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Record W4254747579 · doi:10.1002/ange.201905788

Rapid Identification of Halogen Bonds in Co‐Crystalline Powders via <sup>127</sup>I Nuclear Quadrupole Resonance Spectroscopy

2019· article· en· W4254747579 on OpenAlexafffund
Patrick M. J. Szell, Lorraine Grébert, David L. Bryce

Bibliographic record

VenueAngewandte Chemie · 2019
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear quadrupole resonanceHalogenChemistryQuadrupoleHalogen bondSingle crystalCrystallographySpectroscopyResonance (particle physics)Coupling constantCrystal (programming language)Covalent bondCrystal structureAnalytical Chemistry (journal)Nuclear magnetic resonanceAtomic physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract 127 I nuclear quadrupole resonance (NQR) spectroscopy is established as a rapid and robust method to indicate the formation of iodine–nitrogen halogen bonds in co‐crystalline powders. Once the relevant spectral frequency range has been established, diagnostic 127 I NQR spectra can be acquired in seconds. The method is demonstrated for a series of co‐crystals of 1,4‐diiodobenzene. Changes in the 127 I quadrupolar coupling constant ( C Q ) by up to 74.4 MHz correlate with the length of the C−I donor covalent bond and inversely with the I⋅⋅⋅N halogen‐bond length. The predictive power of this technique is validated on two previously unknown co‐crystalline powders prepared mechanochemically. Single‐crystal growth via co‐sublimation and structure determination by single‐crystal X‐ray diffraction cross‐validates the findings. Natural localized molecular‐orbital analyses provide insight into the origins of the quadrupolar coupling constants.

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 categoriesInsufficient payload (model declined to judge)
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 score0.052
Threshold uncertainty score0.999

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)0.0020.000

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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