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Record W2596355018

Computational studies of combustion processes and oxygenated species

2007· article· en· W2596355018 on OpenAlexfundno aff
Carrigan J. Hayes

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2007
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersMcMaster UniversityOhio State UniversityNational Science Foundation
KeywordsCombustionEnvironmental scienceChemistry
DOInot available

Abstract

fetched live from OpenAlex

Within this dissertation, we report on explorations of reactive oxygen species with implications for combustion and atmospheric chemistry.Various computational approaches, including density functional theory (DFT) and master equation methods, were used to complete these projects.The majority of this thesis involves the oxidation pathways of the alkylated heterocycles that provide a model framework for understanding coal combustion.The enthalpies and energies of reaction for hydrogen-atom loss and alkyl-group fragmentations at various temperatures were calculated via density functional theory (B3LYP/6-311+G**//B3LYP/6-31G*); these results were calibrated against CBS-QB3 calculations.It was suggested that both hydrogen-atom loss and alkyl-group loss reactions will contribute as initiation steps for the high-temperature combustion reactions of these rings.Longer alkyl chains will increase reactivity, and the azabenzene units are more likely to react than the five-membered heterocyclic rings.The initial steps of radical formation are expected to become more favorable at high temperatures.The oxidation steps of these radicals were shown to be exothermic and exoergic, as expected.DFT studies (B3LYP/6-311+G**//B3LYP/6-31G*) showed that these resultant peroxy radicals were more likely to undergo intramolecular reactions to form bicyclic structures.Furthermore, several pathways seemed feasible and must be and Karen Hayes; my siblings, Rob and Katie; and my grandparents, Tom and Jeanne Wright.Although they are too numerous to mention by name, I also thank many uncles, aunts, cousins, and other relatives whose kind support has been a continued source of inspiration throughout my education.In particular, I mention the relatives whom have passed away during my time in graduate school: my aunt, Judy Boling; my greatgrandfather, Fred Hines; my uncle, Gary Wright;

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.206
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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