Nanomaterials for sustainable remediation of chemical contaminants in water and soil
Bibliographic record
Abstract
Rapid growth in population, industry, urbanization and intensive agriculture have led to soil and water pollution by various contaminants. Nanoremediation has become one of the most successful emerging technologies for cleaning up soil and water contaminants due to the high reactivity of nanomaterials (NMs). Numerous publications are available on the use of NMs for removing contaminants, and the efficiencies are often improved by modifications of NMs with polymers, clay minerals, zeolites, activated carbon, and biochar. This paper critically reviews the current state-of-the-art NMs used for sustainable soil and water remediation, focusing on their applications in novel remedial approaches, such as adsorption/filtration, catalysis, photodegradation, electro-nanoremediation, and nano-bioremediation. Insights into process performances, modes of deployment, potential environmental risks and their management, and the consequent societal and economic implications of using NMs for soil and water remediation indicate that widespread acceptance of nanoremediation technologies requires not only a substantial advancement of the underpinning science and engineering aspects themselves, but also practical demonstrations of the effectiveness of already recognized approaches at real world in-situ conditions. New research involving green nanotechnology, nano-bioremediation, electro-nanoremediation, risk assessment of NMs, and outreach activities are needed to achieve successful applications of nanoremediation at regional and global scales.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".