Rapid polyamide membrane compatibility testing of potential anti-biofouling agents for reverse osmosis membrane systems
Bibliographic record
Abstract
ABSTRACT The detrimental impacts of biofouling on reverse osmosis (RO) membrane (ROM) installations is one of the main technical barriers faced by RO technology to provide potable water. To unveil safer alternatives for biofouling prevention in ROM installations, this work assesses the ROM compatibility of three potential low-hazard anti-biofouling agents (lauroyl arginate ethyl (LAE), phenoxyethanol (PE), and sodium benzoate (SB)) via a proposed rapid membrane degradation test. This study offers a cost-effective screening tool to select biocides for extensive compatibility studies. Attenuated total reflectance – Fourier-transform infrared spectroscopy, atomic force microscopy, and scanning electron microscopy assessed ROM surface damage due to biocide exposure. LAE did not show significant morphological or chemical membrane damage at any experimental conditions (exposure time: 1, 8, and 24 h; pH: 4, 7, 9; concentration: 100 mg/L, 50 g/L, 100 g/L, and 150 g/L). However, results indicated that exposure to PE and SB led to membrane degradation. The proposed rapid membrane degradation test showed to be an excellent tool for improving current membrane compatibility testing practices. It identified two ROM-damaging biocides, SB and PE, streamlining large-scale testing efforts. Additionally, it identified a promising biocide, LAE, with the potential to address biofouling in RO systems, prompting further long-term compatibility studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".