Miniature time-of-flight mass spectrometry using molecular Coulomb explosion detection
Bibliographic record
Abstract
Introduction Mass spectrometers are used to determine the masses of atoms, molecules, and clusters in a wide range of applications. Presently, there is a drive towards the miniaturization of such devices for use in spacecraft life support, pollution monitoring, and explosives/narcotics detection applications. For a given mass resolution, the ion flight distance (and hence the size) of a time-of-flight (TOF) mass spectrometer is related to the length of the ionization region along the flight axis. Since femtosecond pulses can ionize atoms and molecules within a very small focal volume with near unit efficiency, they are compatible with miniature mass spectrometers. We have demonstrated a general technique for compact TOF mass spectrometry using two spatially separated laser foci. The first femtosecond laser pulse ionizes a gaseous sample and the second pulse probes for the presence of a specific mass in the analyte. Our approach enables TOF mass analysis to be performed on a sub-millimetre length scale. Furthermore, the second pulse can be intense enough to explode the molecules it probes. Using such laser- induced Coulomb explosion for molecular detection yields a significant improvement in detection efficiency for large molecules. Taken together, these developments can reduce the size and complexity of miniature TOF mass spectrometers and allow the fabrication of integrated mass analyzers with relaxed voltage, vacuum, detector, and timing electronics requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".