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Record W4381890053 · doi:10.1149/1945-7111/ace0dc

Synthesis of α-Amino Acids via Electrochemical Fixation of CO<sub>2</sub> to Imines Using Different Metal Cathodes

2023· article· en· W4381890053 on OpenAlexafffund
Anastasia Dmitrieva, Jury J. Medvedev, Xenia Medvedeva, Elena F. Krivoshapkina, Anna Klinkova

Bibliographic record

VenueJournal of The Electrochemical Society · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsElectrochemistrySelectivityFaraday efficiencyCatalysisCarbon fixationCathodeAmino acidMetalChemistryMaterials scienceInorganic chemistryCombinatorial chemistryOrganic chemistryElectrodeCarbon dioxidePhysical chemistry

Abstract

fetched live from OpenAlex

The electrochemical fixation of CO 2 by imines has recently attracted an increased interest as sustainable strategy for the synthesis of α -amino acids and a green alternative to the traditional Strecker synthesis, which relies on highly toxic precursors. Despite the industrial prospects of the electrochemical approach, the catalyst material effects on the selectivity of the process are still purely understood, hindering rational catalyst design. Herein, we study the electrochemical fixation of CO 2 by N -benzylideneaniline using a wide variety of cathode materials, including 10 polycrystalline metals (Ti, Zn, Au, Pd, Pt, Sn, Ag, Ni, Fe, Cu), glassy carbon, and Pd nanoparticles of different shapes. We found that among all studied bulk metals, Ti and Zn show the best results with above 93% faradaic efficiency of α -amino acid, while other materials show from good to low selectivity (12% for Sn). We also demonstrate that especially high current densities and nearly quantitative faradaic efficiency and selectivity of α -amino acids can be achieved by employing Pd nanoparticles.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.046
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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 teacher head, 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicCarbon dioxide utilization in catalysisFrench-language works237,207