Boron-Induced Surface Defects in Petcoke as Active Centers for Aerobic Oxidative Desulfurization
Bibliographic record
Abstract
Use of metal-free carbonaceous catalysts for efficient sulfur (S) removal from fuel through oxidative desulfurization (ODS), especially using stable oxygen (O 2 ), is limited and challenging as a result of their low O 2 activation efficiency. Instead, a considerable amount of research in this area was dedicated to less green peroxide-assisted oxidation as a result of their easier activation than O 2 by various metal/metal-free catalysts. In this study, we have synthesized a novel carbonaceous metal-free catalyst from petcoke (PC), a waste from the oil refinery process. The surface of PC was modified by doping heteroatom boron (B) through a solvent-free mechanochemical approach. B-doping-induced defects were hypothesized to generate electron redistribution over the adjacent carbon atoms in PC and provide active sites for the O 2 activation to O 2 • – during the aerobic ODS of the refractory sulfur compound dibenzothiophene (DBT). It was shown that the B loading in petcoke measured by inductively coupled plasma optical emission spectroscopy positively correlated to the DBT conversion as well as the defect concentration as measured by Raman spectroscopy. The active species were identified to be the sites of B–C, B–O, and BCO 2 atoms using X-ray photoelectron spectroscopy. Treatment of B-doped PC at 900 °C led to increased B–C interaction and defect concentration, producing 68% DBT conversion compared to 28% at 600 °C treatment, under reaction conditions of 110 °C and 3 h. A further increase in the DBT conversion to 96% was observed at 110 °C and 5 h for the 900 °C treated catalyst. Nearly 42% DBT conversion was observed at 120 °C and 3 h as a result of autoxidation (under no catalyst), which has not been considered in many of the earlier metal-free catalyzed DBT ODS studies. The kinetic analysis suggested a pseudo-first-order reaction for the DBT ODS.
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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".