Remediation of organic contaminated soil by Fe-based nanoparticles and surfactants: a review
Bibliographic record
Abstract
Surfactants and nanoparticles have been effectively used for environmental remediation for many years. Over the years, various methods have been developed to synthesize nanoparticles using different surfactants to obtain a s higher treatment efficiency for organic contaminated soil. Compared to conventional remediation methods, the in-situ remediation technique provides advantages, such as being more eco-friendly and cost-effective. This review provides an overview of the remediation of organic contaminated soil using surfactant-stabilized Fe-based nanoparticles, mostly surfactant-stabilized nanoscale zero-valent iron (nZVI). In addition, the use of other stabilizers and the mechanisms of stabilization are discussed. The combination of surfactants and Fe-based nanoparticles can be effectively used to remediate organic contaminants from soil, such as trichloroethylene (up to 99%), polychlorinated biphenyls (up to 80%), perchloroethylene (up to 93%). The treatment efficiency organic contaminants in soil by surfactant-stabilized nanoparticles is higher than only surfactant (less than 90%) or nanoparticles (less than 80%) due to the synergistic effects between surfactants and nanoparticles. This technique is generally more effective to use as a strong reductant, such as reductive dehalogenation or reductive immobilization of metals, while less cost-effective as an adsorbent. In addition, the remediation rate depends on various factors, such as pH, temperature, natural organic matter, ionic strength, type and concentration of stabilizers, site characteristics, contaminant features, nanoparticle and surfactant properties. However, short lifetimes or potential toxicity of nanoparticles are some limitations of this technique.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".