Enhanced mechanical properties of silicone hydrogels coated with metallic nanoparticles by using the laser-assisted process
Bibliographic record
Abstract
Silicone hydrogels coated with metallic nanostructures have attracted extensive attention because of their versatile applications in biomedical devices. However, few studies have been reported for characterizing the mechanical behavior of silicone hydrogel under biaxial tensile stresses. In addition, compared to most chemical coating processes, the deposition of nanostructures on silicone hydrogel by using a laser-assisted process can avoid chemical impurities and additional sterilization processes. Herein, a laser-assisted process is used for producing polyvinylpyrrolidone (PVP) modified silver (Ag) nanoparticles (Ag-PVP NPs), which are further deposited on silicone hydrogel. In the uniaxial mechanical test, the value of Young's modulus of Ag-PVP NP coated silicone hydrogels is increased as compared to that of silicone hydrogel. The energy absorption of silicone hydrogel and Ag-PVP NP coated silicone hydrogel measured by the uniaxial mechanical test is 15.137 ± 0.412 and 22.014 ± 0.186 MJ/m3, respectively. Furthermore, the biaxial test is applied to study the mechanical properties of silicone hydrogel coated with Ag-PVP NPs. Meanwhile, a constitutive model was applied to further understand the mechanical behavior of silicone hydrogel coated with Ag-PVP NPs. The result indicates that silicone hydrogel coated with Ag-PVP NPs shows a pseudo-elastic nonlinear behavior which is similar to collagen-based tissue substitutes.
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) | 0.001 | 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".