MétaCan
Menu
Back to cohort
Record W4206947107 · doi:10.1002/bkcs.12489

Ligand effects in rhodium complexes for chemical <scp>NADH</scp> regeneration

2022· article· en· W4206947107 on OpenAlexfundno aff
Pegah Tavakoli Fard, Kayoung Kim, SoHyun Lee, Jinheung Kim

Bibliographic record

VenueBulletin of the Korean Chemical Society · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
FundersNational Research FoundationMinistry of Environment - Saskatchewan
KeywordsChemistryRhodiumLigand (biochemistry)CatalysisKinetic isotope effectFormateMedicinal chemistrySteric effectsCofactorStereochemistryDeuteriumOrganic chemistryEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Abstract Various (pentamethylcyclopentadienyl)‐rhodium(III) complexes comprising aromatic bidentate ligands [(Cp*)Rh(L)Cl]+ (Cp* = pentamethylcyclopentadienyl, L = 2,2′‐bipyridine, 1,10‐phenanthroline, and their derivatives) were prepared to compare their reactivities of chemical cofactor regeneration. When the catalytic NADH regeneration was performed with sodium formate−, the reaction rates were compared with six Rh(III) complexes. The kinetics of the reactions including kinetic isotope effects are also studied in the chemical NADH regeneration. The Rh(III) complexes react with formate efficiently to afford the intermediates [(Cp*)Rh(L)(H)]+ and CO2. The electronic and steric effects of the ligands of the Rh complexes were observed on the reaction rates. The overall reaction rates of the NADH regeneration were also obtained using DCOO− in H2O. Such H/D exchange rates of [(Cp*)Rh(L)(H)]+ and the observation of a deuterium kinetic isotope provide valuable mechanistic insight into the catalytic NADH regeneration.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of the Korean Chemical SocietySame topicCarbon dioxide utilization in catalysisFrench-language works237,207