An impulse-driven liquid-droplet deposition interface for combining LC with MALDI MS and MS/MS
Bibliographic record
Abstract
A simple and robust impulse-driven droplet deposition system was developed for off-line liquid chromatography matrix-assisted laser desorption ionization mass spectrometry (LC-MALDI MS). The system uses a solenoid operated with a pulsed voltage power supply to generate impulses that dislodge the hanging droplets from the LC outlet directly to a MALDI plate via a momentum transfer process. There is no contact between the LC outlet and the collection surface. The system is compatible with solvents of varying polarity and viscosity, and accommodates the use of hydrophobic and hydrophilic MALDI matrices. MALDI spots are produced on-line with the separation, and do not require further processing before MS analysis. It is shown that high quality MALDI spectra from 5 fmol of pyro-Glu-fibrinopeptide deposition after LC separation could be obtained using the device, indicating that there was no sample loss in the interface. To demonstrate the analytical performance of the system as a proteome analysis tool, a range of BSA digest concentrations covering about 3 orders of magnitude, from 5 fmol to 1 pmol, were analyzed by LC-MALDI quadrupole time-of-flight MS, yielding 6 and 57% amino acid sequence coverage, respectively. In addition, a complex protein mixture of an E. coli cell extract was tryptically digested and analyzed by LC-MALDI MS, resulting in the detection of a total of 409 unique peptides from 100 fractions of 15-s intervals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".