Molecularly imprinted β‐cyclodextrin/Kaoline particles for the selective recognition and binding of bisphenol A
Bibliographic record
Abstract
Abstract Molecularly imprinted β‐cyclodextrin/Kaoline particles (MIPs) were prepared for recognitive adsorption of bisphenol A (BPA) from aqueous solution. The characterisation of MIPs were achieved by FTIR spectra, SEM micrographs, nitrogen adsorption–desorption analysis and elemental analysis. The equilibrium data, at various temperatures, were described by the Langmuir, Freundlich and Dubinin‐Radushkevich isotherm models. The kinetic properties were successfully investigated by pseudo‐first‐order model, pseudo‐second‐order model, intraparticle diffusion equation, initial adsorption rate and half‐adsorption time. Based on the comparison of diffusion parameters, including intraparticle diffusion, film diffusion and pore diffusion, we can confirm the process of recognitive binging sites in MIPs. A diffusion‐controlled process as the essential adsorption rate‐controlling step was proposed. Moreover, intraparticle diffusion increased with BPA concentration while film and pore diffusion decreased. An increase in the temperature was found to increase intraparticle and pore diffusion, and reduce film diffusion. The selectivity of MIPs also demonstrated higher affinity for target BPA over competitive phenolic compounds than that of non‐imprinted polymers (NIPs).
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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 | 0.000 |
| 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".