Chemical-Vapor-Assisted Electrospray Ionization for Increasing Analyte Signals in Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
We report a chemical-vapor-assisted electrospray ionization (ESI) technique to improve the detection sensitivity of ESI mass spectrometry (MS). This simple technique involves introducing a chemical vapor into the sheath gas around the nano-ESI spray tip or through a tubing with its outlet placed close to the spray tip. A variety of chemical vapors were tested and found to have varying degrees of effects on analyte signal intensities. The use of benzyl alcohol vapors in ESI was found to increase signal intensities of standard peptides by up to 4-fold. When this technique was combined with capillary liquid chromatography tandem MS (LC-MS/MS), the number of unique peptides identified in the acid hydrolysate of alpha casein increased by 45% and the number of peptides and proteins identified in a tryptic digest of E. coli cell lysate increased by 13% and 14%, respectively, along with an increased average match score. This technique could also increase the analyte signals for some small molecules, such as phenylephrine, by up to 3-fold. The increased analyte signals observed in the chemical-vapor-assisted ESI process is related to the enhancement of the ionization efficiency in ESI. The method can be readily implemented to an existing ESI mass spectrometer at minimum cost for improving detection sensitivity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".