Strong‐Proton‐Adsorption Co‐Based Electrocatalysts Achieve Active and Stable Neutral Seawater Splitting
Bibliographic record
Abstract
Abstract Direct electrolysis of pH‐neutral seawater to generate hydrogen is an attractive approach for storing renewable energy. However, due to the anodic competition between the chlorine evolution and the oxygen evolution reaction (OER), direct seawater splitting suffers from a low current density and limited operating stability. Exploration of catalysts enabling an OER overpotential below the hypochlorite formation overpotential (≈490 mV) is critical to suppress the chloride evolution and facilitate seawater splitting. Here, a proton‐adsorption‐promoting strategy to increase the OER rate is reported, resulting in a promoted and more stable neutral seawater splitting. The best catalysts herein are strong‐proton‐adsorption (SPA) materials such as palladium‐doped cobalt oxide (Co 3– x Pd x O 4 ) catalysts. These achieve an OER overpotential of 370 mV at 10 mA cm −2 in pH‐neutral simulated seawater, outperforming Co 3 O 4 by a margin of 70 mV. Co 3– x Pd x O 4 catalysts provide stable catalytic performance for 450 h at 200 mA cm −2 and 20 h at 1 A cm −2 in neutral seawater. Experimental studies and theoretical calculations suggest that the incorporation of SPA cations accelerates the rate‐determining water dissociation step in neutral OER pathway, and control studies rule out the provision of additional OER sites as a main factor herein.
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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".