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Record W2059018914 · doi:10.1002/jms.1540

The chemistry of ionized Acetone/Ar mixtures under varying gas flow and mole ratio conditions: A matrix‐isolation study

2009· article· en· W2059018914 on OpenAlexaff
J. Mark Parnis, Kaitlynn A. King, Matthew G. K. Thompson

Bibliographic record

VenueJournal of Mass Spectrometry · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsTrent University
FundersNational Science Council
KeywordsChemistryAcetoneAnalytical Chemistry (journal)Electron ionizationAcetyleneChemical ionizationIonizationOrganic chemistryIon

Abstract

fetched live from OpenAlex

The chemistry of ionized acetone:Ar mixtures under varying total gas flow rate and acetone:Ar mole ratio conditions has been studied using matrix-isolation techniques. Gaseous acetone diluted in excess argon gas was subjected to electron bombardment with 300-eV electrons. The products of subsequent reaction processes were matrix isolated and analyzed by Fourier transform infrared (FTIR) absorption spectroscopy. Products included 1-propen-2-ol (the enol isomer of acetone), methane, ketene, carbon monoxide, ethane, ethene, acetylene and tricarbon monoxide. Variations in the total flow rate of gas resulted in changes in the efficiency of product formation without significant changes in the relative amounts of the major species formed. Variations in the acetone:Ar mole ratio at fixed total gas flow resulted in striking variations in the products formed, demonstrating a shift from single acetone molecule-derived charge-transfer ionization chemistry at low acetone mole ratios, to processes consistent with the participation of two or more acetone molecules at intermediate mole ratios. These results are interpreted in the context of ion-molecule reaction processes, the onset of which occurs at intermediate acetone mole ratios. Ethane dehydrogenation products are proposed to result from product secondary ionization, a process that is prevalent at high ionizing electron fluxes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.384

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 communication0.0000.000
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 head0.007
GPT teacher head0.272
Teacher spread0.265 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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