Embedding Mixed Sorbents in Binder: Solid-Phase Microextraction Coating with Wide Extraction Coverage and Its Application in Environmental Water Analysis
Bibliographic record
Abstract
Solid-phase microextraction (SPME) is a simple and highly effective sample-preparation technique for water analysis. However, the extraction coverage of a given SPME device with a specific coating can be an issue when analyzing multiple environmental contaminants. Therefore, instead of synthesizing one sorbent material with dual or multiple functions, we investigated a new strategy of preparing SPME blades using a homogeneous slurry made by mixing three different sorbent particles─namely, hydrophobic/lipophilic balanced (HLB), HLB-weak cationic exchange (HLB-WCX), and HLB-weak anionic exchange (HLB-WAX)─with a polyacrylonitrile (PAN) binder. The developed coating is matrix compatible, as the binder functions not only as a glue for immobilizing the sorbent particles but also as a porous filter, which only allows small molecules to enter the pores and interact with the particles, thus avoiding contamination from large elements. The results confirmed that the proposed mixed-coating SPME device provides good extraction performance for polar and nonpolar as well as positively and negatively charged compounds. Based on this device, three comprehensive analytical methodologies─high-throughput SPME-LC-MS/MS (for the quantitative analysis of targeted drugs of abuse and artificial sweeteners), in-bottle SPME-LC-high resolution MS (HRMS) (for the untargeted screening of organic contaminants), and on-site drone sampling SPME-LC-HRMS (for on-site sampling and untargeted screening)─were developed for use in environmental water analysis. The resultant data confirm that the proposed strategies enable comprehensive water quality assessment by using a single SPME device.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".