Ionic Liquids as Matrices in Microfluidic Sample Deposition for High-Mass Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry
Bibliographic record
Abstract
Sample preparation for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) via a microfluidic deposition device using ionic liquid matrices addresses several problems of standard protocols with crystalline matrices, such as the heterogeneity of sample spots due to the co-crystallization of sample and matrix and the limited capability for high-throughput analysis. Since ionic liquid matrices do not solidify during the measurement, the resulting sample spots are homogeneous. The use of these matrices is also beneficial for automated sample preparation, since crystallization of the matrix is avoided and, thus, no clogging of the spotting device can occur. The applicability of ionic liquids to the analysis of biomolecules with high molecular weights, up to ≈ 1 MDa is shown, as well as a good sensitivity (5 fmol) for recombinant human fibronectin, a protein with a molecular weight of 226 kDa. Microfluidic sample deposition of proteins with high molecular weights will, in the future, allow parallel sample preparation for MALDI-MS and for electron microscopy.
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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) | 0.000 | 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) | 0.001 | 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".