Aggregation induced edge sites actuation of 3D MoSe <sub>2</sub> /rGO electrocatalyst for high‐performing water splitting system
Bibliographic record
Abstract
Abstract 2D materials are regarded as promising electrocatalysts for water splitting because of their advances in providing ample active sites and improving electrochemical reaction kinetics. 2D MoSe 2 has a greater intrinsic electrical conductivity and lower Gibbs free energy for reactant adsorption. However, there is still room for improvement in the electrocatalytic performance of MoSe 2 for high‐performance electrochemical water splitting devices. Herein, the in situ preparation of heterostructure made of covalently bonded MoSe 2 and rGO is reported. The obtained electrocatalyst contains the aggregated 3D structured MoSe 2 over rGO, which is covalently bonded together with more edge sites. The active edge sites of MoSe 2 /rGO are dynamically involved in the electrocatalytic activity while facilitating electron transfer. Hence, the MoSe 2 /rGO heterostructure requires a low cell voltage of 1.64 V to reach 100 mA cm −2 in water splitting with high reaction kinetics. The aggregated MoSe 2 over rGO with more edge sites exposed by the 3D structure of MoSe 2 and the interfacial covalent bond in between them provides a favorable electronic structure for the HER and OER with low overpotentials and high current densities and enhances the stability of the electrocatalyst. This work presents an attractive and cost‐effective electrocatalyst suitable for industrial‐scale hydrogen fuel production.
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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".