Valorization of Fibrous Plant-Based Food Waste as Biosorbents for Remediation of Heavy Metals from Wastewater—A Review
Bibliographic record
Abstract
Mobilization of heavy metals in the environment has been a matter of concern for several decades due to their toxicity for humans, environments, and other living organisms. In recent years, use of inexpensive and abundantly available biosorbents generated from fibrous plant-based food-waste materials to remove heavy metals has garnered considerable research attention. The aim of this review is to investigate the applicability of using fibrous plant-based food waste, which comprises different components such as pectin, hemicellulose, cellulose, and lignin, to remove heavy metals from wastewater. This contribution confirms that plant-fiber-based food waste has the potential to bind heavy metals from wastewater and aqueous solutions. The binding capacities of these biosorbents vary depending on the source, chemical structure, type of metal, modification technology applied, and process conditions used to improve functionalities. This review concludes with a discussion of arguments and prospects, as well as future research directions, to support valorization of fibrous plant-based food waste as an efficient and promising strategy for water purification.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".