Effects of ground loop currents on signal intensities in electrospray mass spectrometry
Bibliographic record
Abstract
The occurrence of electrochemical processes during the operation of an electrospray ionization (ESI) source is well established. In the positive ion mode, electrons are drawn from the ESI metal capillary to a high voltage power supply. These electrons are the product of charge-balancing oxidation reactions taking place at the liquid/metal interface of the ion source. In a recent study, (Anal. Chem.2001, 73, 4836-4844), our group has shown that the introduction of a ground loop can dramatically enhance the rate of these oxidation processes. Such a ground loop can be introduced by connecting the sample infusion syringe (or the liquid chromatography column, in the case of LC-MS studies) to ground. The magnitude of the ground loop current can be controlled by the electrolyte concentration in the analyte solution, and by the dimensions of the capillary connecting the syringe needle and the ESI source. Using ferrocene as a model system, it is demonstrated that the introduction of such a ground loop can significantly enhance the signal intensity of analytes that form electrochemically ionized species during ESI. However, analytes that form protonated molecular ions, such as reserpine, also show higher signal intensities when a ground loop is introduced into the system. This latter observation is attributed to the occurrence of electrolytic solvent (acetonitrile and/or water) oxidation processes. These reactions generate protons within the ion source, and thus facilitate the formation of [M + nH](n+) ions. Overall, this work provides an example of how the careful control of electrochemical parameters can be exploited to optimize signal intensities in ESI-MS.
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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 | 0.006 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".