Preparation of a surface molecularly imprinted thermo-sensitive polymer for selective trypsin capture
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) are innovative porous materials created through polymerization templating methods for highly selective, sensitive detection and separation of the desired chemical species, known as template molecules. Surface imprinting over nano-sized substrates with special functional groups is an effective approach for improving the mass transfer and rebinding capacity. In this study, we successfully performed surface imprinting using methacrylic acid (MAA) and thermoresponsive N -isopropyl acrylamide (NIPAm) as functional monomers on acrylic-functionalized silica nanoparticles and served as a Trypsin artificial receptor. The resulting Trypsin imprinted polymer exhibited a binding capacity of 1022.44 mg/g, surpassing the non-imprinted polymer’s capacity of 292.41 mg/g within 45 min. Moreover, the imprinting factor, the main influence factor in MIP performance, was 3.5, demonstrating the significant selectivity of the polymeric products. In addition, due to the temperature sensitivity of NIPAm the template removing process was simply done by changing pore size. The silica nanoparticles and the synthesized MIP particles had diameters of approximately 95 nm and 105 nm, respectively, with the polymer layer thickness being around 5 nm. The MIP adsorption model followed the Freundlich isotherm and pseudo-second-order kinetics resulting in efficiencies of 93.0 % and 95.4 %, respectively. The developed Trypsin imprinted polymer was reusable for four consecutive uses,demonstrating both resilience and selectivity towards Trypsin.
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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 | 0.001 |
| 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".