Morphology and Entrapped Enzyme Performance in Inkjet-Printed Sol–Gel Coatings on Paper
Bibliographic record
Abstract
We recently reported on the multilayer printing of sol–gel/enzyme bioinks onto porous filter paper to create bioactive paper test strips. The method involves printing of four inks: a polymer underlayer, a sol–gel-based silica layer, an acetylcholinesterase (AChE) enzyme layer, and finally a top layer of silica. To improve our understanding of the nature of these printed materials, filter paper printed with various ink components was characterized by activity assays (with and without protease treatment), confocal microscopy to assess the location and mixing of layers, and scanning electron microscopy of deposited inks to assess the morphology and ink location. Although the silica and enzyme solutions were printed sequentially, they formed a composite material within the porous paper network and coated only the fibers as a 35 ± 15 nm thin film without filling the macropores. The silica coating on the cellulose fibers was sufficiently flexible to allow bending of the paper substrate, unlike traditional silica thin films. The protease assay results showed that the AChE was more protected as the amount of sol–gel-derived silica printed on paper was increased. The top layer of sol–gel ink was found to play a critical role in protection against proteolysis, while the bottom layer of sol–gel ink was found to be necessary to prevent the potential inhibition of AChE by the cationic polymer (used as a capture agent for the product of the enzymatic reaction). Overall, the data show that inkjet-printed sol–gel materials form thin, protein-entrapping films that are suitable for the production of printed biosensors.
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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 | 0.000 | 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 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".