The combined use of thermal desorption and selected ion flow tube mass spectrometry for the quantification of xylene and toluene in air
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Bibliographic record
Abstract
Thermal desorption (TD) is commonly employed for volatile chemical analysis, it being the method of choice for occupational health and safety monitoring. TD allows for offline capture of volatiles onto a solid sorbent followed by desorption and analysis at a later time. Although TD is routinely used in conjunction with gas chromatography (TD-GC), the assay throughput is low and requires the use of gas standards for quantification. Another technique increasingly employed for volatile chemical analysis, selected ion flow tube mass spectrometry (SIFT-MS), is capable of real-time absolute (i.e. without calibration standards) quantification of volatile chemicals present at single digit parts per billion or higher concentrations. SIFT-MS is, however, normally used for online direct analysis of gas samples rather than offline collection and analysis. The goal of this study was to determine whether a combination of TD and SIFT-MS could be used to quantify volatile compounds, specifically xylene and toluene, more rapidly than TD-GC and without the need for calibration standards. SIFT-MS was able to quantify xylene and toluene levels within 45 s of desorption. Due to the robustness of the SIFT-MS analysis in the presence of water vapour and other major components of air, the purging of tubes usually required to remove these constituents during the TD cycle was not required, therefore reducing the TD cycle time. Comparing the quantity of xylene and toluene applied to the TD tube with the absolute levels quantified by SIFT-MS subsequent to desorption suggested a recovery of over 95% of the applied compound. We conclude that the combination of TD and SIFT-MS allows more rapid and accurate quantification of xylene and toluene (compared with TD-GC) to be achieved without the need for calibration standards, features which may be advantageous in applications requiring rapid analysis and high throughput.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it