<scp>GC</scp>/<scp>MS</scp>for Combustion and Pyrolysis Research
Bibliographic record
Abstract
Abstract The ability to determine combustion and thermal decomposition (pyrolysis) products at trace concentrations can lead to important information on catalytic pathways, the degradation process itself, and structural (fingerprint) information on the parent molecule(s), among others. Gas chromatography coupled with mass spectrometry (GC/MS) is well suited to separate and identify the volatile molecules and molecular fragments generated by combustion and pyrolysis processes. Whereas, pyrolysis (conducted in an inert atmosphere) is often used as a sample introduction device directly coupled to GC/MS, combustion is generally carried out offline and its products are collected and analyzed with GC/MS, or even pyrolysis‐GC/MS. In the past few years, this powerful technique has been used to examine a variety of sample types for quite diverse applications. This chapter describes the state of the art and the progress of GC/MS for the measurement of combustion and pyrolysis products. An introduction to the technique, including the principles of GC/MS, is first provided, followed by a demonstration of the instrument's capabilities for combustion and pyrolysis research. All reports discussed herein were selected in part to demonstrate the diversity of current combustion and pyrolysis research using GC/MS. The present authors' studies with temperature‐programmed desorption in conjunction with GC/MS to characterize oxygen shuttling on the chars derived from different gaseous environment, are highlighted. Finally, the use of GC/MS in combination with isotope‐labeling techniques to reveal complex reaction pathways and mechanisms is discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.242 | 0.111 |
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".