Biodegradable polyhydroxybutyrate microfiber membranes decorated with photoactive <scp>Ag‐TiO<sub>2</sub></scp> nanoparticles for enhanced antibacterial and anti‐biofouling activities
Bibliographic record
Abstract
Abstract Developing advanced materials with antibacterial and antifouling activities offers an adequate protection solution against surface bacterial contamination—a common cause of infection threatening human health. The current work reports the preparation of polyhydroxybutyrate (PHB) microfiber membranes decorated with photoactive Ag‐TiO2 nanoparticles using electrospinning coupled with dip‐coating methods. The decoration of Ag‐TiO2 nanoparticles strongly enhances the antibacterial properties of prepared membranes, particularly under light illumination, thanks to their photocatalytic activity. The best‐performing sample exhibits potent antibacterial efficiency exceeding 99% against Escherichia coli and Staphylococcus epidermidis after 3 and 1 h of exposure to low‐power commercial LED light, respectively. The prepared samples also display excellent reusability with an insignificant antibacterial activity decrease after three cycles (<2% loss in antibacterial efficiency). Furthermore, these samples effectively prevent bacterial fouling due to their potent antibacterial properties. Notably, despite the strong antibacterial effect, decorated Ag‐TiO2 nanoparticles promote the adhesion of microorganisms, accelerating the biodegradation of PHB microfibers. As a result, the prepared microfiber membranes with nanoparticle decoration exhibit biodegradability comparable to the non‐decorated membrane, with the soil degradation rates reaching almost 99% after only 6 weeks.
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) | 0.000 | 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".