MétaCan
Menu
Back to cohort
Record W3197443761 · doi:10.26434/chemrxiv.11923176.v1

Metal–Ligand Exchange Coupling Promotes Iron-Catalyzed Electrochemical CO2 Reduction at Low Overpotentials

2020· preprint· en· W3197443761 on OpenAlexfundno aff
Jeffrey S. Derrick, Matthias Loipersberger, Diana A. Iovan, Peter T. Smith, Khetpakorn Chakarawet, Jeffrey R. Long, Martin Head‐Gordon, Christopher J. Chang

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersBasic Energy SciencesDivision of ChemistryLawrence Berkeley National LaboratoryCanadian Institute for Advanced ResearchU.S. Department of EnergyOffice of ScienceNational Institutes of HealthNational Science Foundation
KeywordsOverpotentialCatalysisElectrochemistrySelectivityMetalChemistryLigand (biochemistry)Inorganic chemistryCoupling (piping)Delocalized electronPhotochemistryMaterials scienceChemical physicsElectrodePhysical chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Biological and heterogenous catalysts for electrochemical CO2 reduction often exhibit a high degree of electronic delocalization that serves to minimize overpotential and maximize selectivity over hydrogen evolution. Here, we report a molecular iron(II) complex that achieves a similar feat as a result of strong metal–ligand exchange coupling. This interaction promotes an open-shell singlet electronic structure that drives the electrochemical reduction of CO2 to CO with over 90% selectivity and turnover frequencies of 100,000 s−1 at low overpotentials, with no degradation over 20 hours. The decrease in the thermodynamic barrier engendered by this strong metal–ligand exchange coupling enables homogeneous CO2 reduction catalysis in water without compromising reaction selectivity.

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 categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueChemRxivSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207