New Insights into Materials for Pesticide and Other Agricultural Pollutant Remediation
Bibliographic record
Abstract
The increase in the world population and the intensification of agricultural practices have resulted in the release of several contaminants into the environment, especially pesticides and heavy metals. This article reviews recent advances in using adsorbent and catalytic materials for environmental decontamination. Different materials, including clays, carbonaceous, metallic, polymeric, and hybrid materials, are evaluated for their effectiveness in pollutant removal. Adsorption is an effective technique due to its low cost, operational simplicity, and possibility of adsorbent regeneration. Catalytic processes, especially those using metallic nanoparticles, offer high efficiency in degrading complex pesticides. Combining these technologies can enhance the efficiency of remediation processes, promoting a more sustainable and practical approach to mitigate the impacts of pesticides and other agricultural pollutants on the environment. Therefore, this review article aims to present several types of materials used as adsorbents and catalysts for decontaminating ecosystems affected by agricultural pollutants. It discusses recent works in literature and future perspectives on using these materials in environmental remediation. Additionally, it explores the possibilities of using green chemistry principles in producing sustainable materials and using agro-industrial waste as precursors of new materials to remove contaminants from the environment.
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.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".