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Record W2009751711 · doi:10.1139/v10-155

Computational study of proper and improper hydrogen bonding in methanol complexes

2011· article· en· W2009751711 on OpenAlexafffundvenue
C. Dale Keefe, Zuzana Is̆tvanková

Bibliographic record

VenueCanadian Journal of Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsCape Breton University
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research FundNova Scotia Research Innovation TrustResearch Nova ScotiaAtlantic Canada Opportunities AgencyCanada Research Chairs
KeywordsChemistryHydrogen bondMethanolMoleculeDimerHydrogenComputational chemistryAtoms in moleculesDensity functional theoryCovalent bondLow-barrier hydrogen bondOrganic chemistry

Abstract

fetched live from OpenAlex

The bulk properties of alcohols, like those of aqueous solutions, are governed mostly by hydrogen bonding; however, in contrast with water molecules, the chemical structure of a simple alcohol such as methanol offers an opportunity to explore the effects of both proper and improper hydrogen bonding on a single hydrogen donor. The presence of the hydroxyl group generally gives rise to a strong proper hydrogen bond, while the methyl group of methanol is likely involved in the weaker improper hydrogen bond, among other weak non-covalent interactions. The effects of the two types of hydrogen bonds on the stability, geometric parameters, and properties of electron density of methanol complexes are examined while considering different geometrical arrangements of the methanol dimer and the binary complexes of methanol with water, acetonitrile, and chloromethane. Subsequently, potential conclusions about the nature of improper hydrogen bonding and the origin of the C–H bond contraction that results upon complex formation are discussed. Quantum theory of atoms in molecules and natural bond orbital methods were used in the analysis; all calculations were performed at the MP2(full)/6-311++G(d,p) level of theory.

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 score0.064
Threshold uncertainty score0.677

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 head0.024
GPT teacher head0.233
Teacher spread0.210 · 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

Citations18
Published2011
Admission routes3
Has abstractyes

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