Effect of Ammonium Cations on the Diffusivity and Structure of Hydroxide Ions in Low Hydration Media
Bibliographic record
Abstract
Anion exchange membrane (AEM) fuel cells are an attractive alternative technology to the acidic proton exchange membrane-based fuel cells. Conduction of hydroxide ions in AEMs creates an alkaline operating environment that allows using platinum-free catalysts, while still maintaining the performance needed for commercial application (e.g., the automotive industry). However, this technology is very sensitive to the behavior of hydroxide ions under low hydration conditions because of the consumption of water near the cathode. We use molecular dynamics simulation to investigate the behavior of two model quaternary ammonium cations used in AEM technologies at low hydration states in the presence of hydroxide anions. Both systems show the existence of an interesting ion complex—water-bridged hydroxide pair—that surprisingly involves two hydroxide anions in close proximity that is found to be highly stable. We use these new insights to explain the observed change in diffusivity of hydroxide and water from high hydration to low hydration regimes. The prevalence of these structures at different levels of hydration also explains the difference in diffusivity observed between the two studied cations. We believe that this hydroxide pair complex is key to understanding and controlling performance and stability in AEMs and in similar electrolyte systems.
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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) | 0.000 | 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".