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Record W2314249053 · doi:10.1139/cjc-2012-0323

The role of the Lewis acid−base properties in the supramolecular association of 1,2,5-chalcogenadiazoles

2013· article· en· W2314249053 on OpenAlexafffundvenue
Anthony F. Cozzolino, Philip J. W. Elder, Lucia Myongwon Lee, Ignacio Vargas‐Baca

Bibliographic record

VenueCanadian Journal of Chemistry · 2013
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsChemistryChalcogenSupramolecular chemistryNatural bond orbitalLewis acids and basesCovalent bondChemical physicsCrystallographyMoleculeNon-covalent interactionsElectron localization functionComputational chemistryElectronDensity functional theoryHydrogen bondCrystal structureOrganic chemistry

Abstract

fetched live from OpenAlex

The secondary bonding interactions that link the supramolecular structures assembled by 1,2,5-chalcogenadiazoles were analyzed through explicit orthogonalization of molecular orbitals (NBO), topological analysis of the electron density (AIM), and the electron localization function (ELF). The results of these analyses are consistent with a bonding description that attributes important covalent and electrostatic character to these interactions. Application of these analyses to the individual molecules highlighted the structural features from which each of those contributions originates, namely the polarity and modest strength of the E–N bond. Both of these effects increase along the series S, Se, Te. Perturbations to the heterocycle electronic structure that result in a weaker and more polar E–N bond cause an increase in the Lewis acidity at the chalcogen centre, which in turn leads to stronger secondary bonding interactions with Lewis bases. Additionally, the contribution of dispersion forces is not negligible and is most important in the case of sulfur.

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 score0.017
Threshold uncertainty score0.466

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 science0.0010.000
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 head0.005
GPT teacher head0.168
Teacher spread0.163 · 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

Citations37
Published2013
Admission routes3
Has abstractyes

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