On an Aerodynamic Mechanism to Enhance Ion Transmission and Sensitivity of FAIMS for Nano-Electrospray Ionization-Mass Spectrometry
Bibliographic record
Abstract
Simulations show that significant ion losses occur within the commercial electrospray ionization-field asymmetric waveform ion mobility spectrometer (ESI-FAIMS) interface owing to an angular desolvation gas flow and because of the impact of the FAIMS carrier gas onto the inner rf (radio frequency) electrode. The angular desolvation gas flow diverts ions away from the entrance plate orifice while the carrier gas annihilates ions onto the inner rf electrode. A novel ESI-FAIMS interface is described that optimizes FAIMS gas flows resulting in large improvements in transmission. Simulations with the bromochloroacetate anion showed an improvement of ~9-fold to give ~70% overall transmission). Comparable transmission improvements were attained experimentally for six peptides (2+) in the range of m/z 404.2 to 653.4 at a chromatographic flow rate of 300 nL/min. Selected ion chromatograms (SIC) from nano-LC-FAIMS-MS analyses showed 71% (HLVDEPQNLIK, m/z 653.4, 2+) to 95% (LVNELTEFAK, m/z 582.3, 2+) of ion signal compared with ion signal in the SIC from LC-MS analysis. IGSEVYHNLK (580.3, 2+) showed 24% more ion signal compared with LC-MS and is explained by enhanced desolvation in FAIMS. A 3-10 times lower limits of quantitation (LOQ) (<15% RSD) was achieved for chemical noise limited peaks with FAIMS. Peaks limited by ion statistics showed subtle improvement in RSD and yielded comparable LOQ to that attained with nano-LC-MS (without FAIMS). These improvements were obtained using a reduced FAIMS separation gap (from 2.5 to 1.5 mm) that results in a shorter residence time (13.2 ms ± 3.9 ms) and enables the use of a helium free transport gas (100% nitrogen).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".