Sorbent Coated Glass Wool Fabric as a Thin Film Microextraction Device
Bibliographic record
Abstract
A new approach for thin film microextraction (TFME) with mixed-phase sorptive coating is presented. Carboxen/polydimethylsiloxane (CAR/PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB) TFME samplers were prepared using spin coating and glass wool fabric mesh as substrate. The samplers were easily tailored in size and shape by cutting tools. Good durability and flat-shape stability were observed during extraction, stirring in water, and thermal desorption. The latter characteristic obviates the need for an extra framed holder for rapid TFME and makes the samplers more robust and easier to deploy. The samplers combine the advantages of adsorptive solid-phase microextraction (SPME) and TFME, including one-step solvent-free extraction and preconcentration, direct thermal desorption, and enhanced sensitivity without sacrificing analysis time due to thin film geometry. The analytical performance of these new devices was demonstrated using water samples spiked with N-nitrosamines (NAs) as model compounds. Over an order of magnitude enhancement of extraction efficiencies was obtained for the model compounds compared with the SPME fibers of similar coatings and PDMS thin film membrane. The results of this study indicate that these novel thin film devices are promising for rapid and efficient microextraction of polar analytes in water.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".