Nanomaterials for sustainable remediation of chemical contaminants in water and soil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <i>in-situ</i> 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.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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