Electrospray ionization suppression, a physical or a chemical phenomenon?
Bibliographic record
Abstract
Mass spectrometry is a powerful qualitative and quantitative analytical technique that has been introduced in many bioanalytical and research laboratories in the last 10 years. The combination of HPLC with tandem MS yields a particularly powerful tool and it is now the method of choice for the analysis drugs, metabolites, biomarkers and proteins. However, HPLC-MS methods are not completely without problems that can compromise the quality of the results. An important phenomenon that can affect the quantitative performance of a mass detector is ion suppression. In this study, we measured the influence of the observed current (I) vs signal intensity and the variation of the observed current (I) when analyzing biological samples. Our experiment suggests that, despite the fact that it is possible for other chemicals to compete for protons in the droplets, the increase in the observed current (I) during the signal suppression is important and indicates that the conductivity of the liquid increases significantly. The salts and the charged species influence the conductivity and the surface tension of the droplets and modify the equilibrium between the two main forces involved during the electrospray process, resulting in an erratic spray behavior.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".