Drinking Water Purification by Electrosynthesis of Hydrogen Peroxide in a Power‐Producing PEM Fuel Cell
Bibliographic record
Abstract
The industrial anthraquinone auto-oxidation process produces most of the world's supply of hydrogen peroxide. For applications that require small amounts of H2 O2 or have economically difficult transportation means, an alternate, on-site H2 O2 production method is needed. Advanced drinking water purification technologies use neutral-pH H2 O2 in combination with UV treatment to reach the desired water purity targets. To produce neutral H2 O2 on-site and on-demand for drinking water purification, the electroreduction of oxygen at the cathode of a proton exchange membrane (PEM) fuel cell operated in either electrolysis (power consuming) or fuel cell (power generating) mode could be a possible solution. The work presented here focuses on the H2 /O2 fuel cell mode to produce H2 O2 . The fuel cell reactor is operated with a continuous flow of carrier water through the cathode to remove the product H2 O2 . The impact of the cobalt-carbon composite cathode catalyst loading, Teflon content in the cathode gas diffusion layer, and cathode carrier water flowrate on the production of H2 O2 are examined. H2 O2 production rates of up to 200 μmol h(-1) cmgeometric (-2) are achieved using a continuous flow of carrier water operating at 30 % current efficiency. Operation times of more than 24 h have shown consistent H2 O2 and power production, with no degradation of the cobalt catalyst.
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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".