Increasing the rate of sample vaporization in an open air desorption ionization source by using a heated metal screen as a sample holder
Bibliographic record
Abstract
Rapid vaporization of sample into the ionizing gas exiting a direct analysis in real time (DART®) source has been enabled by directing a high electrical current through a metal wire screen to which sample has been applied. This direct heating of the screen enables rapid vaporization of sample as the wire temperature rises from room temperature to greater than 400°C in less than 20 s. Positioning the screen between the DART source and atmospheric pressure inlet of the mass spectrometer ensures that the ionizing gas is in close proximity to the sample molecules, resulting in efficient ionization while significantly reducing the time required for mass spectrometric analysis. The capability to modulate the electrical current flow through the wires facilitates either rapid desorption for the determination of single component samples or slower desorption where analysis of mixtures might be desired. The technology also enables deployment of strategies for the determination of chemicals present as powders that might otherwise require dissolution prior to analysis. Results from the use of this thermally assisted DART ('TA-DART') system for the analysis of pure compounds, simple mixtures, solids and low vapor pressure samples are presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".