Dynamics and reactivity of thermalized ions in a radiofrequency quadrupole gas-reaction cell used for the production of radioactive molecular ions
Bibliographic record
Abstract
Abstract Two-dimensional radiofrequency quadrupole (RFQ) ion guides are versatile tools that are used over a wide range of ion energies. Properly tuned, they can provide an ideal environment for ion-molecule chemistry to proceed by allowing thermalized ions to react on-line with gaseous reactants. We exploit this capability in the Ion Reaction Cell (IRC), an RFQ-based system designed to form radioactive molecules (RM) from radioactive ion beams (RIB). Functionally, the IRC accepts RIB produced upstream by the isotope separation on-line (ISOL) method, decelerates it to eV energies for production of the RM and then re-accelerates RM to their incident energy. This system promises to form a highly versatile source of complex inorganic or organic RM that can be used in fundamental symmetry studies or as precursors to new radiopharmaceuticals. We discuss the challenges of transferring ions from a RIB into a gas cell at room temperature while constraining ions within the RFQ energy well, and of reforming a beam from synthesized RM. Various factors influencing ion dynamics and reactivity in thermal and non-thermal zones inside the IRC are considered, using results obtained recently with the Isobar Separator for Anions, an analogous RFQ technique used in Accelerator Mass Spectrometry, to illustrate key points.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".