Novel Cobalt-Doped Ni<sub>0.85</sub>Se Chalcogenides (Co<sub><i>x</i></sub>Ni<sub>0.85–<i>x</i></sub>Se) as High Active and Stable Electrocatalysts for Hydrogen Evolution Reaction in Electrolysis Water Splitting
Bibliographic record
Abstract
In this paper, novel cobalt-doped Ni 0.85 Se chalcogenides (Co x Ni 0.85– x Se, x = 0.05, 0.1, 0.2, 0.3, and 0.4) are successfully synthesized and studied as high active and stable electrocatalysts for hydrogen evolution reaction (HER) in electrolysis water splitting. The morphologies, structures, and composition of these as-prepared catalysts are characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, and transmission electron microscopy. The electrochemical tests, such as linear sweep voltammetry, cyclic voltammetry, electrochemical impedance spectroscopy, and chronoamperometry testing, are performed to evaluate these catalysts’ HER catalytic performance including activity and stability. The results indicate that a suitable doping can result in synergetic effect for increasing the catalytic performance. Among different catalysts, Co 0.1 Ni 0.75 Se shows the highest HER performance. After introducing the reduced graphene oxide (rGO) into this catalyst as the support, the resulted Co 0.1 Ni 0.75 Se/rGO shows even better performance than unsupported Co 0.1 Ni 0.75 Se, which are confirmed by the reduction of HER overpotential of Co 0.1 Ni 0.75 Se/rGO to 103 mV compared to 153 mV of Co 0.1 Ni 0.75 Se at a current density of 10 mA/cm 2, and the smaller Tafel slope (43 mV/dec) and kinetic resistance (21.34 Ω) than those of Co 0.1 Ni 0.75 Se (47 mV/dec, 30.23 Ω). Furthermore, the large electrochemical active surface area and high conductivity of such a Co 0.1 Ni 0.75 Se/rGO catalyst, induced by rGO introduction, are confirmed to be responsible for the high HER performance.
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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".