Adsorptive denitrogenation and desulfurization of diesel using activated carbons oxidized by (NH<sub>4</sub>)<sub>2</sub>S<sub>2</sub>O<sub>8</sub> under mild conditions
Bibliographic record
Abstract
(NH4)2S2O8 solutions were applied to oxidize a wood‐based activated carbon (AC) under different conditions. Oxidation decreases the specific surface area of AC samples but increases the oxygen functional groups. Carboxylic acid groups and ketone groups may facilitate the adsorption of nitrogen (N) and sulfur (S) compounds in light cycled oil (LCO). An appropriate adsorbent was determined as the oxidized carbon which shows high N removal from LCO. The resultant adsorbent, AC‐(NH4)2S2O8‐15%‐1.5, exhibits higher adsorption capacities for N and S compounds than the original carbon in dynamic adsorption of a model diesel fuel. Carboxylic acid groups and lactone groups might favour quinoline adsorption. Carboxylic anhydride groups and phenolic groups are likely to improve the adsorption of indole, carbazole and dibenzothiophene (DBT). The thermodynamic and kinetic behaviours of AC‐(NH4)2S2O8‐15%‐1.5 were investigated. The adsorption of carbazole on AC‐(NH4)2S2O8‐15%‐1.5 follows the Langmuir model, while that of quinoline, indole and DBT follows the Freundlich model. All the adsorption processes are spontaneous, with the nature of adsorption for indole and carbazole endothermic, and that for quinoline and DBT exothermic. The adsorption of the N and S compounds in this study follows a pseudo second‐order model. Intra‐particle diffusion is one of the rate‐controlled steps for N compounds.
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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".