Electrodeposition of PdPt Nanoparticles on Edges and S‐Vacancies in Exfoliated MoS<sub>2</sub> Nanosheets for Enhanced Hydrogen Evolution Activity
Bibliographic record
Abstract
Abstract Deposition of metal nanoparticles onto the molybdenum disulfide (MoS2) nanosheets is an efficient method to tune the electronic structure of the MoS2 and maximize its catalytic performance towards the hydrogen evolution reaction (HER). Herein, we report the electrodeposition of Pd and Pt nanoparticles onto desulfurized MoS2 nanosheets (MoS2–x) to achieve an improved HER activity in an acidic electrolyte. The initial MoS2 powder was exfoliated and isolated through centrifugation, followed by electrochemical desulfurization to create defect sites. Subsequently, Pt and Pd nanoparticles were electrodeposited onto the S‐vacancies of MoS2–x nanosheets. The resulting PdPt nanoparticles, with a diameter of 3.3 ±1.7 nm, were distributed across the surfaces of the nanosheets. A preferential deposition was evident at the edges of the nanosheets, particularly when Pd was deposited first followed by Pt. Owing to this preferential deposition of Pd and Pt and the synergistic interaction of MoS2–x with Pd and Pt, the prepared catalyst exhibited a low overpotential of 30 mV at 10 mA cm−2, which is 2.7× lower than the MoS2–x alone. The prepared catalyst exhibited a 1.7× increase in the mass activity at 20 mV overpotential, relative to that of a commercial Pt/C nanocatalyst, showcasing its promising potential as an alternative catalyst.
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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".