Green Technology Approach Towards the Removal of Heavy Metals, Dyes, and Phenols from Water Using Agro‐based Adsorbents: A Review
Bibliographic record
Abstract
The swift pace of socioeconomic development and climatic change have put significant strain on the quality of water resources. While, the bulk availability of agro-based materials arising from nature and agricultural practices has paved the way for researchers to utilize them in eradicating toxic industrial pollutants such as dyes, heavy metals, phenolic compounds, pesticides, etc. by using them as adsorbents. In the area of pollution remediation, inventive technologies have been developing. The adsorption technique stands out among the other wastewater treatment methods as it is simple, easy, efficient, and cost-effective. The agro-based adsorbents not only have great potential for the treatment of polluted water but also their use in this area contributes to minimizing natural waste. The agro-based adsorbent can be employed in its original raw form or after undergoing simple processes such as drying, grinding, and carbonization. Moreover, these adsorbents are typically modified physically or chemically to change their surface properties and improve their adsorption efficiency. The low-cost agro adsorbents have shown efficient adsorption capacities towards removing various organic and hazardous water pollutants. With a few exceptions, the majority of adsorbents have demonstrated heavy metals, dyes and phenol removal efficiencies exceeding 90 %. This review summarises the available information and strategies for using agro-based adsorbents to eliminate hazardous water pollutants. It is a prospective area for research in the field of environmental pollution.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".