Balanced adsorbate interactions in CeVO4 nanosheets: A highly efficient and stable electrocatalyst for water splitting
Bibliographic record
Abstract
This study focusses on the synthesis of cerium vanadate (CeVO 4 ) nanosheets on nickel foam (NF) via a single-step hydrothermal method and their bifunctional performance as electrocatalysts for overall water splitting under alkaline conditions. The CeVO 4 /NF catalyst exhibited excellent oxygen evolution reaction (OER) activity with a low overpotential of 255 mV at 10 mA cm −2 and outstanding long-term durability, retaining 94.3 % of its activity after 75 h. It also showed competitive performance for the hydrogen evolution reaction (HER), achieving an overpotential of 184 mV. Density functional theory (DFT) calculations revealed that vanadium serves as the primary active site, offering balanced adsorbate interactions, strong enough to promote adsorption of key intermediates while still allowing efficient desorption. This balance explains the favorable energy profile and the rate-determining step (OOH* desorption), contributing to enhanced OER efficiency. When coupled with Pt/C as the cathode in a two-electrode alkaline electrolyser, CeVO 4 /NF delivered a low cell voltage of 1.58 V and maintained excellent performance for over 120 h, outperforming the IrO 2 ||Pt/C benchmark. These findings underscore the potential of CeVO 4 /NF as a highly efficient and durable electrocatalyst for sustainable water splitting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".