Adsorption behaviours of sulfonated humic acid at fly ash‐water interface: Investigation of equilibrium and kinetic characteristics
Bibliographic record
Abstract
Abstract Sulfonated humic acid (SHA) has been widely used in oilfield production and it can significantly contribute to COD, BOD, sulphurated pollutants, and other toxic substances when discharged into the environment. There is an urgent need for effective and safe remediation of such toxic compounds from wastewater. This study investigated the adsorption characteristics of SHA onto coal fly ash. The removal percentage increased with fly ash dosages up to 1 and 6 g/L for Shand and BD fly ash, respectively, and it reached a constant level at a certain fly ash dosage. The Langmuir isotherm model could better fit equilibrium data for the adsorption of SHA on fly ash. Kinetic analysis showed that this process can be described well by a pseudo‐second‐order model. The adsorption of SHA on fly ash was improved by increasing temperature from 15 to 35 °C, and the thermodynamics results indicated that the adsorption was endothermic in nature. In addition, both ionic strength and pH had an influence on the adsorption performance. Fly ash showed the potential to be an effective adsorbent for the removal of SHA from an aqueous phase. The results can be used to reveal the migration patterns of organic contaminants at the fly ash surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".