Interfacial Chemical Bond Modulation of Co<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>-MoO<sub>3–<i>x</i></sub> Heterostructures for Alkaline Water/Seawater Splitting
Bibliographic record
Abstract
The development of a high current density with high energy conversion efficiency electrocatalyst is vital for large-scale industrial application of alkaline water splitting, particularly seawater splitting. Herein, we design a self-supporting Co 3 (PO 4 ) 2 -MoO 3– x /CoMoO 4 /NF superaerophobic electrode with a three-dimensional structure for high-performance hydrogen evolution reaction (HER) by a reasonable devise of possible “Co-O-Mo hybridization” on the interface. The “Co-O-Mo hybridization” interfaces induce charge transfer and generation of fresh oxygen vacancy active sites. Consequently, the unique heterostructures greatly facilitate the dissociation process of H 2 O molecules and enable efficient hydrogen spillover, leading to excellent HER performance with ultralow overpotentials (76 and 130 mV at 100 and 500 mA cm –2 ) and long-term durability of 100 h in an alkaline electrolyte. Theoretical calculations reveal that the Co 3 (PO 4 ) 2 -MoO 3– x /CoMoO 4 /NF promotes the adsorption/dissociation process of H 2 O molecules to play a crucial role in improving the stability and activity of HER. Our results exhibit that the HER activity of non-noble metal electrocatalysts can be greatly enhanced by rational interfacial chemical bonding to modulate the heterostructures.
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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) | 0.000 | 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".