Bioactivity, Degradation, and Mechanical Properties of Poly(vinylpyrrolidone-<i>co</i>-triethoxyvinylsilane)/Tertiary Bioactive Glass Hybrids
Bibliographic record
Abstract
Currently, composite and class I hybrid biomaterials are used for tissue regeneration applications. To improve and better control biomaterial properties, we synthesized class II organic/inorganic (O/I) hybrids, in which organic polymers and inorganic tertiary bioactive glass (TBG) were covalently cross-linked. To tailor their microstructure, bioactivity, degradation, and mechanical properties, we altered the degree of cross-linking by varying the amount of functional groups in the polymer that mediate covalent bonding to the TBG. We synthesized class II hybrids in a two-step process: first, vinylpyrrolidone (VP) and triethoxyvinylsilane (TEVS) were copolymerized at various molar ratios to obtain different amounts of silane functional groups in the copolymer; second, TBG and the copolymer were mixed and allowed to undergo hydrolysis and polycondensation forming Si-O-Si- and Si-O-P-bridging networks between the organic and inorganic phases. Higher amounts of functional groups increased copolymer-TBG covalent bonding and decreased degradation and the release of TBG dissolution products. Incubation in simulated body fluid led to biomimetic apatite deposition on the hybrid biomaterial surfaces, which was primarily dependent on O/I weight ratios. A higher TBG content improved apatite deposition and biocompatibility. Porous and interconnected three-dimensional scaffolds, fabricated by indirect 3D printing using polycaprolactone as a sacrificial template, had intriguing yield and compressive strengths, compressive moduli, and toughness. These studies demonstrate, for the first time, that the functionality of our synthesized copolymers greatly affects the nature of O/I matrix formation and degradation behavior of the class II hybrid biomaterials, creating possibilities for tailoring the physical, biochemical, and mechanical properties of scaffold biomaterials for tissue regeneration and related applications.
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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) | 0.000 | 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".