Segmented-Field-Gradient-Focusing Ion Guide for Simultaneously Improved Ion Transfer across a Wide Mass Range and Enhanced Sensitivity
Bibliographic record
Abstract
The sensitivity of the atmospheric pressure ionization mass spectrometer (API-MS) has been improved owing to the advancement in the atmospheric pressure interface. However, ion scattering losses in the first vacuum chamber undermine this improvement. In this work, a novel multipole ion guide, called a segmented-field-gradient-focusing ion guide (SFGF-IG), was developed to address these losses, especially caused by the asymmetric-shaped supersonic jet generated by a slot-shaped inlet. The SFGF-IG combined a dodecapole (65 mm in length) and a quadrupole (125 mm in length), with each segment having its tilt angle optimized. By analyzing three different ion trajectory conditions, "Transmitted", "Reflected", and "Absorbed″, the key factors affecting the ion transfer in the SFGF-IG were investigated. Here, this analytical method for developing a high-performance ion guide was expanded to account for the influence of background gas. This revised method provides a framework for understanding the dominant mechanism that influences ion transfer within high-performance ion guides under complex background gas flow. Results showed that the SFGF-IG reduced ion scattering losses in the first vacuum chamber, which allowed the greater gas-throughput slot-shaped inlet to increase the API-MS sensitivity. Compared with the conventional ion funnel, the SFGF-IG demonstrated a significantly reduced low-mass discrimination effect and improved ion transfer across a wide mass range (100-2000 m/z), enabling simultaneous analysis of high-, medium-, and low-mass ions. Moreover, to verify the actual performance of the SFGF-IG, it was integrated into a home-built quadrupole-linear ion trap tandem MS (Q-LITMS) equipped with a slot-shaped inlet. The instrument detection limit (IDL) of the reserpine reached 0.15 fg (RSD 6.08%), two-fold lower than the conventional Q-LITMS configuration (0.3 fg and RSD 11.1%). This work provides a complete development approach for a high-performance ion guide to reduce ion scattering losses and further enhances the sensitivity of laboratory-scale 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".