Optimizing Ion Optical Design for Laser Ablation Source in Mass Spectrometry
Bibliographic record
Abstract
Abstract The study focuses on optimizing a Laser Ablation Source (LAS) for use in mass spectrometry, particularly aiming to enhance transmission of ions and and determination of isotopic compositions in diverse matrices at TRIUMF’s Ion Trap for Atomic and Nuclear Science (TITAN). Critical variables affecting mass spectrometer resolution, such as ion kinetic energy distribution and ion transport are optimized through LAS. The paper explores the importance of consistent initial conditions, ion transport efficiency, and ion capture for accurate and reproducible measurements. Using SIMION software, we optimized ion optical design to tackle challenges like collimating ions travelling off-axis and mass filtering. A significant part of the study is dedicated to ion optic design to enable low abundance measurement of ions of interest (e.g., 10 ions of interest out of 1014 total ions). Techniques like high voltage switching and beam shaping were explored, and the final design aims for high transport efficiency and a 4 mm effective target scan area with maximum transport efficiency. The integration of LAS with a Multiple-Reflection Time-Of-Flight Mass Spectrometer (MR-TOF-MS) is expected to yield an analytical tool with high spatial resolution and transport efficiency for isotope abundance measurement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".