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Record W4386193804 · doi:10.1002/anie.202310851

Catalytic <i>ipso</i>‐Nitration of Organosilanes Enabled by Electrophilic <i>N</i>‐Nitrosaccharin Reagent

2023· article· en· W4386193804 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsSaint Mary's University
FundersUniversity of BernSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsNitrationElectrophileChemistryReagentElectrophilic substitutionCombinatorial chemistryCatalysisElectrophilic aromatic substitutionOrganic chemistryComputational chemistry

Abstract

fetched live from OpenAlex

Abstract Nitroaromatic compounds represent one of the essential classes of molecules that are widely used as feedstock for the synthesis of intermediates, the preparation of nitro‐derived pharmaceuticals, agrochemicals, and materials on both laboratory and industrial scales. We herein disclose the efficient, mild, and catalytic ipso ‐nitration of organotrimethylsilanes, enabled by an electrophilic N ‐nitrosaccharin reagent and allows chemoselective nitration under mild reaction conditions, while exhibiting remarkable substrate generality and functional group compatibility. Additionally, the reaction conditions proved to be orthogonal to other common functionalities, allowing programming of molecular complexity via successive transformations or late‐stage nitration. Detailed mechanistic investigation by experimental and computational approaches strongly supported a classical electrophilic aromatic substitution (S E Ar) mechanism, which was found to proceed through a highly ordered transition state.

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.

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 score0.045
Threshold uncertainty score1.000

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)0.0010.000

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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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