Optimizing a Washing Procedure To Mobilize Polycyclic Aromatic Hydrocarbons (PAHs) from a Field-Contaminated Soil
Bibliographic record
Abstract
A laboratory study was conducted to assess the feasibility of a washing process with nonionic/anionic surfactant for the mobilization of PAH compounds from a field-contaminated soil. Soil washing was combined with surfactant regeneration and detoxification steps to generate innocuous products. Ultrasonication of field-contaminated soil with a 3% (w/v) surfactant suspension for 5 min mobilized appreciable quantities of all polycyclic aromatic hydrocarbon (PAH) compounds. Of the three surfactants, the Brij 98 formulation proved to be slightly more efficient for three successive extractions, mobilizing 88% of the soil PAH burden, whereas companion extractions using fresh reagents each time mobilized 89% of the soil PAH content. Formulating the Brij 98 surfactant in 0.1 M phosphate buffer (pH 8.0) increased the recovery of all PAHs as well as the recovery of surfactant (>90%), but soil residues exceeded permissible maxima for five- and six-ringed analytes. On the basis of the cumulative recoveries of PAH compounds in the aqueous fraction, five successive washes were predicted to reduce the residual soil burdens to legislatively permissible levels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".