Remediation of DDT‐contaminated soil using optimized mixtures of surfactants and a mixing system
Bibliographic record
Abstract
Abstract Soil contaminated with persistent pesticides, such as DDT, poses a serious risk to humans and to wildlife. A surfactant‐aided soil‐washing technique was studied as an alternative method for remediation of DDT‐contaminated soil. An ex situ soil washing method was investigated using nonionic and anionic surfactants due to the clayey structure of the contaminated soil. A mixture of 1 percent nonionic surfactant (Brij 35) and 1 percent anionic surfactant (SDBS) removed more than 50 percent of DDT from soil in a flow‐through system, whereas individual surfactants or other combinations of the surfactants had a lower removal efficiency. The soil‐washing technique was improved using a mixing system. The mixture of surfactants was optimized in the mixing system, and the combination of 2 percent Brij 35 and 0.1 percent SDBS was found to be optimum, removing 70 to 80 percent of DDT. Prewashing of the soil with tap water decreased the adsorption of surfactants to soil particles by 30 to 40 percent, and postwashing recovered 90 percent of the surfactants. © 2010 Wiley Periodicals, Inc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".