Simultaneous Ni-TiO2 coated electrodes for hydrogen evolution reaction in alkaline media
Bibliographic record
Abstract
Hydrogen is recognized as a clean and reliable energy option, and its efficient and effective production is essential for future energy systems and their deployments. In this study, Ni and Ni-TiO 2 coatings are prepared on stainless steel substrates by co-electrodeposition at different deposition times and are evaluated for the hydrogen evolution reaction (HER) in alkaline media. The XRD and FESEM analyses confirm the beneficial role of TiO 2 incorporation. The Ni-TiO 2 electrode deposited for 7 min shows the best performance, achieving a hydrogen production rate of 51 mL cm −2 h −1 , with an energy efficiency of 21.45 %, an exergy efficiency of 21.42 %, a Faraday efficiency of 94.97 %, a Tafel slope of 0.118 V dec −1 , an exchange current density of 1.04x10 −4 A cm −2 , and a charge-transfer resistance of 0.068 Ω cm 2 . The AGREE score of 0.64 further highlights the environmental advantages. This study also represents the first application of the AGREE metric to Ni-TiO 2 co-electrodeposited electrodes. • The study demonstrates enhanced hydrogen evolution reaction (HER) activity in alkaline media. • The incorporation of TiO 2 significantly enhances HER performance through co-electrodeposition. • The best electrocatalytic efficiency was achieved with Ni-TiO 2 coated for 7 min. • The process is in line with the principles of green analytical chemistry, confirmed by AGREE analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".