Development and optimization of molecularly imprinted polymers for the analysis of organic pollutants in environmental water samples
Bibliographic record
Abstract
Agricultural, industrial, and municipal water releasing organic contaminants into the environment is of serious ongoing concern. To monitor these waterborne pollutants, sample preparation steps are required prior to analysis. Consequently, massive efforts have been directed toward new approaches that are fast, selective, cost-effective, user-friendly and green. Molecularly imprinted polymers (MIPs) are an elegant solution to add selectivity into sorptive materials. In this thesis, MIPs were prepared using different polymerization techniques and in various formats such as MIP particles, MIP thin film, and MIP-coated mesh. The prepared sorbents were successfully utilized for extraction of different classes of pollutants such as polycyclic aromatic hydrocarbons (PAHs) and organophosphorus pesticides (OPPs) from water samples. To improve the heterogeneity of MIPs, a controllable polymerization mechanism (reversible addition fragmentation chain transfer (RAFT) polymerization) was implemented for synthesis MIPs on Fe₃O₄@SiO₂ particles for extraction of PAHs. A tailormade MIP formulation was also created and optimized for selective extraction of OPPs from water. The sorbent formulae are versatile for use in different formats such as thin film and mesh. MIP extraction devices can be readily interfaced with various detection systems such as gas chromatography flame ionization detector (GC-FID), atmospheric pressure chemical ionization gas chromatography-tandem mass spectrometry (APCI-GCMS/ MS) and liquid chromatography -tandem mass spectrometry (LC-MS/MS) using liquid desorption, and thermal desorption. Additionally, these devices can increase the throughput, reliability, and simplicity of environmental analysis. For example, we developed a new solvent assisted thermal desorption head space (ST-HD) method, which we demonstrate to be excellent for the introduction of analytes enriched by MIP thin films, and it is amenable to direct and semi-automated method improving reproducibility and throughput. In this thesis, MIP fabrication and performance will be demonstrated and evaluated. The value MIP techniques in providing precious sensitivity, selectivity to the quality of analysis of organic pollutants in water is presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".