Design and Optimization of Molecularly Imprinted Polymer Targeting Epinephrine Molecule: A Theoretical Approach
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) are a growing highlight in polymer chemistry. They are chemically and thermally stable, may be used in a variety of environments, and fulfill a wide range of applications. Computer-aided studies of MIPs often involve the use of computational techniques to design, analyze, and optimize the production of MIPs. Limited information is available on the computational study of interactions between the epinephrine (EPI) MIP and its target molecule. A rational design for EPI-MIP preparation was performed in this study. First, density functional theory (DFT) and molecular dynamic (MD) simulation were used for the screening of functional monomers suitable for the design of MIPs of EPI in the presence of a crosslinker and a solvent environment. Among the tested functional monomers, acrylic acid (AA) was the most appropriate monomer for EPI-MIP formulation. The trends observed for five out of six DFT functionals assessed confirmed AA as the suitable monomer. The theoretical optimal molar ratio was 1:4 EPI:AA in the presence of ethylene glycol dimethacrylate (EGDMA) and acetonitrile. The effect of temperature was analyzed at this ratio of EPI:AA on mean square displacement, X-ray diffraction, density distribution, specific volume, radius of gyration, and equilibrium energies. The stability observed for all these parameters is much better, ranging from 338 to 353 K. This temperature may determine the processing and operating temperature range of EPI-MIP development using AA as a functional monomer. For cost-effectiveness and to reduce time used to prepare MIPs in the laboratory, these results could serve as a useful template for designing and developing EPI-MIPs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".