Using Gas Modifiers to Significantly Improve Sensitivity and Selectivity in a Cylindrical FAIMS Device
Bibliographic record
Abstract
Recent reports describing enhanced performance when using gas additives in a DMS device (planar electrodes) have indicated that comparable benefits are not attainable using FAIMS (cylindrical electrodes), owing to the non-homogeneous electric fields within the analyzer region. In this study, a FAIMS system (having cylindrical electrodes) was modified to allow for controlled delivery of gas additives. An experiment was carried out that illustrates the important distinction between gas modifiers present as unregulated contaminants and modifiers added in a controlled manner. The effect of contamination was simulated by adjusting the ESI needle position to promote incomplete desolvation, thereby permitting ESI solvent vapor into the FAIMS analyzer region, causing signal instability and irreproducible CV values. However, by actively controlling the delivery of the gas modifier, reproducible CV spectra were obtained. The effects of adding different gas modifiers were examined using 15 positive ions having mass-to-charge (m/z) values between 90 and 734. Significant improvements in peak capacity and increases in ion transmission were readily attained by adding acetonitrile vapor, even at trace levels (≤0.1%). Increases in signal intensity were greatest for the low m/z ions; for the six lowest molecular weight species, signal intensities increased by ∼10- to over 100-fold compared with using nitrogen without gas additives, resulting in equivalent or better signal intensities compared with ESI without FAIMS. These results confirm that analytical benefits derived from the addition of gas modifiers reported with a uniform electric field (DMS) also are observed using a non-homogenous electric field (FAIMS) in the analyser region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".