Functionalization of bacterial cellulose-based nanofibrous surfaces with antibacterial moieties for membrane biofouling mitigation
Bibliographic record
Abstract
Nanofibrous membranes have garnered considerable attention for filtration purposes owing to their capability to endure high fluid flow rates while effectively removing micro- and nano-sized pollutants from solutions. However, biofouling remains a significant issue in the nanofibrous membrane processes as it leads to reduced permeate flux, increased energy costs, and shortened lifespan of membranes. Here we report the functionalization of nanofibrous bacterial cellulose (BC) membrane by simple impregnation for filtration of bio-effluents, including organic matter and microbe containing solutions. Antibacterial and anti-biofouling properties were imparted to the BC nanofibers by a custom-tailored silane with picolinic acid named TCPA. Functionalized BC membranes with silver nanoparticles were used as control to compare filtration, antibacterial and anti-biofouling properties. Cross-flow filtration tests showed that functionalization with silane did not compromise the membrane permeation characteristics, instead, it supported in flux enhancement due to the increase in the hydrophilic character of the silane-treated BC. In a series of bacterial challenge tests, the functionalized membranes significantly reduced E. coli adhesion compared to pristine bacterial cellulose membranes and the control membrane, demonstrating strong anti-biofouling potential in settings where long-term exposure to high microbial loads is anticipated. This study highlights the potential of tailored functionalization of nanofibrous membranes with TCPA as filters for water treatment systems, offering a combination of anti-biofouling and intrinsic antibacterial advantages.
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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) | 0.000 | 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) | 0.001 | 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".