Desulphurization of Transportation Fuels by Per-Formic Acid Oxidant Using MoOx Loaded on ZSM-5 Catalyst
Bibliographic record
Abstract
Desulphurization of model and real oil samples was investigated using performic acid as oxidant assisted by air as co-oxidant. The catalysts used were Mo-oxide supported on ZSM-5 zeolite, which was synthesized in the laboratory and characterized by FT-IR, XRD, SEM and SSA analysis. In case of model oil, the optimum condition determined for complete oxidation of all the model compounds including thiophene, DBT and 4,6-DMDBT were; 60?C, 60 min, ambient pressure and air flow rate of 100 mL/min. The oxidation reactivity decreased from 4,6-DMDBT to DBT and thiophene, which was found to follow pseudo first order kinetics. The real oil sample used in the study included untreated naphtha (NP), light gas oil (LGO), heavy gas oil (HGO) and Athabasca bitumen (Bit.). In case of NP and LGO the sulfur removal of above 78% was attained whereas in case of HGO and Bit. samples about 60% of desulfurization was achieved.
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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".