The combination of an electrospray ion source and an electrostatic storage ring for lifetime and spectroscopy experiments on biomolecules
Bibliographic record
Abstract
An electrospray ion source has been coupled to an accelerator that injects ions into an electrostatic heavy-ion storage ring. Since the dc ion current produced by electrospray ionization is low (∼106 ions/s), ions are accumulated in a cylindrical ion trap filled with a helium buffer gas. The ions are collisionally damped in the buffer gas and confined to the central trap region by a rf field. Extraction from the trap occurs within a few microseconds and after acceleration through 22 kV, the ions of interest are selected by a magnet according to their mass to charge ratio. The ion bunch is subsequently injected into the ring. Both positive and negative ions have been stored, with masses ranging over 3 orders of magnitude (∼102–104 Da). From a pickup signal in the ring, the number of ions in a bunch is estimated to be of the order of 103–104 when the accumulation time is 0.1 s. Our first measurements show that we can store a sufficient number of ions to study the decay of metastable ions and to determine relative destruction cross sections. The technique could be useful to probe conformers differing only in size. Furthermore, our setup can be used for spectroscopic measurements of the ion-photon interaction such as the excitation of [Cytochrome c+17H]17+ protein ions with 532 nm photons.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".