Electrospray ionization stability and concentration sensitivity in capillary electrophoresis‐mass spectrometry using a flow‐through microvial interface
Bibliographic record
Abstract
Concentration sensitivity is a key performance indicator for analytical techniques including for capillary electrophoresis-mass spectrometry (CE-MS) with electrospray ionization (ESI). In this study, a flow-through microvial interface was used to couple CE with MS and improve the ESI stability and detection sensitivity. By infusing a peptide mixture through the interface into an MS detector at a typical flow rate for CE-MS analysis, the spatial region near the interface was mapped for MS signal intensity. When the sprayer tip was within a 6 × 6.5 × 5 mm region in front of the MS inlet, the ESI was stable with no significant loss of signal intensity for ions with m/z 239. Finite element simulations showed that the average electric field strength at the emitter tip did not change significantly with minor changes in emitter tip location. Experiments were conducted with four different mass spectrometer platforms coupled to CE via the flow-through microvial interface. Key performance indicators, that is, limit of detection (LOD) and linearity of calibration curves were measured for nine amino acids and five peptides. Inter- and intraday reproducibility were also tested. The results were shown to be suitable for quantification when internal standards were used.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".