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Record W1497872594 · doi:10.1002/9783527628148.hoc022

<scp>GC</scp>/<scp>MS</scp>for Combustion and Pyrolysis Research

2010· other· en· W1497872594 on OpenAlexfundno aff
James V. Cizdziel, Wei‐Yin Chen

Bibliographic record

VenueHandbook of Combustion · 2010
Typeother
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersMcMaster University
KeywordsCombustionPyrolysisChemistryMass spectrometryInert gasDesorptionAnalytical Chemistry (journal)Environmental chemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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