A Promising Surfactant for Enhanced Sorption and Desorption of Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
Gemini surfactants, an innovative class of amphiphilic molecules, are of increasingly scientific interest due in part to their effectiveness in soil/water remediation. This study was carried out to investigate the overall partitioning of three representative polycyclic aromatic hydrocarbons (PAHs) in the soil-water–surfactant system which takes into consideration the soil-sorbed cationic Gemini surfactant, presence of Gemini micelles, and related developed coefficients. The results indicated that the adsorption of Gemini surfactants onto soil particles through both cation exchange and hydrophobic interaction contribute to the bi or multilayer formation. The sorbed C 12–3–12 studied herein, is a highly effective partitioning media for PAHs to adsorb onto the soil phase from the aqueous phase and thus, can be considered as a good adjuvant for an enhanced sorption zone. The partitioning behavior of PAHs in the soil–water–Gemini surfactant has a strong relationship with their K ow . The experimental results from this research will be used to gain an understanding of the effect of cationic Gemini surfactant on the distribution of HOCs in a soil-water system and provide some fundamental and valuable information in remediation of contaminated soils and waters.
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) | 0.000 | 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".