Enzyme Encapsulation in Glycerol–Silicone Membranes for Bioreactions and Biosensors
Bibliographic record
Abstract
Efficient enzyme immobilization is one of the main challenges in biocatalysis. Properly immobilized enzymes ensure enzyme reusability and high conversion efficiency of biocatalytic reactions, which ultimately leads to reduction of the process costs and increased sustainability. Here a simple, versatile, and cost-efficient platform technology for physical encapsulation of enzymes within elastomeric membranes is presented. The membranes are obtained by simple mixing of two immiscible liquids, a commercial silicone prepolymer and glycerol, which results in formation of glycerol-in-silicone emulsions. Upon curing of the silicone phase free-standing elastomers are obtained, and the glycerol droplets are randomly dispersed in the silicone matrix. Enzymes are dissolved in the glycerol phase prior to the emulsification process; thus, each glycerol reservoir of the glycerol–silicone membrane becomes a microsize bioreactor. In a simple experiment an enzyme-containing glycerol–silicone membrane was immersed in water with dissolved substrate of a bioreaction. The concentration gradient induces the migration of the substrate to the glycerol reservoirs where it is converted by the enzyme to a product, which is subsequently released from the membrane. In this article the performances of processes involving diffusion and enzymatic reactions within the glycerol–silicone membranes are compared. The glycerol content in the membranes was found to have a significant impact on the reaction rate. This concept was also utilized to create a proof-of-concept elastomeric colorimetric glucose biosensor.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".