Coupling of Solid-Phase Microextraction Directly to Mass Spectrometry via an Improved Microfluidic Open Interface to Facilitate High-Throughput Determinations
Bibliographic record
Abstract
Mass spectrometry analysis can be performed by introducing samples directly to mass spectrometry, allowing the increase of the analysis throughput; however, some disadvantages of direct-to-mass spectrometry analysis include susceptibility to matrix effects and risk of instrument contamination from inadequate sample preparation. Solid-phase microextraction is one of the most suitable sample preparation methods for direct-to-mass spectrometry analysis, as it offers matrix-compatible coatings which ensure analyte enrichment with minimal or no interference from matrix. One of the ways solid-phase microextraction can be coupled directly to mass spectrometry is via a microfluidic open interface. This manuscript reports improvements made to the initial microfluidic open interface design, where the system components have been simplified to mostly commercially available materials. In addition, the analysis of samples has been automated by implementing software that fully controls the analysis workflow, where the washing procedure is optimized to completely reduce the carryover. Herein, the extraction and desorption time profiles from thin and thick SPME devices was studied where the overall workflow consisted of high-throughput sample preparation of 1.3 min per 96 samples and <1 min per sample instrumental analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".