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Record W2299499736 · doi:10.1021/acs.jpca.5b10848

NMR Investigations of Noncovalent Carbon Tetrel Bonds. Computational Assessment and Initial Experimental Observation

2015· article· en· W2299499736 on OpenAlexafffund
Scott A. Southern, David L. Bryce

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2015
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNon-covalent interactionsCrystallographyDensity functional theoryMaterials scienceComputational chemistryHydrogen bondChemistryMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

Group IV tetrel elements may act as tetrel bond donors, whereby a region of positive electrostatic potential (σ-hole) interacts with a Lewis base. The results of calculations of NMR parameters are reported for a series of model compounds exhibiting tetrel bonding from a methyl carbon to the oxygen or nitrogen atoms in various functional groups. The (13)C chemical shift (δiso) and the (1c)J((13)C,Y) coupling (Y = (17)O, (15)N) across the tetrel bond are recorded as a function of geometry. The sensitivity of the NMR parameters to the noncovalent interaction is demonstrated via an increase in δiso and in |(1c)J((13)C,Y)| as the tetrel bond shortens. Gauge-including projector-augmented wave density functional theory (DFT) calculations of δiso are reported for crystals that exhibit tetrel bonding in the solid state. Experimental δiso values for solid sarcosine and its tetrel-bonded salts corroborate the computational findings. This work offers new insights into tetrel bonding and facilitates the incorporation of tetrel bonds as restraints in NMR crystallographic structure refinement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.329
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations96
Published2015
Admission routes2
Has abstractyes

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