Stepwise Adsorption of Phenanthrene at the Fly Ash–Water Interface as Affected by Solution Chemistry: Experimental and Modeling Studies
Bibliographic record
Abstract
Fly ash (FA) is predominantly generated from coal-fired power plants. Contamination during disposal of FA can cause significant environmental problems. Knowledge about the interaction of FA and hydrophobic organic pollutants in the environment is very limited. This study investigated the adsorption of phenanthrene at the interface of FA and water. The performance of phenanthrene adsorption on FA and the effects of various aqueous chemistry conditions were evaluated. The adsorption isotherms exhibited an increasing trend in the adsorbed amounts of phenanthrene, while a stepwise pattern was apparent. A stepwise multisite Langmuir model was developed to simulate the stepwise adsorption process. The adsorption of phenanthrene onto FA was noted to be spontaneous at all temperatures. The thermodynamic results indicated that the adsorption was an exothermic process. The adsorption capacity gradually decreased as pH increased from 4 to 8; however, this trend became less significant when pH was changed from 8 to 10. The binding affinity of phenanthrene to FA increased after the addition of humic acid (HA). The pH variation was also responsible for the changes of phenanthrene adsorption on FA in the presence of HA. High ionic strength corresponded to low mobility of phenanthrene in the FA-water system. Results of this study can help reveal the migration patterns of organic contaminants in the FA-water system and facilitate environmental risk assessment at FA disposal sites.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".