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Record W2808363512 · doi:10.1039/c7cs00619e

Electrochemical strategies for C–H functionalization and C–N bond formation

2018· review· en· W2808363512 on OpenAlexfundno aff
Markus D. Kärkäs

Bibliographic record

VenueChemical Society Reviews · 2018
Typereview
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsnot available
FundersVetenskapsrådetYork University
KeywordsSurface modificationElectrochemistryExpeditingChemistryCarbon fibersCombinatorial chemistryNanotechnologyMaterials scienceElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Conventional methods for carrying out carbon-hydrogen functionalization and carbon-nitrogen bond formation are typically conducted at elevated temperatures, and rely on expensive catalysts as well as the use of stoichiometric, and perhaps toxic, oxidants. In this regard, electrochemical synthesis has recently been recognized as a sustainable and scalable strategy for the construction of challenging carbon-carbon and carbon-heteroatom bonds. Here, electrosynthesis has proven to be an environmentally benign, highly effective and versatile platform for achieving a wide range of nonclassical bond disconnections via generation of radical intermediates under mild reaction conditions. This review provides an overview on the use of anodic electrochemical methods for expediting the development of carbon-hydrogen functionalization and carbon-nitrogen bond formation strategies. Emphasis is placed on methodology development and mechanistic insight and aims to provide inspiration for future synthetic applications in the field of electrosynthesis.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.060
GPT teacher head0.338
Teacher spread0.278 · 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

Citations994
Published2018
Admission routes1
Has abstractyes

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