Metal‐Organic Framework‐Derived Fe‐Doped Co<sub>1.11</sub>Te<sub>2</sub> Embedded in Nitrogen‐Doped Carbon Nanotube for Water Splitting
Bibliographic record
Abstract
Abstract A rational design is reported of Fe‐doped cobalt telluride nanoparticles encapsulated in nitrogen‐doped carbon nanotube frameworks (Fe‐Co 1.11 Te 2 @NCNTF) by tellurization of Fe‐etched ZIF‐67 under a mixed H 2 /Ar atmosphere. Fe‐doping was able to effectively modulate the electronic structure of Co 1.11 Te 2 , increase the reaction activity, and further improve the electrochemical performance. The optimized electrocatalyst exhibited superior hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performances in an alkaline electrolyte with low overpotentials of 107 and 297 mV with a current density of 10 mA cm −2 , in contrast to the undoped Co 1.11 Te 2 @NCNTF (165 and 360 mV, respectively). The overall water splitting performance only required a voltage of 1.61 V to drive a current density of 10 mA cm −2 . Density function theory (DFT) calculations indicated that the Fe‐doping not only afforded abundant exposed active sites but also decreased the hydrogen binding free energy. This work provided a feasible way to study non‐precious‐metal catalysts for an efficient overall water splitting.
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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) | 0.000 | 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 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".