Two-Dimensional FAIMS-TIMS Separation for Probing Structural Diversity of Peptides with Resolution Exceeding 6500
Bibliographic record
Abstract
A two-dimensional gas-phase ion separation platform was developed by coupling high-sensitivity and high-resolution racetrack field asymmetric waveform ion mobility spectrometry (r-FAIMS) with trapped ion mobility spectrometry (TIMS) and time-of-flight mass spectrometry. This platform was applied to explore the conformational diversity of doubly charged bradykinin (BK+2H + ) 2+ ions. Four conformers of the (BK+2H + ) 2+ ion can be resolved by using fully optimized TIMS, while six conformers of the (BK+2H + ) 2+ ion with different compensation voltages (CV) can be resolved by using r-FAIMS with pure nitrogen as a carrier gas and a 2.6 kV dispersion voltage (DV). By further TIMS analyses of the CV-selected (BK+2H + ) 2+ ions corresponding to all the six FAIMS separated peaks, nearly 30 conformers of the (BK+2H + ) 2+ ion can be resolved. Multiple conformers of (BK+2H + ) 2+ ions were found to have completely different CV values, although their mobilities were essentially the same. Multiple TIMS peaks, which are basically buried in the ambient noise in the normal TIMS spectrum, can be easily detected in the CV-selected TIMS spectra. These experimental measurements have positively proven that the separations in the FAIMS and TIMS dimensions are highly orthogonal. The overall resolving power of two-dimensional r-FAIMS-TIMS separation is essentially proportional to the product of the resolving powers of r-FAIMS and TIMS. The theoretical calculation, based on the data correlation between FAIMS and TIMS measurements, has shown that the total resolving power of the combined r-FAIMS and TIMS separation exceeds 6500 for the (BK+2H + ) 2+ ions, making the new instrument platform very effective for the structural elucidation of large biomolecules.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".