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 Ni0.85Se chalcogenides (CoxNi0.85–xSe, 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, Co0.1Ni0.75Se shows the highest HER performance. After introducing the reduced graphene oxide (rGO) into this catalyst as the support, the resulted Co0.1Ni0.75Se/rGO shows even better performance than unsupported Co0.1Ni0.75Se, which are confirmed by the reduction of HER overpotential of Co0.1Ni0.75Se/rGO to 103 mV compared to 153 mV of Co0.1Ni0.75Se at a current density of 10 mA/cm2, and the smaller Tafel slope (43 mV/dec) and kinetic resistance (21.34 Ω) than those of Co0.1Ni0.75Se (47 mV/dec, 30.23 Ω). Furthermore, the large electrochemical active surface area and high conductivity of such a Co0.1Ni0.75Se/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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".