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Record W2963558711 · doi:10.1002/anie.201905788

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

2019· review· en· W2963558711 on OpenAlexafffund
Patrick M. J. Szell, Lorraine Grébert, David L. Bryce

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typereview
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Veterans Affairs
KeywordsNuclear quadrupole resonanceHalogenCrystallographySingle crystalQuadrupoleHalogen bondSpectroscopyCoupling constantElectric field gradientResonance (particle physics)ChemistryCrystal (programming language)Triclinic crystal systemCovalent bondCrystal structureAnalytical Chemistry (journal)Materials scienceNuclear magnetic resonanceAtomic physicsOrganic chemistryAlkyl

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.308
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations27
Published2019
Admission routes2
Has abstractyes

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