Antibacterial activity of combination of synthetic and biopolymer non-woven structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Fibrous structures and synthetic polymer blends offer potential usages in making biomedical devices, textiles used in medical practices, food packaging, tissue engineering, environmental applications and biomedical arena. These products are also excellent candidates for building scaffolds to grow stem cells for implantation, to make tissue engineering grafts, to make stents to open up blood vessels caused by atherosclerosis or narrowed by blood clots, for drug delivery systems for micro- to nano-medicines, for transdermal patches, and for healing of wounds and burn care. The current study was designed to evaluate the antimicrobial activity of woven and non-woven forms of nano- and macro-scale blended polymers having biocompatible and biodegradable characteristics. METHODS: The antimicrobial activity of non-woven fibrous structures created with the combination of synthetic and biopolymer was assessed using Gram-negative, Gram-positive bacteria, such as Staphylococcus aureus, Proteus vulgaris, Escherichia coli and Enterobacter aerogenes using pour plate method. Structural evaluation of the fabricated samples was performed by Fourier transform infrared spectroscopy. RESULTS: Broad spectrum antibacterial activities were found from the tested materials consisting of polyvinyl alcohol (PVA) with chitosan and nylon-6 combined with chitosan and formic acid. CONCLUSIONS: The combination of PVA with chitosan was more bactericidal or bacteriostatic than that of nylon-6 combined with chitosan and formic acid. PVA combination with chitosan appears to be a broad-spectrum antimicrobial agent.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it