Optimization and characterization of a new microextraction device for determination of phenols in water samples
Bibliographic record
Abstract
Porous water-compatible molecularly imprinted polymer coatings with selective binding sites for extraction of phenols from environmental water samples were prepared on glass using an optimized mixture of water-soluble carboxylic acid functional monomers, ethylene glycol dimethacrylate crosslinker, catechol as a pseudo-template, and a porogen system of methanol/water with linear polymer polyethylene glycol. The MIP devices were combined with ultra high-performance liquid chromatography with a photodiode array detector suitable for the simultaneous determination of trace levels of phenol, alkylphenols and chlorophenols in seawater (SW) and produced water (PW). For effective imprinting, the MIP formulation was optimized through systematic optimization of critical factors like the nature and the amounts of functional monomer, crosslinker, template, and porogen. To improve the analytical method, the parameters that influence extraction, including salinity, pH, adsorbent mass, desorption solvent, and desorption time were optimized. Under the optimized conditions, the detection limits ranged from 0.1 to 2 μg L-1, and enrichment factor between 12.8 and 133.5. The recoveries from spiked samples ranged from 85 to 100% with %RSDs of 0.2–14% for SW and 81–107% with %RSD of 0.1–11% for PW. The MIP device is simple, robust, inexpensive can be used in automation and high throughput sample processing. To better understand the performance of MIPs, four different isotherm models were used to study molecular recognition of five phenols on catechol imprinted polymer and cross-reactivity for 11 phenolic compounds through individual and simultaneous adsorption process, respectively. It was found that heterogeneity is a relative phenomenon depending on the chemistry of the adsorbates. The Langmiur-Freundlich isotherm model successfully explains the adsorption behaviour for small phenols and fails to explain the molecular recognition for the large phenols, while the BET isotherm successful in that and suggests formation of multilayer. It was observed that the competition of phenols for the binding sites of the catechol imprinted polymer depends on their hydrophobicity and solubility in water. In this work, we proved that a single isotherm model is not enough to explain the behaviour of the analytes toward adsorbent surface. Each model gives valuable quantitative data that help to explain the recognition mechanism for the adsorbates.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".