Matrix-Compatible Solid-Phase Microextraction Pin Coupled Directly to Mass Spectrometry using Probe Electrospray Ionization
Bibliographic record
Abstract
A solid-phase microextraction (SPME) pin device with a biocompatible coating on the tip was developed for direct coupling to mass spectrometry (MS) via a vertical dipping-and-spray strategy using an automated probe electrospray ionization (PESI) interface. The developed method provides superior sensitivity compared to standard PESI-MS due to the enrichment effects of SPME and the significant increase in the volume of sample and/or solvent collected during dipping due to the SPME pin’s notably larger size. The tips of the SPME pins were coated with a biocompatible coating consisting of small sorbent particles embedded into a polyacrylonitrile (PAN) binder. This coating enables the extraction of small molecules, while preventing larger molecules such as tissue fragments, proteins, and cell matter from coming into the sorbent. The developed SPME pin-PESI-MS method also features much lower matrix effects compared to PESI-MS for the analysis of complex biology samples. When applied for the analysis of 8 drugs of abuse in urine samples, the SPME pin-PESI-MS method provided good linearity ( R 2 ≥ 0.9997), high sensitivity with limits of detection between 0.003 to 0.03 ng/mL, and good reproducibility with RSD% ≤ 6%. The vertical design of the SPME-PESI-MS direct-coupling interface allows the potential fully automation of the system using a conventional autosampler.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".