Influence of the pH Gradient on Bipolar Membrane Operation
Bibliographic record
Abstract
Bipolar membranes (BPMs) provide a key framework for integration of earth abundant catalysts in energy conversion systems and development of water purification systems. Efficient BPM operation requires water dissociation (WD) catalysts in the BPM, however, understanding of the catalyzed WD process and the impact of operating conditions has remained limited. Here, Nafion-Aemion BPMs employing known WD catalysts (graphene oxide, aluminum hydroxide, titanium oxide, iridium oxide) were investigated using electrochemical analyses as a function of catalyst loading and pH gradient conditions up to 50 mA cm-2. Altered catalyst loadings allowed the balance between field strength and catalyst utilization to be observed, while control of the pH gradient provided insight to catalyst layer operation and the limiting WD process. These results were then related to a current-voltage expression for BPM operation, where the number of ionizable catalyst sites available for proton transfer processes, space charge region thickness at the limiting WD interface, and catalyst dielectric constant are key factors. Graphene oxide was limited by hydroxide formation near the anion exchange layer, titanium oxide and iridium oxide were limited by proton formation near the cation exchange layer, while aluminum hydroxide was limited by both processes. Graphene oxide and aluminum hydroxide exhibited high field utilization and WD activity, attributed to low dielectric constants and catalyst structure. These results indicate key areas for improving BPM operation and provide methods to determine the limiting WD process.
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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 | 0.001 | 0.001 |
| 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".