New surfaces for desorption electrospray ionization mass spectrometry: porous silicon and ultra‐thin layer chromatography plates
Bibliographic record
Abstract
The performance of nanoporous silicon (pSi) and ultra-thin layer chromatography (UTLC) plates as surfaces for desorption electrospray ionization (DESI) was compared with that of polymethyl methacrylate (PMMA) and polytetrafluoroethylene (PTFE), both popular surfaces in previous DESI studies. The limits of detection (LODs) and other analytical characteristics for six different test compounds were determined using all four surfaces. The LODs for the compounds were in the fmol-pmol (pg-ng) range. The LODs with the pSi surface were further improved for each of the compounds when heat was applied to the surface during sample application which gave LODs as low as or lower than those achieved with PMMA and PTFE. The UTLC plates were successfully used as a rapid means of chromatographic separation prior to DESI-MS analysis. Another advantage achieved using the newer pSi and UTLC surfaces was increased speed of analysis, associated with drying of solution-phase samples. This took place immediately at the UTLC surface and it could be achieved rapidly by gently heating the pSi surface. The presence of salts in the sample did not cause suppression of the analyte signal with any of the surfaces.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".