Facile Preparation of Metallic Sites Anchored Nanocarbon Materials for Electrocatalysis
Bibliographic record
Abstract
Abstract Herein, a novel method, heat mechanochemical synthesis (HMCS), is reported for facile and scalable preparation of electrocatalysts with metallic sites anchored on carbon materials. The method simultaneously provides mechanical energy and heat to drive reaction, by which target metal atoms are efficiently trapped in N‐doped and defect‐rich carbon that prepared in advance (NC). The advantages of this method are: (1) high contents of metals at the outermost surface; (2) unique metal sites formed under collisions with mechanical energy; and (3) not annealed at high temperatures, which would retain some unique active sites on the materials. A series of unitary transition metal (Cr, Mn, Fe, Co, Ni, Cu, or Zn) superficially anchored carbons are obtained via the HMCS. More than this, it is quite convenient to prepare multiple metals, such as combinations among these metals, highly dispersed and anchored on the carbon materials in one time. As a representative, the as‐prepared Fe‐NC (denoted as FeNCHM in context) is investigated in detail as catalyst for ORR and Zn‐air battery applications. The authors envision that the method of HMCS provides a good support of surface engineering for preparation of metals sites containing carbon at large scale.
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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".