MétaCan
Menu
Back to cohort
Record W2343461168 · doi:10.1021/acs.jpcc.6b02806

<sup>35</sup>Cl Solid-State NMR and Computational Study of Chlorine Halogen Bond Donors in Single-Component Crystalline Chloronitriles

2016· article· en· W2343461168 on OpenAlexafffund
Patrick M. J. Szell, David L. Bryce

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Council CanadaFonds National de la Recherche Luxembourg
KeywordsHalogen bondHalogenChemistryLone pairNatural bond orbitalChlorineCrystallographyComputational chemistryMoleculeDensity functional theoryOrganic chemistryAlkyl

Abstract

fetched live from OpenAlex

Halogen bonding is a noncovalent interaction between the electrophilic region of a halogen (σ-hole) and an electron donor, which has gained popularity in the field of crystal engineering due to its strength and linearity. Here, we present a 35 Cl solid-state NMR study of chlorine atoms as halogen bond donors, with interpretation aided by crystallographic symmetry and computational chemistry. In a series of chlorinated benzonitrile compounds, the magnitude of the 35 Cl quadrupolar coupling constant ( C Q ) was found to increase, and the quadrupolar asymmetry parameter (η) was found to decrease upon halogen bonding. A natural localized molecular orbital (NLMO) analysis attributes these changes to an increase in the contribution from the lone pair NLMOs to | C Q |. This is distinguished from a short proton-chlorine contact, which causes an increase in the magnitude of the σ-bond contribution and a decrease in the lone pair contribution, resulting in an overall decrease in | C Q |. This first direct 35 Cl solid-state NMR study of chlorine as a halogen bond donor provides new physical insight into the relationship between NMR observables and the halogen bond.

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 score0.022
Threshold uncertainty score0.441

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 head0.016
GPT teacher head0.271
Teacher spread0.256 · 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

Citations62
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207