Water Cleaning Adsorptive Membranes for Efficient Removal of Heavy Metals and Metalloids
Bibliographic record
Abstract
Heavy metal pollution represents an urgent worldwide problem due to the increasing number of its sources; it derives both from industrial, e.g., mining, metallurgical, incineration, etc., and agricultural sources, e.g., pesticide and fertilizer use. Features of membrane technology are the absence of phase change or chemical additives, modularity and easy scale-up, simplicity in concept and operation, energy efficiency, and small process footprint. Therefore, if membrane technology is coupled to adsorption technology, one of the most effective treatment strategies to remove heavy metals, namely, Adsorptive Membrane Technology, many typical disadvantages of traditional processes to remove heavy metals, such as low-quality treated water, excessive toxic sludge production, which requires further treatment, can be overcome. In this review, after a broad introduction on the relevance of heavy metal removal and the methods used, a thorough analysis of adsorptive membrane technology is given in terms of strategies to immobilize the adsorbents onto/into membranes and materials used. Regarding this latter aspect, the impressive number of papers present in the literature on the topic has been categorized into five types of adsorptive membranes, i.e., bio-based, bio-inspired, inorganic, functionalized, and MMMs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".