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Record W4226315882 · doi:10.26434/chemrxiv-2022-jtt63

A quantum-chemical analysis on the Lewis acidity of diaryl-halonium ions

2022· preprint· en· W4226315882 on OpenAlexafffund
Raphaël Robidas, Dominik L. Reinhard, Stefan M. Huber, Claude Y. Legault

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de SherbrookeNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesDeutsche ForschungsgemeinschaftCentre in Green Chemistry and CatalysisCompute Canada
KeywordsLewis acids and basesSteric effectsChemistryQuantum chemicalHalogenAtomic orbitalComputational chemistryNon-covalent interactionsCharge (physics)DecompositionIonHydrogen bondChemical physicsMoleculeStereochemistryOrganic chemistryElectronCatalysisQuantum mechanicsPhysics

Abstract

fetched live from OpenAlex

Cyclic diaryliodonium compounds like iodolium derivatives have increasingly found use as noncovalent Lewis acids in the last years. They are more stable than acyclic systems and are markedly more Lewis acidic. Herein, this higher Lewis acidity is analyzed and explained via quantum-chemical calculations and energy decomposition analyses. Its key origin is the change in energy levels and hybridization of iodine’s orbitals, leading to both more favorable electrostatic interaction and better charge transfer. Both of the latter seem to contribute in similar fashion, while hydrogen bonding as well as steric repulsion with the phenyl rings play at best a minor role. In comparison to iodolium, bromolium and chlorolium are less Lewis acidic the lighter the halogen, which is predominantly based on less favorable charge-transfer interactions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0150.000

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 head0.033
GPT teacher head0.265
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

Citations1
Published2022
Admission routes2
Has abstractyes

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