Mechanical and antimicrobial properties of green and photoactive <scp> AgTiO <sub>2</sub> </scp> /poly(3hydroxybutyrate) ( <scp>PHB</scp> ) electrospun membranes
Bibliographic record
Abstract
Abstract The rise of antibiotic‐resistant microbes and concerns over non‐biodegradable waste highlight the need for innovative materials with integrated functionalities that address these critical issues. In this study, we synthesized poly(3hydroxybutyrate) (PHB) electrospun nanofibers blended with gelatin (Ge) and loaded with photoactive AgTiO 2 nanoparticles with improved mechanical and biological properties. Biological tests revealed excellent antibacterial activity of the prepared membrane, with efficiency exceeding 99% against Escherichia coli and 95% against Staphylococcus epidermidis after 90 and 60 min of exposure to low‐power commercial LED lights, respectively. Filtration studies using a dead‐end stainless‐steel cell reveal that both bacteria are eliminated (>99% after one filtration cycle). Results of the viability test showed that blending PHB with Ge improves the membrane's anti‐biofouling properties. The membranes were also characterized using SEM and EDX mapping techniques for morphological and elemental analysis, DSC and TGA to evaluate thermal properties and crystallinity, and FTIR to confirm chemical structure. Moreover, the electrospun membranes exhibited enhanced mechanical properties with the addition of Ge. PHB/Ge/3 wt% AgTiO 2 sample showed 2.2 times better tensile strength and 1.89 times improved Young's modulus compared to PHB membranes. Finally, the breakdown of PHB/Ge/AgTiO 2 membranes occurred progressively over only 8 weeks, showing the membranes' green and sustainable nature. Highlights Antibacterial efficacy of 99% against E. coli and 95% against S. epidermidis . Microfiltration efficiency >99% achieved in one filtration cycle. Mechanical strength is enhanced by 2.2 times with the addition of gelatin. Effective anti‐biofouling is confirmed by CLSM and SEM tests. Membranes degraded within 8 weeks in natural soil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".