Development of Molecularly Imprinted Polymer in Porous Film Format for Binding of Phenol and Alkylphenols from Water
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) were fabricated on glass slides with a "sandwich" technique giving ~20 µm thick films. Methanol/water as a solvent, and polyethyleneglycol and polyvinylacetate as solvent modifiers, were used to give a porous morphology, which was studied with scanning electron microscopy and gravimetric analysis. Various MIPs were synthesized through non-covalent imprinting with phenol as the template; itaconic acid, 4-vinylpyridine, and styrene as monomers; ethylene glycol dimethacrylate, triethylene glycol dimethacrylate, and pentaerythritol triacrylate (PETA) as cross-linkers. Binding and imprinting properties of the MIPs were evaluated based on phenol adsorption isotherms. Since phenol has only one weakly acidic hydroxyl group and lacks unique structural characteristics necessary for binding specificity, the preparation of selective MIPs was challenging. The recognition of phenol via hydrogen bonding is suppressed in water, while hydrophobic interactions, though promoted, are not specific enough for highly-selective phenol recognition. Nevertheless, the styrene-PETA MIP gave modest imprinting effects, which were higher at lower concentrations (Imprinting Factor (IF) = 1.16 at 0.5 mg·L(-1)). The isotherm was of a Freundlich type over 0.1-40 mg·L(-1) and there was broad cross-reactivity towards other structurally similar phenols. This shows that phenol MIPs or simple adsorbents can be developed based on styrene for hydrophobic binding, and PETA to form a tighter, hydrophilic network.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".