Development and Characterization of Antibacterial Polymer Blends Composed of PVA, PAAm, and PEG Integrated with Carbon Nanotubes
Bibliographic record
Abstract
The PVA-PAAm-PEG polymer blends and their corresponding nanocomposite films were synthesized using the solution casting method, with carbon nanotubes (CNTs) added in varying concentrations of 0.005 g, 0.010 g, 0.015 g, and 0.020 g.Structural characterization was carried out through FTIR spectroscopy, which revealed that the interactions were predominantly physical, indicating the absence of new chemical bonds or interatomic interactions.SEM analysis confirmed this finding by demonstrating a uniform dispersion of CNTs within the polymer matrix.Through the evaluation of their absorption spectra over the 200-800 nm wavelength range, the optical properties were investigated.The results indicated a progressive increase in optical absorption with higher CNT concentrations.Concurrently, the optical band gap was observed to decrease from 4.8 eV to 3.6 eV.The refractive index similarly exhibited increasing levels of CNT incorporation.Both the dielectric constant and dielectric loss were found to increase with higher CNT concentrations, while they showed a decrease with higher applied electric field frequencies.On the contrary, AC conductivity demonstrated enhancement with increasing frequency, particularly at 102 Hz.Additionally, the antibacterial properties of the nanocomposites were tested against Escherichia coli (E.coli), revealing augmented antibacterial activity as the CNT concentration increased.This highlights the potential application of these nanocomposites in antimicrobial technologies.
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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) | 0.000 | 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".