Effect of Ormosil and Polymer Doping on the Morphology of Separately and Co-hydrolyzed Silica Films Formed by a Two-Step Aqueous Processing Method
Bibliographic record
Abstract
The entrapment of biomolecules within organic−inorganic nanocomposite materials derived by a sol−gel method has proven to be a viable route for the development of biosensors and biocatalysts. However, the phase separation behavior within nanocomposite materials formed by a protein-compatible two-step aqueous processing method is not well-understood. In this study, a range of imaging methods was used to assess the degree of heterogeneity in a series of dipcast thin films formed with different types and levels of ormosils in the presence and absence of polyethylene glycol (PEG), using both separate and co-hydrolysis of precursors. Both microscopic (bright-field and fluorescence microscopy) and nanoscale (atomic force microscopy and scanning electron microscopy) imaging demonstrate that short chain monofunctional ormosils such as methyltrimethoxysiline do not lead to significant heterogeneity when mixed with TEOS, while disubstituted (dimethyldimethoxysilane) or longer chain (isobutyltrimethoxysilane) ormosils show significant heterogeneity at the microscopic and nanoscopic scale when prepared by a separate hydrolysis method. The addition of PEG can improve the homogeneity in some materials, likely due to the coating of silica sol particles, which reduces microscopic phase separation; however, cohydrolysis of the precursors provides a more general route to create homogeneous materials. Interestingly, the heterogeneity observed by bright-field microscopy, which reflects variations in the refractive index, did not fully correlate with the fluorescence microscopy images of entrapped fluorophores, suggesting that chemical heterogeneity exists even when samples appear to be homogeneous. The implications of these findings for biosensor development will be discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".