Surface roughening of a non‐tapered open tubular emitter for improved electrospray ionization mass spectrometry performance at low flow rates
Bibliographic record
Abstract
A non-tapered open tubular emitter with 75 microm internal diameter (i.d.) and 360 microm external diameter (o.d.) was developed by simply grinding the exit aperture of a fused-silica capillary. The roughened emitter, with a relatively large aperture, generates stable electrospray signals (generally <5% relative standard deviation (RSD) for most conditions studied) at less than 500 nL/min flow rates, and was characterized with atomic force microscopy. The surface treatment greatly extends the operational range of an open tubular emitter to lower flow rates, compared to that of a cleaved capillary with similar dimensions. The stabilized nanoelectrospray is attributed to the increased surface roughness and modified wetting characteristics of the emitter exit resulting from grinding. Electrospray performance was evaluated, and as a result of the enhanced sensitivity from a roughened emitter, five femtomoles of leucine enkephalin were detected at a 50 nL/min flow rate with a signal to noise (S/N) ratio of 48. Furthermore, trypsin-digested bovine serum albumin (BSA) was used to demonstrate the application of the emitter in protein identification, giving a sequence coverage of 60%. These emitters are robust, and may become a facile alternative to tapered emitters at moderate nano flow rates (e.g. 50 to 500 nL/min).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".