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Record W3156360255 · doi:10.1002/ejoc.202100349

Silylating Disulfides and Thiols with Hydrosilicones Catalyzed by B(C<sub>6</sub>F<sub>5</sub>)<sub>3</sub>

2021· article· en· W3156360255 on OpenAlex
Mengchen Liao, Sijia Zheng, Michael A. Brook

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Organic Chemistry · 2021
Typearticle
Languageen
FieldChemistry
TopicSulfur-Based Synthesis Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryThio-SilylationSiloxaneCatalysisSilaneReactivity (psychology)Steric effectsCleaveSulfurThiolMedicinal chemistrySiliconePolymer chemistryOrganic chemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract Hydrosilanes and silicones, catalyzed with B(C 6 F 5 ) 3 , may be used to silylate thiols or cleave disulfides giving silyl thio ethers. Alcohols were found to react faster than thiols or disulfides, while alkoxysilanes (the Piers‐Rubinsztajn reaction) were slower such that the overall order of reactivity was found to be HO&gt;HS&gt;SS&gt;SiOEt. The resulting silane and silicone‐protected thio ethers produced from the sulfur‐based functional groups could be cleaved to thiols using alcohols or mild acid with rates that depend on the steric bulk of the siloxane.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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