Development of Zeolite‐Loaded Air‐Brushed Nanofibers for Oral Use and Its Characterization
Bibliographic record
Abstract
Abstract Zeolites are commercially available and naturally occurring minerals and synthetic materials, they are crystalline aluminosilicates with distinctive chemical and structural properties. Zeolites have a wide range of uses, including catalysis, gas separation, agronomy, animal feed additives, ecology, medicine, and cosmetics. The antibacterial activity of zeolite‐based nanofibers is fabricated and investigated in the current work utilizing a novel airbrushing method. Airbrushing is used to create zeolite‐loaded nanofibers with two groups of 5% and 10% zeolite, respectively. Scanning electron microscopy (SEM), Attenuation of Reflectance Fourier Transform Infrared Spectroscopy (ATR‐FT‐IR), contact angle measurement and an antibacterial test against Streptococcus mutans are used to characterize the fabricated zeolite‐loaded nanofibers. The SEM pictures demonstrate that the innovative strategy used in the study—air brushing—to effectively produce zeolite‐loaded nanofibers is a success. The morphology and diameter in nanometers is verified by SEM images for both groups. The ATR‐FT‐IR peaks confirm the blended compound and its chemical structure. The water contact angle measurement of the nanofibers of both groups show the hydrophobic qualities based on wettability. The findings of the zeolite nanofiber antibacterial test show a zone of inhibition (ZOI) of 19 mm compared to a positive control groups ZOI of 28 mm, indicating superior antibacterial activity against gram‐positive S. mutans. Based on the findings, it can be stated that zeolite nanofiber can be created by airbrushing and has antibacterial properties that can be utilized to stop dental caries, where S. mutans is the primary pathogenic organism.
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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".