High‐Performance Ammonium Cobalt Phosphate Nanosheet Electrocatalyst for Alkaline Saline Water Oxidation
Bibliographic record
Abstract
Abstract The development of highly efficient electrocatalysts toward the oxygen evolution reaction is imperative for advancing water splitting technology to generate clean hydrogen energy. Herein, a two dimensional (2D) nanosheet ammonium cobalt phosphate hydrate (NH4CoPO4·H2O) catalyst based on the earth‐abundant non‐noble metal is reported. When used for the challenging alkaline saline water electrolysis, the NH4CoPO4·H2O catalyst with the optimal thickness of 30 nm achieves current densities of 10 and 100 mA cm−2 at the record low overpotentials of 252 and 268 mV, respectively, while maintaining remarkable stability during the alkaline saline water oxidation at room temperature. X‐ray absorption fine spectra reveal that the activation of Co (II) ions (in NH4CoPO4·H2O) to Co (III) species constructs the electrocatalytic active sites. The 2D nanosheet morphology of NH4CoPO4·H2O provides a larger active surface area and more surface‐exposed active sites, which enable the nanosheet catalyst to facilitate the alkaline freshwater and simulated seawater oxidation with excellent activity. The facile and environmentally‐benign H2O‐mediated synthesis route under mild condition makes NH4CoPO4·H2O catalyst highly feasible for practical manufacturing. In comparison with noble metals, this novel electrocatalyst offers a cost‐effective alternative for economic saline water oxidation to advance water electrolysis technology.
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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".