The application of non-oxidizing biocides to prevent biofouling in reverse osmosis polyamide membrane systems: a review
Bibliographic record
Abstract
Abstract Biofouling of polyamide membranes is one of the main barriers faced by reverse osmosis (RO) technologies to supply fresh water. Currently, biofouling is addressed by feed water pretreatment using chlorine, followed by membrane cleaning. Chlorine damages polyamide membranes and also generates harmful disinfection byproducts. Thus, safer strategies are needed to prevent biofouling in polyamide membrane systems. This review investigates the applicability of the following non-oxidizing biocides in preventing and controlling biofouling in RO systems, including their antimicrobial efficiency, hazard levels, membrane compatibility, and applicability to drinking water treatment: (1) 2,2-dibromo-3-nitrilopropionamide (DBNPA); (2) 2-methyl-4-isothiazolin-3-one (MIT); (3) sodium bisulfite (SBS), (4) phenoxyethanol (PE), (5) sodium benzoate (SB). According to this review, MIT and DBNPA present most of the features attributed to an ideal anti-biofouling chemical but also are the most hazardous biocides. Due to safety and efficacy, none of the five chemicals were determined to be the final solution to address membrane biofouling. However, alternative RO biocide research is in early development and requires further investigation via biofouling prevention studies. Therefore, future research efforts on the investigation of economic, eco-friendly, and safe antifouling agents to prevent and treat biofouling in RO systems are paramount to promote sustainable water supply in water-stressed countries.
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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) | 0.001 | 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".