Hollow NiSe Nanocrystals Heterogenized with Carbon Nanotubes for Efficient Electrocatalytic Methanol Upgrading to Boost Hydrogen Co‐Production
Bibliographic record
Abstract
Abstract Electro‐oxidative organic upgrading, as an ideal alternative to sluggish oxygen evolution reaction (OER) performance, can effectively decrease energy consumption to boost hydrogen evolution reaction (HER) performance. However, developing highly active electrocatalysts for long‐term durable organic upgrading with high selectivity at large and steady current density remains challenging. Herein, hollow NiSe nanocrystals heterogenized with carbon nanotubes (h‐NiSe/CNTs) are fabricated via a facile one‐pot approach. The highly dispersed h‐NiSe/CNTs 3D network can efficiently facilitate rapid mass/electron diffusion, thus achieving highly active and long‐term stable electrocatalysis for catalyzing methanol to value‐added formate at high and steady current density (≈345 mA cm −2 ) with high Faradaic efficiency (>95%). This reaction replaces sluggish OER performance to reduce the energy consumption for boosting H 2 generation by six times. The critical active species and methanol activation mechanism are systematically studied using X‐ray photoelectron spectroscopy, X‐ray absorption fine structure analysis, in situ Raman, and density functional theory calculations, indicating that the non‐ignorable SeO x collaborated with in situ formed NiOOH species can synergistically modulate the d band center to achieve an optimal adsorption for methanol selective oxidation and suppress the further oxidation to CO 2 , thus leading to active and stable electrolysis for producing value‐added formate with high selectivity and co‐generating H 2 with less energy consumption.
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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".