Capillary electrophoresis characterization of molecularly imprinted polymer particles in fast binding with 17β‐estradiol
Bibliographic record
Abstract
Molecularly imprinted polymer (MIP) submicron particles were synthesized, using either ethylene glycol dimethacrylate or trimethylolpropane trimethacrylate as a cross-linker, specifically for recognition of 17β-estradiol (E2). HPLC with fluorescence detection (HPLC-FD) results showed that 90(±5)% of E2 bound onto these particles after 2 min of incubation, and 96(±3)% after long equilibrium. The binding capacity was 8(±3) μmol/g for MIP particles prepared using ethylene glycol dimethacrylate, and 33-43(±8) μmol/g for using trimethylolpropane trimethacrylate. CE separation of MIP and non-imprinted polymer particles was successful when 50 mM borate buffer (pH 8.5) containing 0.005% w/v EOTrol™ LN in reverse polarity (-30 kV) was used. The electrophoretic mobilities of MIP and non-imprinted polymer particles, together with dynamic light scattering measurement of particle sizes, allowed for an estimation of their surface charges. Automated injection of E2 and particles in mixture set a lower limit of 20(±1) s on incubation time for the study of fast binding kinetics. The presence of E2 and bisphenol A (BPA) together tested the selectivity of MIP particles, when the two compounds competed for available binding cavities or sites. Addition of E2 after BPA confirmed E2 occupation of the specific binding cavities, via displacement of BPA.
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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 | 0.001 |
| 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) | 0.001 | 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".