The application of surfactant-enhanced soil washing process combined with adsorption using a recoverable magnetic granular activated carbon for remediation of PAH-contaminated soil
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Bibliographic record
Abstract
The efficiency of surfactant-enhanced soil washing combined with adsorption for PAHs removal from a real contaminated soil and the feasibility of surfactant recycling were investigated in this study. A synthesized magnetic granular activated carbon (MGAC) composite was used as the adsorbent and Tween 80 was the surfactant employed in this research. The experimental data for the adsorption of PAHs by MGAC in aqueous phase were best fitted with the Dubinin–Radushkevich isotherm model (0.66<R2<0.91), with the maximum PAH adsorption capacity of MGAC being in the range 6.2–10.9 µg/mL. According to the PAH solubilization tests, the increase in the Tween 80 dose from 1% to 5% (v/v) resulted in an increase in the total PAHs solubility up to ≈ 40%. The optimum ranges of the operational parameters for Tween 80-enhanced soil washing were determined as 5% Tween 80, a liquid to soil ratio of 10:1, a 72h washing time, and a temperature of 20°C. Under these conditions, the average PAHs percent removal from the contaminated soil was 67.6%. The remediation of contaminated soil samples using recycled 5% Tween 80 and 2% (w/w) MGAC (with no regeneration) was assessed in 7 successive cycles. The results indicated that the PAH removal efficiencies were 68.6, 70.7, 70.3, 61.6, 55.5, 50.2, and 39.4% for the repeated washing cycles, respectively. Tween 80 and the non-regenerated MGAC did not produce any waste or effluent after six times reuse in the treatment process. The surfactant adsorption tests showed that only 5 to 10% of Tween 80 would be adsorbed to the soil particles, suggesting the high recovery of the surfactant solution from the soil.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it