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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".