Positively Charged Pt‐Based Nanoreactor for Efficient and Stable Hydrogen Evolution
Bibliographic record
Abstract
Abstract Positively charged Pt can work as the active center for hydrogen evolution reaction (HER) but the corresponding design of state‐of‐the‐art electrocatalysts at high current densities has never been realized. Here the application of positively charged Pt in an effective Fe‐PtNiPO nanoreactor for highly efficient and stable HER is demonstrated. Synchrotron radiation X‐ray absorption spectroscopy confirms the formation of internal positively charged Pt and the in situ experiments reveal the quick charge transfer in the nanoreactor. Ni‐based materials around Pt are used to tune the electronic structure and promote the water dissociation to form locally enriched H+, while a porous Fe shell can both prevent the loss of active material and allow the efficient material transport. All the beneficial compositions work together to form an effective nanoreactor for HER. As a result, the Fe‐PtNiPO nanoreactor shows a low overpotential of 19 mV to achieve 10 mA cm−2 and exhibits a high mass activity of 10.93 A mgPt−1 (at 100 mV). Most importantly, it only needs an ultra‐low overpotential of 193 mV to achieve a high current density of 1000 mA cm−2 with an excellent stability over 300 h, which represents one of the best electrocatalysts for alkaline HER and might be used for large‐scale industrial application in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".