Experiment and theory combine to produce a practical negative ion calibration set for collision cross‐section determinations by travelling‐wave ion‐mobility mass spectrometry
Bibliographic record
Abstract
RATIONALE: There are relatively few cross-section measurements for negatively charged ions. Available calibrants provide sufficient cross-section coverage for the 390 Å(2) to 641 Å(2) and 1174 Å(2) to 3395 Å(2) ranges. This is not particularly well suited for determining the collision cross-sections of smaller ions, such as small peptides. METHODS: Molecular mechanics/molecular dynamics (MM/MD) simulations, coupled with simulated annealing, were used to find the low-energy molecular conformations of polystyrene (PS) oligomers of length 3-9 (singly deprotonated) and 5-13 (doubly deprotonated). The trajectory method in MOBCAL was employed to derive their respective collision cross-sections, Ω. A calibration plot relating corrected Ω values to drift times in a Waters Synapt G2 mass spectrometer was used to predict the Ω values for the -2 to -6 charge states of dT(10) DNA. RESULTS: The in silico design of a reliable negative ion calibration set for ion mobility spectrometry successfully resulted in the use of α,ω-carboxy-terminated PS oligomers to determine the collision cross-sections of negatively charged ions in the range 132-388 Å(2). All charge states of dT(10) DNA were predicted to within 3% of the referenced values for these ions. CONCLUSIONS: α,ω-Carboxy-terminated PS oligomers were found to be an excellent choice to calibrate ion mobility spectrometers to obtain cross-sections for moderately sized ions. Oligomers with fewer, or weaker, interactions among the internal side chains (like poly(ethylene glycol) oligomers) tend to have a wide range of low-energy molecular conformations resulting in large standard deviations in their theoretically predicted collision cross-sections.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".