Enhanced selectivity of a molecularly imprinted polymer toward the target molecule via esterification of non‐specific binding sites with diazomethane
Bibliographic record
Abstract
Diazomethane (CH(2)N(2)) was used to methylate the non-specific binding sites after molecularly imprinted polymer particles were prepared using methacrylic acid as the functional monomer, ethylene glycol dimethacrylate as the cross-linker and bisphenol A (BPA) as the template. After diazomethane treatment and subsequent removal of BPA by triethylamine, the treated molecularly imprinted polymer (TMIP) particles were tested for binding selectivity toward BPA and other organic compounds by capillary electrophoresis with ultraviolet detection. Even in the presence of compounds that were positively charged, neutral or negatively charged in the background electrolyte, BPA was selectively bound with the highest efficiency. A significant decrease in the affinity for metformin (MF, a positively charged compound), along with (13) C nuclear magnetic resonance spectra and electrophoretic mobility data, provided strong evidence for the elimination of non-specific -COOH binding sites in the TMIP particles. Only 8% of MF and 16% of diclofenac sodium salt (a negatively charged compound) remained as non-specific bindings because of hydrophobic interactions. Further comparison with poly(methyl methacrylate) revealed the true merits of the TMIP, which exhibited minimal non-specific bindings while preserving a high level of specific binding owing to molecular recognition.
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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 | 0.000 |
| 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".