Controlling the Material Properties and Biological Activity of Lipase within Sol−Gel Derived Bioglasses via Organosilane and Polymer Doping
Bibliographic record
Abstract
The development of optical biosensors based on sol−gel entrapped proteins requires a detailed understanding of the evolution of the physicochemical properties of the material, their affects on protein function, and how these factors can be tailored by processing conditions. In this study, the polymer additives poly(vinyl alcohol) (PVA) and poly(ethylene glycol) (PEG) were dispersed into sol−gel processed materials derived from tetraethyl orthosilicate (TEOS) alone or copolymerized with methyltriethoxysilane (MTES) or dimethyldimethoxysilane (DMDMS), and their effects on the chemical and physical properties of the materials were monitored. In general, the physical properties, including transmittance and resistance to cracking, improved with increasing PEG concentration, but deteriorated with PVA content. The spectroscopic data obtained from entrapped 7-azaindole and 6-propionyl-2-(dimethylamino)naphthalene suggested that the inclusion of polymers and organic moieties into the matrix affected both the homogeneity of the materials and the polarity of the internal environment, with PEG reducing and PVA increasing the internal polarity. In light of these results, preliminary studies were performed on the effects of organic and polymer content on the initial and long-term activity of entrapped lipase. Concomitant with the material data, PVA tended to have a detrimental affect on lipase activity, while PEG provided a concentration-dependent enhancement of the enzyme activity. This study demonstrates for the first time that durable, optically transparent materials with significant lipase activity can be prepared and that optimal materials are produced with TEOS as a precursor and a few weight percent of low molecular weight PEG as an additive, with no need for organosilane precursors.
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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".