Nitrogen‐Doped Carbon Activated in Situ by Embedded Nickel through the Mott–Schottky Effect for the Oxygen Reduction Reaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of low-cost non-precious-metal electrocatalysts with high activity and stability in the oxygen reduction reaction (ORR) remains a great challenge. Heteroatom-doped carbon materials are receiving increased attention in research as effective catalysts. However, the uncontrolled doping of heteroatoms into a carbon matrix tends to inhibit the activity of a catalyst. Here, the in situ activation of a uniquely structured nitrogen-doped carbon/Ni composite catalyst for the ORR is demonstrated. This well-designed catalyst is composed of a nitrogen-doped carbon shell and embedded metallic nickel. The embedded Ni nanoparticles, dispersed on stable alumina with a high specific surface area for protecting them from agglomeration and in an unambiguous composite structure, are electron-donating and are shielded by the nitrogen-doped carbon from oxidation/dissolution in harsh environments. The electronic structure of the nitrogen-doped carbon shell is modulated by the transfer of electrons at the interface of nitrogen-doped carbon-Ni heterojunctions owing to the Mott-Schottky effect. The electrochemically active surface area result implies that the active sites do not relate to Ni directly and the enhanced catalytic activity mainly arises from the modulation of nitrogen-doped carbon by nickel. XPS and theoretical calculations suggest that the donated electrons are transferred to pyridinic N primarily, which ought to enhance the catalytic activity intrinsically. Benefiting from these transferred electrons, the half-wave potential of the nitrogen-doped carbon/Ni composite catalyst is 94 mV positively shifted compared to the Ni-free sample.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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