Highly efficient nanosized MoS2/MoP heterocatalyst for enhancing hydrogen evolution reaction over a wide pH range
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Bibliographic record
Abstract
Energy consumption associated with the catalysts contributes partly to the high ohmic resistance arising from the low conductivity of the catalyst and poor charge transfer between nanoparticles, which has been difficult to study due to the complicated nanostructured framework of the catalysts. We constructed a novel heterostructure electrocatalyst (MoS 2 /MoP@NC) composed of nanosized MoS 2 /MoP heterostructures anchoring on hierarchical N-doped carbon for smoothing electron transfer in boosting hydrogen evolution reaction (HER). With the merits of large surface area, rapid charge transfer, and optimized electronic structure induced by charge transfer across the sufficient interface, the optimal MoS 2 /MoP@NC (MoSP) catalyst shows a competitive overpotential of 140 (0.5 M H 2 SO 4 ), 76 (1.0 M KOH), and 103 mV (0.5 M NaCl &1.0 M KOH) at 10 mA cm −2 , respectively. Raman experiment and Density functional theory (DFT) calculations reveal the formation of Mo-S-Mo bonds between MoS 2 and MoP, which favor enhancing the Femi level to facilitate the electron transfer, therefore regulating the electronic structure for the optimization of adsorption energy of hydrogen intermediate. Based on the experimental results, we constructed an energy consumption model of catalysts, where energy consumption comes from three aspects. The heterostructure design decreases the energy consumption of the catalysts greatly compared to the single-phase Mo-based catalyst of MoS 2 (78.0%) and MoP (45.2%) in alkaline electrolytes. • The catalyst (MoS 2 /MoP@NC) exhibits excellent catalytic performance for HER in acidic and alkaline electrolytes. • An energy consumption model of catalysts was constructed based on experimental data. • The design of MoS 2 /MoP@NCeffectively reduces three sources of energy consumption of catalysts in water electrolysis.
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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 | 0.000 | 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) | 0.000 | 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 it