Solid‐phase microextraction combined with surface‐enhanced laser desorption/ionization introduction for ion mobility spectrometry and mass spectrometry using polypyrrole coatings
Bibliographic record
Abstract
The successful application of polypyrrole (PPY) solid-phase microextraction (SPME) coatings as both an extraction phase and a surface to enhance laser desorption/ionization (SELDI) of analytes is reported. This SPME/SELDI fiber integrates sample preparation and sample introduction on the tip of a coated optical fiber, as well as acting as the transmission medium for the UV laser light. Using ion mobility spectrometry (IMS) detection, the signal intensity was examined as a function of extraction surface area and concentration of analyte. The linear relationship between concentration and signal intensity shows potential applicability of this detection method for quantitative analysis. Extraction time profiles for the fiber, using tetraoctylammonium bromide as test analyte, illustrated that equilibrium can be reached in less than one minute. To investigate the performance of the PPY coating, the laser desorption profile was studied. The fiber was also tested using a quadrupole time-of-flight (Q-TOF) mass spectrometer with leucine enkephalin as test analyte. Since no matrix was used, mass spectra free from matrix background were obtained. This novel SPME/SELDI fiber is easy to manufacture, and is suitable for studying low-mass analytes because of the intrinsic low background. These findings suggest that other types of conductive polymers could also be used as an extraction phase and surface to enhance laser desorption/ionization in mass spectrometry.
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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) | 0.001 | 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".