Porous NiO Nanosheet Bifunctional Electrodes Modified with Ultrafine Ni<sub>3</sub>S<sub>2</sub> Quantum Dots for Green Hydrogen Production via Urea Electrolysis
Bibliographic record
Abstract
Electrolysis of urea can simultaneously produce molecular hydrogen and alleviate the environmental problems caused by urea-rich wastewater effluents. Therefore, it is of great significance to develop low-cost, highly active, and resistant electrodes and electrocatalysts to achieve high-efficiency urea electrolysis processes. In this study, porous NiO nanosheets (NiO/Ni 3 S 2 /NF) decorated with Ni 3 S 2 quantum dots were prepared on the surface of nickel foam (NF) via the Kirkendall effect by the gas-phase vulcanization reaction. The obtained NiO/Ni 3 S 2 /NF exhibits good electrocatalytic activity and stability toward the urea oxidation reaction (UOR) and hydrogen evolution reaction (HER). An electrolyzer containing NiO/Ni 3 S 2 /NF||NiO/Ni 3 S 2 /NF on both the anode and cathode, immersed in an alkaline aqueous solution containing urea, is assembled, and only a cell voltage of 1.408 V is required to reach a current density of 10 mA cm –2, a value which is much lower than that for an electrolyzer made of RuO 2 /NF||Pt/C/NF (1.490 V). It is found that the superior catalytic performance of NiO/Ni 3 S 2 /NF is mainly attributed to the uniform pore structure on NiO nanosheets and the synergistic effect between Ni 3 S 2 quantum dots and NiO nanosheets. This study provides a method to simultaneously construct the porous structure and heterostructure through gas-phase reactions.
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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) | 0.001 | 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".