High Dynamic Range of GC MS using Orthogonal Injection into Multi-Reflecting Time-of-Flight Mass Spectrometer
Bibliographic record
Abstract
Gas chromatography-mass spectrometry (GC-MS) is a cornerstone technique for analyses of semi-volatile compounds, constantly evolving to meet new challenges. The adoption of Time-of-flight mass spectrometers (TOF MS) has enhanced GC-MS with faster spectral acquisition and broader mass coverage, enabling rapid GC separations and even faster two-dimensional GCxGC analyses. Multi-reflecting TOF MS (MRT) further improves resolution compared to traditional TOF MS, facilitating the detection of minor analytes within complex matrices. This paper presents a significant improvement in MRT sensitivity and dynamic range achieved through a strong ion beam compression within the ion guide and the use of a conventionally oriented orthogonal accelerator. Sensitivity is improved to 100,000 ion/pg at a resolution of R=25,000, enabling the detection of low fg analyte quantities within 3 μg of matrix at 10 ng/s matrix fluxes. The demonstrated linear dynamic range exceeding 6 orders of magnitude potentially enables even deeper exploration of complex mixtures, which is crucial for ultra-trace analyses and metabolomic studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".