Inhibitory Effect of Vanillin on Biofilm Formation by Multi-Species Wastewater Culture
Bibliographic record
Abstract
This paper mainly investigates the inhibitory effect and mechanism of vanillin on mixed-culture biofilm formation, and discusses the feasibility of controlling membrane fouling with vanillin. The biofilms were cultured under static conditions and a microfluidic chamber, respectively. The results show that vanillin can effectively inhibit the microbial adhesion and biofilm formation; the inhibition rate increases with the vanillin concentration. After 12h treatment with 300mg/L vanillin, the inhibition rate of biofilm formation reached 85.34%. Besides, vanillin can mitigate bacterial adhesion by reducing the microbial secretion of exoprotein and exopolysaccharide, exhibit an inhibitory effect on the expression of N-acyl-l-homoserine lactones (AHLs), but had no effect on environmental deoxyribonucleic acid (eDNA). After being treated with 300mg/L vanillin, exoprotein, exopolysaccharide and AHLs decreased by 28.48%, 17.23% and 46.64%, respectively. Furthermore, there is a positive correlation between the AHLs and the content of extracellular polymeric substances (EPSs); the AHLs-mediated quorum sensing (QS) might be related to microbial adhesion through the regulation of EPS. Finally, vanillin has a great potential in membrane fouling control. The research findings provide a good reference for biofilm control in water purification and wastewater treatment systems.
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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) | 0.000 | 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 | 0.001 |
| 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".