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Record W4385629968 · doi:10.3762/bjoc.19.86

Exploring the role of halogen bonding in iodonium ylides: insights into unexpected reactivity and reaction control

2023· review· en· W4385629968 on OpenAlexafffund
Carlee A. Montgomery, Graham K. Murphy

Bibliographic record

VenueBeilstein Journal of Organic Chemistry · 2023
Typereview
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCompute Canada
KeywordsHypervalent moleculeYlideChemistryHalogen bondNucleophileHalogenIntramolecular forceLewis acids and basesChemoselectivityReactivity (psychology)Combinatorial chemistryComputational chemistryOrganic chemistryIodineAlkylCatalysis

Abstract

fetched live from OpenAlex

Halogen bonding is commonly found with iodine-containing molecules, and it arises when Lewis bases interact with iodine’s σ-holes. Halogen bonding and σ-holes have been encountered in numerous monovalent and hypervalent iodine-containing compounds, and in 2022 σ-holes were computationally confirmed and quantified in the iodonium ylide subset of hypervalent iodine compounds. In light of this new discovery, this article provides an overview of the reactions of iodonium ylides in which halogen bonding has been invoked. Herein, we summarize key discoveries and mechanistic proposals from the early iodonium ylide literature that invoked halogen bonding-type mechanisms, as well as recent reports of reactions between iodonium ylides and Lewis basic nucleophiles in which halogen bonding has been specifically invoked. The reactions discussed herein are organized to enable the reader to build an understanding of how halogen bonding might impact yield and chemoselectivity outcomes in reactions of iodonium ylides. Areas of focus include nucleophile σ-hole selectivity, and how ylide structural modifications and intramolecular halogen bonding (e.g., the ortho -effect) can improve ylide stability or solubility, and alter reaction outcomes.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.306
Teacher spread0.237 · 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
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

Citations19
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueBeilstein Journal of Organic ChemistrySame topicCatalytic C–H Functionalization MethodsFrench-language works237,207