Electrochemical Reduction of Dissolved Oxygen in Alkaline, Solid Polymer Electrolyte Films
Bibliographic record
Abstract
Mass transport of oxygen through an ionomer contained within the cathode catalyst layer in an anion exchange membrane fuel cell is critical for a functioning fuel cell, yet is relatively unexplored. Moreover, because water is a reactant in the oxygen reduction reaction (ORR) in alkaline media, an adequate supply of water is required. In this work, ORR mass transport behavior is reported for methylated hexamethyl- p -terphenyl polymethylbenzimidazoles (HMT-PMBI), charge balanced by hydroxide ions (IEC from 2.1 to 2.5 mequiv/g), and commercial Fumatec FAA-3 membranes. Electrochemical mass transport parameters are determined by potential step chronoamperometry using a Pt microdisk solid-state electrochemical cell, in air at 60 °C, with relative humidity controlled between 70% and 98%. The oxygen diffusion coefficient ( D bO2 ), oxygen concentration ( c bO2 ), and oxygen permeability ( D bO2 ·c bO2 ) were obtained by nonlinear curve fitting of the current transients using the Shoup–Szabo equation. Mass transport parameters are correlated to water content of the ionomer membrane. It is found that the oxygen diffusion coefficients decreased by 2 orders of magnitude upon reducing the water content of the ionomer membrane by lowering the relative humidity. The limitation of the Shoup–Szabo equation for extracting ORR mass transport parameters using thin ionomer films was evaluated by numerical modeling of the current transients, which revealed that a significant discrepancy (up to 29% under present conditions) was evident for highly hydrated membranes for which the oxygen diffusion coefficient was largest, and in which the oxygen depletion region reached the ionomer/gas interface during the chronoamperometric analysis.
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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) | 0.000 | 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".