Electrocatalytic hydrogen evolution on the noble metal-free MoS2/carbon nanotube heterostructure: a theoretical study
Bibliographic record
Abstract
Abstract Molybdenum disulfide (MoS2) is considered as a promising noble-metal-free electrocatalyst for the Hydrogen Evolution Reaction (HER). However, to effectively employ such material in the HER process, the corresponding electrocatalytic activity should be comparable or even higher than that of Pt-based materials. Thus, efforts in structural design of MoS2 electrocatalyst should be taken to enhance the respective physico-chemical properties, particularly, the electronic properties. Indeed, no report has yet appeared about the possibility of an HER electrocatalytic association between the MoS2 and carbon nanotubes (CNT). Hence, this paper investigates the synergistic electrocatalytic activity of MoS2/ CNT heterostructure for HER by Density Functional Theory simulations. The characteristics of the heterostructure, including density of states, binding energies, charge transfer, bandgap structure and minimum-energy path for the HER process were discussed. It was found that regardless of its configuration, CNT is bound to MoS2 with an atomic interlayer gap of 3.37 Å and binding energy of 0.467 eV per carbon atom, suggesting a weak interaction between CNT and MoS2. In addition, the energy barrier of HER process was calculated lower in MoS2/CNT, 0.024 eV, than in the MoS2 monolayer, 0.067 eV. Thus, the study elaborately predicts that the proposed heterostructure improves the intrinsic electrocatalytic activity of MoS2.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".