Efficient and selective oxidation of furfural into high‐value chemicals by cobalt and nitrogen co‐doped carbon
Bibliographic record
Abstract
Abstract Supported non‐noble metal catalysts for the selective oxidation of furfural into high‐value chemicals have attracted considerable attention. Herein, an atom‐level‐isolated, cobalt‐catalyst‐supported N‐doped carbon (Co SAs‐N@C) was synthesized through a metal cobalt–tannic acid coordination assembly strategy. The obtained Co SAs‐N@C with 7.68 wt% of Co content was qualitatively and quantitatively characterized by X‐ray diffraction, X‐ray photoelectron spectroscopy, high‐resolution transmission electron microscopy, high‐angle annular dark‐field scanning transmission electron microscopy, inductively coupled plasma optical emission spectrometer, and X‐ray absorption fine structure techniques. From the structural properties, potassium thiocyanate poisoning, and acid leaching, it was found that the atomic‐dispersed CoNx species of the catalyst acts as the active site and plays a vital role in its highly effective oxidative esterification activity. The catalytic mechanism of this reaction is involved in radical oxidation, as confirmed by electron paramagnetic resonance spectra. The density functional theory calculations revealed that the reaction pathway proceeds as a hemiacetal pathway. Interestingly, the Co SAs‐N@C catalyst also showed satisfactory catalytic activity for the aerobic oxidation of furfural. Unexpectedly, the Co SAs‐N@C catalyst still possessed excellent stability and recyclability without loss of activity after five reuses.
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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".