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Record W1975720371 · doi:10.1063/1.1605384

A spectroscopic and computer simulation study of butanol vapors

2003· article· en· W1975720371 on OpenAlexaff
George S. Fanourgakis, Yujun Shi, Styliani Consta, R. H. Lipson

Bibliographic record

VenueThe Journal of Chemical Physics · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryMass spectrumProtonationIonIonizationDissociation (chemistry)Mass spectrometryButanolPhysical chemistryCluster (spacecraft)Analytical Chemistry (journal)Ab initioComputational chemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

Clusters of butanol formed above neat liquid samples were entrained in a supersonic jet and probed using 10.5 eV vacuum ultraviolet laser single-photon ionization/time-of-flight mass spectrometry. The four different isomers of butanol (n-butanol, sec-butanol, iso-butanol, and tert-butanol) were studied separately to assess the influence of the structure of the alkyl chain on the formation and stability of the hydrogen bonded clusters. Most of the higher mass features observed in the mass spectra could be assigned to protonated alcohol clusters, H(ROH)n+, n⩽3; R=C4H9, that arise from facile proton-alkoxy radical/alkoxide anion dissociation. Signals due to protonated trimers were only evident in the spectra of tert- and sec-butanol. Empirical force fields, density functional theory and ab initio methods were used to identify the geometries of all clusters up to the pentamers for the different isomers. Monte Carlo simulations established vapor-phase cluster distributions, while molecular dynamics was used to assess the relative stability of the isomeric tetramers. Together, these experimental and theoretical results suggest that butanol tetramers are “magic-number” structures, and that the protonated ion signals of size n could be correlated with the neutral cluster of size n+1, provided the vapor pressures sampled in the supersonic jet exceeded equilibrium values.

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 score0.344
Threshold uncertainty score0.319

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 head0.012
GPT teacher head0.271
Teacher spread0.258 · 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

Citations34
Published2003
Admission routes1
Has abstractyes

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