Efficacy testing of non-oxidizing biocides for polyamide membrane biofouling prevention using a modified CDC biofilm reactor
Bibliographic record
Abstract
Abstract Biofouling is one of the most challenging obstacles faced by reverse osmosis (RO) membrane systems to supply potable water. Currently, biofouling is imperfectly handled by RO feed water pre-chlorination, which is associated with the production of carcinogenic disinfection by-products. To propose a safer alternative to control biofouling in RO drinking water applications, this study investigates the efficacy of five biocides to prevent and remove Pseudomonas aeruginosa biofilms from RO membranes: 2-methyl-4-isothiazolin-3-one (MIT); 2,2-dibromo-3-nitrilopropionamide (DBNPA); sodium bisulfite (SBS); sodium benzoate (SB); and ethyl lauroyl arginate (LAE). Experiments were conducted on the Center for Disease Control (CDC) Biofilm Reactor (CBR) with biocidal dosing estimated on 96-well microtiter plates. Confocal Scanning Laser Microscopy (CLSM) and Scanning Electron Microscopy (SEM) were used to analyze the biocides' anti-biofilm efficacies under dynamic conditions relative to minimum biofilm inhibitory and eradication concentrations. The results in this study indicated that LAE presented the best anti-biofilm efficacies in treating P. aeruginosa biofilms when compared to all studied biocides; it not only prevented biofilm formation (>98%) but also it effectively removed pre-established biofilms (>99%) from RO membrane coupons. Therefore, due to safety and efficacy, LAE is an excellent candidate for controlling biofouling in drinking water RO membrane systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".