Optimization of Fiber Coating Structure Enables Direct Immersion Solid Phase Microextraction and High-Throughput Determination of Complex Samples
Bibliographic record
Abstract
This study presents a new approach for improving the structure, and hence the robustness, of the SPME fiber coating applied for gas chromatography (GC) analysis. It involves application of an external layer of poly(dimethyl siloxane) (PDMS) over the commercial PDMS/divinyl benzene (DVB) extraction phase. The fiber provided extraction capabilities similar to that exhibited by the original PDMS/DVB fiber toward triazole pesticides from water samples. Furthermore, the fiber could be utilized for over 100 extractions in direct contact with a complex food matrix such as whole grape pulp, with no sample pretreatment required. The amount of extracted pesticides from whole grape pulp had RSD values below 20% throughout 130 extraction/desorption/conditioning cycles, which is a dramatic improvement when compared to commercial PDMS/DVB fiber coating applied in food analysis facilitating high-throughput automation.
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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 | 0.000 | 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 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".