Emerging Tools for Recognition and/or Removal of Dyes from Polluted Sites: Molecularly Imprinted Membranes
Bibliographic record
Abstract
Dyes are used in different industries as textile, paper, food processing, cosmetic, leather tanning, rubber, printing and so on. These chemical substances have negative effect on the quality of the water and food, causing human diseases and environmental problems. In view of these aspects, colorant have attracted the interest of the scientists in developing efficient routes for their detection and/or removal from the polluted sites. Although traditional technologies used for removal of dyes are efficient, there is the necessity of developing innovative systems both more cheaply and of easy performance. In this scenario, the integration of the membrane science with the molecular imprinting technology is an alternative way that present many advantages such us the removal or detection of a specific dye or a class of dyes and cost reduction processes. In fact, exploiting the benefits of these two technologies it is possible to develop molecularly imprinted membranes able to recognize a dye of interest in specific mode. This potential is promising for combatting the illegal use of dyes in food, drinks and aquaculture as well as for their removal. The main positive aspects of the imprinted membranes are their chemical stability, reusability, as well as the resistance to the pH and temperature. In addition, their preparation requires short operation time and it is not expensive. All these properties have an encouraging impact in dealing with the problem of dyes contamination. This short review offers a description of the concept of molecular imprinting, starting from the approach of the synthesis of imprinted polymers until the description of the preparation of imprinted membranes. The application of imprinted polymers and membranes for the detection and/or removal of dyes from polluted sites will be also discussed.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".