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Record W2586711605 · doi:10.1021/acs.langmuir.6b04151

Kinetics, Chemistry, and Morphology of Syngas Photoinitiated Chemical Vapor Deposition

2017· article· en· W2586711605 on OpenAlexafffund
Donya Farhanian, Grégory De Crescenzo, Jason R. Tavares

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHydro-QuébecCanada Foundation for Innovation
KeywordsSyngasChemistryChemical vapor depositionX-ray photoelectron spectroscopyChemical engineeringKineticsThin filmSubstrate (aquarium)Chemical kineticsChemical structurePhotochemistryOrganic chemistryMaterials scienceNanotechnologyCatalysis

Abstract

fetched live from OpenAlex

Syngas is the product of gasification processes and is used for the production of petrochemicals. Little attention has been paid to its use in the production of oligomeric thin films under ambient conditions. Herein, the nature of the photoinitiated chemical vapor deposition of films made from syngas using high-wavelength ultraviolet light is discussed, including an exploration of the oligomeric films' structure, synthesis mechanism, and growth kinetics. Specifically, X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry analyses provide insight into the chemical structure, illustrating the effect of photogenerated radicals in the formation of aliphatic, anhydride, and cyclic structures. The films are covalently bonded to the substrate and chemically uniform. Electron and atomic force microscopy identify an islandlike morphology for the deposit. These insights into the mechanism and structure are linked to processing parameters through a study on the effect of residence time and treatment duration on the deposition rate, as determined through profilometry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.363

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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 designBench or experimental
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

Citations16
Published2017
Admission routes2
Has abstractyes

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