Determination of Flow Rates in Capillary Liquid Chromatography Coupled to a Nanoelectrospray Source using Droplet Image Analysis Software
Bibliographic record
Abstract
Liquid chromatography coupled to electrospray tandem mass spectrometry (LC-ESI-MS/MS) is widely used in proteomic and metabolomic workflows. Considerable analytical improvements have been observed when the components of LC systems are scaled down. Currently, nano-ESI is typically done at capillary LC flow rates ranging from 200 to 300 nL/min. At these flow rates, trouble shooting and leak detection of LC systems has become increasingly challenging. In this paper we present a novel proof-of-concept approach to measure flow rates at the tip of electrospray emitters when the ionization voltage is turned off. This was achieved by estimating the changes in the droplet volume over time using digital image analysis. The results are comparable with the traditional methods of measuring flow rates, with the potential advantages of being fully automatable and nondisruptive.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".