Study of pH Gradients and Carbonation of Hydroxide Exchange Membrane Electrolyzers
Bibliographic record
Abstract
Hydroxide exchange membrane electrolyzers (HEMELs) can produce hydrogen at scale with high efficiencies. However, like their HEM fuel cell counterparts, the alkaline membrane and ionomer of HEMELs may be susceptible to CO2 contamination and cause performance losses. CO2 can form (bi)carbonates which increase ohmic resistances due to their reduced conductivity compared to hydroxide. More importantly, the potential gradient across the membrane drives a self-purging mechanism which can lower the anode pH, causing thermodynamic overpotentials. We used modeling and experiments to study these phenomena in HEMELs in order to understand CO2-related losses through the conductivity and pH effects of different CO2 concentrations over a range of current densities. We found that pH gradients are the more significant barrier to cell performance and controlled them using three supporting electrolytes. We found that operating HEMELs at high current densities >1000 mA cm−2 can recover >200 mV of overpotential due to self-purging of (bi)carbonates, but there is still some unrecoverable overpotential from the generated pH gradients. KOH electrolytes can be used to reduce this pH gradient, but K2CO3 and KHCO3 supporting electrolytes are susceptible to the same detrimental effects of carbonation and should not be used to minimize CO2 contamination.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".