous cathode and anode films of Nafion-based electrolyzer cells in half-cell and full cell configuration. Analysis of the electrodes in half-cell configurations provides estimates of kinetic parameters, active area, ionic conductivity, and diffusion coefficients associated with the electrode from a single EIS spectrum that are comparable to values obtained from in situ values.[6] Further analysis of the full cell operation with variable cathode gas composition provides insight as to the effect of the cathode gas composition on both the cathode and anode operation and stability. The work presented here will show the versatility and limitations of DRT-coupled EIS analysis of novel fuel cell and electrolyzer designs as well as present key findings for improving electrolyzer performance and stability. [1]Ayers, K. et al. Annu. Rev. Chem. Biomolec. Eng. 2019, 10, 219-239. [2]Pham, C. et al Adv. Energy Mater. 2021, 11, 2101998. [3]Liu, H. et al. J. Phys. Chem. Lett. 2022, 13, 6520-6531. [4]Wan, T. et al. Electrochimica Acta 2015, 184, 483-499. [5]Dierickx, S., Ivers-Tiffee, E. Electrochimica Acta 2020, 355, 136764. [6]Giesbrecht, P.K., Freund, M.S. J. Phys. Chem. C 2022, 126, 132-150.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".