Assessment of Food Processing and Pharmaceutical Industrial Wastes as Potential Biosorbents: A Review
Bibliographic record
Abstract
There is a growing need for the use of low-cost and ecofriendly adsorbents in water/wastewater treatment applications. Conventional adsorbents as well as biosorbents from different natural and agricultural sources have been extensively studied and reviewed. However, there is a lack of reviews on biosorption utilizing industrial wastes, particularly those of food processing and pharmaceuticals. The current review evaluates the potential of these wastes as biosorbents for the removal of some hazardous contaminants. Sources and applications of these biosorbents are presented, while factors affecting biosorption are discussed. Equilibrium, kinetics, and mechanisms of biosorption are also reviewed. In spite of the wide spread application of these biosorbents in the treatment of heavy metals and dyes, more research is required on other classes of pollutants. In addition, further work should be dedicated to studying scaling up of the process and its economic feasibility. More attention should also be given to enhancing mechanical strength, stability, life time, and reproducibility of the biosorbent. Environmental concerns regarding disposal of consumed biosorbents should be addressed by offering feasible biosorbent regeneration or pollutant immobilization options.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".