Experimental Methods in Chemical Engineering: Mass Spectrometry—MS
Bibliographic record
Abstract
Abstract Mass spectrometry identifies the atomic mass of molecules and fragments in the gas phase. The spectrometer ionizes the molecules that then pass through an electric or magnetic field towards a detector. The field modifies the molecule's trajectory and we infer mass from its direction and velocity in a static field or from the stability of its path in a dynamic field. The electric current is amplified and a mass spectrum is generated from the location or timing of the signal from the detector, translated into a plot of the intensity as a function of the mass‐over‐charge ratio. It is field deployable, measures concentrations in real time with a temporal resolution better than 100 ms, and detection limits of fg. However, the signal drifts with time so we have to calibrate it as frequently as every hour. Calibrating requires multiple mixtures with varying concentrations to map the non‐linear response. The Web of Science Core Collection indexed over 60 000 articles that refer to MS (2016 and 2017) with applications ranging from permanent gas analysis, to identifying protein, forensic science, and natural products. The bibliometric software VOSViewer( ) identified four clusters of research related to MS: (1) proteomics, proteins, plasma, and metabolomics; (2) solid phase extraction together with gas chromatography; (3) tandem mass spectrometry and liquid chromatography; and (4) waste water and toxicity. We expect that the technique will continue to evolve with increased sensitivity, lower drift, and greater specificity. Miniaturization efforts should also continue in order to develop faster field deployable instruments.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.041 |
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".