Evaluating the mechanical, thermal, and antibacterial properties of poly (lactic acid)/silicone rubber blends reinforced with (3‐aminopropyl) triethoxysilane‐functionalized titanium dioxide nanoparticles
Bibliographic record
Abstract
Abstract Poly (lactic acid)/silicone rubber (PLA/SR) blends were reinforced with (3‐aminopropyl) triethoxysilane‐functionalized titanium dioxide nanoparticles, and the effect of the virgin (TDO) and functionalized (FTDO) nanoparticles was investigated in the presence and absence of compatibilizer. The results demonstrated that the functionalization of TDO had no negative effect on the morphology of the blends, and no aggregation was seen in the blends containing TDO and FTDO. Furthermore, nanoparticles were localized at the interface of PLA and SR. Adding TDO in the PLA/SR blends increased the mechanical properties. The same trend was observed after incorporating the compatibilizer and FTDO. Additionally, a good agreement was seen between the theoretical and experimental mechanical properties values. Moreover, introducing SR diminished the crystallinity of PLA, and further reduction was observed by adding TDO, FTDO, and compatibilizer. Also, the modification of TDO did not affect the crystallinity of the blends. Incorporating 1 and 2 phr TDO to the blends increased the thermal stability while diminishing with 3 phr TDO. In addition, FTDO improved thermal stability. The antibacterial test indicated that the higher nanoparticles content, the better the antibacterial properties.
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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".