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Record W4390141797 · doi:10.1002/cctc.202301168

Electrocatalytic Nitrite Reduction in Neutral Water with Ni(II) and Co(II) Macrocycle Complexes: Catalytic Evaluation and Mechanistic Elucidation

2023· article· en· W4390141797 on OpenAlexafffund
Jonathan Ferguson, Joshua Brown, D.S. Richeson

Bibliographic record

VenueChemCatChem · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryNitriteHydroxylamineCatalysisInorganic chemistrySelectivityCoulometryAqueous solutionLigand (biochemistry)RedoxElectrochemistryMoietyCombinatorial chemistryNitrateOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Nitrite anions, of anthropogenic origin, in the environment disrupt the nitrogen cycle making the electrocatalytic reduction of nitrite a significant objective. Homogeneous Ni(II) and Co(II) complexes bearing a macrocyclic supporting ligand consisting of a tridentate redox active bis(imino)pyridine moiety coupled with a tertiary amine donor site are effective and selective for the electrocatalytic reduction of nitrite to ammonium ion and hydroxylamine in buffered (pH 7) aqueous solutions. Controlled potential coulometry at potentials from −0.98 to −1.05 V vs Ag/AgCl in 4‐morpholinepropanesulfonic acid (MOPS) buffer yielded ammonium as the major product with Faradaic efficiencies ranging from 76–90 %. A foot‐of‐the‐wave analysis yielded calculated turn‐over frequencies of 61 s −1 and 28 s −1 for the Ni and Co complexes, respectively. The Ni complex displayed a higher selectivity for NH 4 + /NH 2 OH than the Co analog. A computational examination of the catalytic mechanism of the Ni complex was used to support and elucidate the proposed chemical steps, provide some energetic details of the electron and proton transfers, and present a rationale for the selectivity of this reduction.

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)0.000
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 score0.023
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.251
Teacher spread0.235 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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