Nanocomposites of Terbium Sulfide Nanoparticles with a Chitosan Capping Agent for Antibacterial Applications
Bibliographic record
Abstract
This study aims to investigate the effect of alkaline pH on the bottom-up synthesis of nanocomposites (NCs) containing terbium sulfide nanoparticles (Tb2S3 NPs), where chitosan (CS) was employed as a capping agent, along with evaluation of the antibacterial activity of these NCs. The NCs were characterized using spectroscopy (FESEM-EDX, Raman, FTIR, XRD, XPS, and DLS), zeta-potential, and TGA. The results of FE-SEM, XPS, Raman, and FTIR characterization support the formation of CS-Tb2S3 NPs. A pH variation from 9 to 11 during composite formation was shown to affect the size and composition of NCs. The antibacterial activity of CS-Tb2S3 NCs was studied by coating onto commercial contact lenses, where the best loading efficiency of NCs was 48%. The NCs prepared at pH 10 (without contact lenses) had greater antibacterial activity against Staphylococcus aureus, with a zone of inhibition diameter of 7.15 mm. The coating of NCs onto commercial contact lenses was less effective for inhibition of Staphylococcus aureus, in contrast with the greater activity observed for tetracycline. CS-Tb2S3 NCs offer promising antimicrobial properties that can be further optimized by control of the surface loading and accessibility of Tb2S3 NPs through further study of the role of the chitosan capping agent, since steric effects due to CS are likely to attenuate antimicrobial activity via reduced electron transfer in such nanocomposite systems.
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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".