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Electrochemical radiofluorination using a split-bipolar electrode

2024· article· en· W4402696468 on OpenAlexafffund
Tomoyuki Kurioka, Yingqing Lu, Jason Jones, Xueyi Wang, Daniel Hernández‐Valdés, Afaf Genady, R. Michael van Dam, Shinsuke Inagi, Saman Sadeghi

Bibliographic record

VenueJournal of Fluorine Chemistry · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicFluorine in Organic Chemistry
Canadian institutionsMcMaster University
FundersJapan Science and Technology AgencyJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthMinistry of Education, Culture, Sports, Science and Technology
KeywordsChemistryElectrochemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

• Electrochemical radiofluorination (ECF) is achieved with split bipolar electrodes. • Radiochemical conversion of up to 70 % for methyl (methylthio)acetate. • A m of 28–43 GBq/μmol is achieved with starting activity of 2.5–3.3 GBq. • A m obtained in this work is five times higher than previous reports using ECF. Electrochemical (radio)fluorination (ECF) is a versatile approach for nucleophilic radiofluorination of electron-rich compounds such as thioether derivatives. However, ECF generally requires high concentrations of supporting salts, which leads to low molar activity ( A m ) of the final product due to undesired contamination with [ 19 F]F – ions. Here, we demonstrate the first example of radiofluorination using a split bipolar electrode (s-BPE) platform with low concentration of supporting salt. Under optimal reaction conditions, the ECF of methyl (methylthio)acetate (MMTA) using s-BPEs provided [ 18 F]F-MMTA with radiochemical conversion of up to 70 % and high molar activities (28–43 GBq/μmol; 0.74–1.1 Ci/μmol), with approximately 5 mM of tetrabutylammonium perchlorate as supporting electrolyte.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.055
GPT teacher head0.409
Teacher spread0.354 · 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 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

Citations5
Published2024
Admission routes2
Has abstractyes

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