MétaCan
Menu
Back to cohort
Record W2331920916 · doi:10.1021/ef5000529

On Discerning Intermolecular and Intramolecular Vibrations in Experimental Acene Spectra

2014· article· en· W2331920916 on OpenAlexaff
Faustine Spillebout, Didier Bégué, Isabelle Baraille, John M. Shaw

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTetraceneAceneIntermolecular forceIntramolecular forceChemistryAnthraceneSpectral lineInfrared spectroscopyComputational chemistryPentaceneMoleculeMolecular vibrationChemical physicsPhysical chemistryPhotochemistryPhysicsStereochemistryQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

The far-infrared spectra of the aromatic hydrocarbons tetracene and pentacene have been calculated using a perturbational-variational method coupled with potential truncation. This shortening of the potential enabled accurate vibration calculations for these large molecules and their respective dimers. Thus, it was possible to identify all IR bands obtained experimentally in the far-IR range by Michaelian et al., as well as to differentiate bands resulting from intermolecular and intramolecular modes of vibration separately, and combined intermolecular + intramolecular vibration modes. Far-IR spectra for smaller acene family members, naphthalene and anthracene, were also computed, and trends in intermolecular vibrations, for the acene family as a whole, were identified. The results obtained illustrate the quality and the detail of the insights realized by interrogating experimental spectra using high-precision, unscaled quantum mechanics computational approaches, and provide a benchmark for future work targeting identification of dominant molecular motifs and intermolecular association phenomena arising in ill-defined hydrocarbons including asphaltenes based on IR and Raman spectral decomposition.

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 score0.065
Threshold uncertainty score0.689

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)0.0010.000

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 head0.004
GPT teacher head0.214
Teacher spread0.209 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicAnalytical Chemistry and ChromatographyFrench-language works237,207