A broader view on ion heating in traveling-wave devices using fragmentation of CsI clusters and extent of H˙ migration as molecular thermometers
Bibliographic record
Abstract
Electron transfer dissociation (ETD) is becoming increasingly important in mass spectrometry-based analysis of peptides and proteins. Supplemental collisional activation of undissociated electron transfer products can significantly increase fragmentation yield and sequence coverage, but hydrogen rearrangements - specifically, transfer of a hydrogen radical from a c to a z fragment - lead to distorted isotope distributions and increased potential for signal overlap. Concomitant collisional activation during the ion/ion reaction significantly reduces these rearrangements, but, in ion traps, also leads to lower reaction rates and reduced overlap of anion and cation clouds. In traveling-wave ion mobility devices, it has been reported - although not under ETD conditions - that significant ion activation can occur depending on the T-wave height and velocity. Here, we investigate this phenomenon in more detail using a commercial instrument (Waters Synapt G2) and report that a similar effect can be induced within the traveling-wave Trap cell where the ETD reaction occurs, using fairly typical T-wave settings. This ion 'heating' is demonstrated by analyzing the observed isotope distributions (sensitive to the aforementioned hydrogen rearrangements) of ETD fragments of ubiquitin and substance P. A more detailed investigation of ion activation using cesium iodide clusters (without ETD reagent anions present) shows that the observed behavior is consistent with the known dynamics of ions within traveling-wave devices. The insights gained in this work are potentially relevant both for 'native ETD' studies (in which tuning needs to be optimized to avoid unintentional ion activation) as well as the design of future T-wave ETD devices (where this 'heating' effect might be exploited to promote fragment release).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".