Aromatic compound adsorption from aqueous solution on activated carbons^|^#8212;Effects of adsorbate polarity and surface functional groups^|^#8212;
Bibliographic record
Abstract
The effect of acidic functional groups on an activated carbon surface on the adsorption of benzene, phenol and nitrobenzene was examined. Adsorption experiments for these aromatics in aqueous solution were conducted using two types of activated carbon with large and small amounts of surface functional groups, DAOx and DAOxOG, respectively, to obtain the adsorption isotherms. Adsorption kinetics of nitrobenzene and phenol were also examined. The results showed that the adsorption amounts of these adsorbates were higher for DAOxOG than those for DAOx. However, the adsorbed amount of nitrobenzene on DAOx gradually increased as concentration increased, and the maximum adsorption capacity was close to that of DAOxOG. The different adsorption rates on DAOx were also observed between nitrobenzene and phenol. Two types of silica, MSU-2 and HMS, were also prepared to investigate the adsorption affinity of nitrobenzene and phenol for a hydrophilic surface. The amount of nitrobenzene adsorbed on each silica was higher than that of phenol. This indicated that nitrobenzene adsorbed more favourably on a hydrophilic surface than phenol. These results suggested that the difference in adsorptive behaviour of adsorbates on the adsorbents was due to the different adsorption mechanism of adsorbates, caused by the different polar characteristics of each substituent group.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".