Effects of TiO2, Ag-TiO2, and Cu-TiO2 nanoparticles on mechanical and anticariogenic properties of conventional pit and fissure sealants
Bibliographic record
Abstract
The objective of this study was to determine the effects of TiO2, Ag-TiO2, and Cu-TiO2 nanoparticles (NPs) addition on the mechanical and antibacterial properties of resin-based sealants. TiO2, Ag-TiO2, and Cu-TiO2 NPs were characterized with FTIR, Raman, SEM-EDX, TEM, XPS, and XRD, and evaluated for cytotoxicity study. After characterization, the nanoparticles were mixed with commercial pit and fissure sealants (PAFS) in ratios of 1% and 2%. A total of 7 groups were made, control group (PAFS only) and experimental groups (1%-2% TiO2, 1%-2% Ag-TiO2, and 1%-2% Cu-TiO2). ISO standards were adopted to prepare samples for mechanical properties, i.e., compressive strength (CS), flexural strength (FS), and Vickers hardness evaluation. Samples were tested against Streptococcus mutans through an agar well diffusion test. The CS, FS, and Vickers hardness were increased for the Cu-TiO2 group with respect to Ag-TiO2 but values were less compared to TiO2 groups. The highest flow rate was measured in the control group which was 8.16±0.06 mm and 9.17±0.1 mm after 3 and 10 mins respectively. In the agar well diffusion test, the control group showed no zone of inhibition, and the lowest zone of bacterial inhibition was found in PAFS with 1% TiO2 NPs group (13.3±1.5 mm) while the highest was found in PAFS with 2% Ag-TiO2 NPs (21.8±1.7 mm). Cu-doped TiO2 NPs showed more biocompatibility as compared to Ag-doped TiO2. The outcomes were statistically significant for all the mechanical tests and agar well diffusion antibacterial test as the p-value ≤0.05 while for the cytotoxicity test, the p-value >0.05. The TiO2 addition generally improved both the mechanical and antibacterial properties of pit and fissure sealant.
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.
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".