Operando Three-Electrode Analysis of Nafion-Based Polymer Electrolyte Membrane Water Electrolyzers – Thermodynamic Relations
Bibliographic record
Abstract
Polymer electrolyte membrane water electrolyzers (PEMWEs) offer clean hydrogen production when coupled to renewables, requiring minimal overvoltages for efficient operation. This study adapts and improves upon a three-electrode configuration utilized in fuel cell and electrolyzer technologies for comprehensive in operando thermodynamic analyses of PEMWE operation by accounting for the impact of catalyst layer dimensions and alignment. Catalyst-coated membrane Nafion N117-based cells with precise dimensions were developed, with design tolerances determined using finite-element simulations and verified by microscopic imaging. An external membrane strip connected to a Ag/AgCl reference electrode in acid solution enabled three-electrode electrochemical analyses of model platinum on Vulcan carbon and IrO 2 catalysts in PEMWEs with minimal artifacts. This design enabled thermodynamic analysis and indicated cathodic and anodic contributions to the cell voltage. Under 1 atm hydrogen, liquid-fed conditions, the cathode exhibited non-negligible voltage contributions attributed to hydrogen transport processes, while charge transport in the anode accounted for ca. 15% of the series resistance. Above 1 A cm –2, the cathode became the main polarization resistance while the anode exhibited a low frequency inductive response. This work provides critical insights into PEMWE operation and methods for in operando analyses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".