Ion sampling effects under conditions of total solvent consumption
Bibliographic record
Abstract
The motivation of this work was to study some of the properties of nanoelectrospray operation under conditions where the entire sprayed liquid is vaporized and inhaled into the vacuum system. Under these conditions the desolvation requirements, sampling efficiency, concentration versus mass sensitivity, and molar response characteristics of various compounds were studied. The combined efficiency of ion production from solution and transfer into the vacuum system, referred to as sampling efficiency, is presented under various inlet conditions including different flow rates, solution compositions, and compound types. Under ideal solvent conditions the results for favorable compounds show sampling efficiencies of 70-85% at flows in the range of 50-500 nL/min. Efficiencies were lower for aqueous samples and compounds of different structures gave different molar response factors under these high sampling efficiency conditions. The relative molar response factors are presented in terms of those observed with higher flow rate sources which operate at significantly lower sampling efficiencies. In all cases, operating in this flow regime, the ion count rate was directly proportional to the absolute mass of analyte molecules entering the source. The experimental source used to carry out these studies included gas nebulization to stabilize the electrospray process, a heated laminar flow chamber to enhance desolvation and ion production, and various atmosphere-to-vacuum aperture diameters to maximize ion transfer.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".