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Record W4283370351 · doi:10.1021/jacs.2c02972

Templating Bicarbonate in the Second Coordination Sphere Enhances Electrochemical CO <sub>2</sub> Reduction Catalyzed by Iron Porphyrins

2022· article· en· W4283370351 on OpenAlexafffund
Jeffrey S. Derrick, Matthias Loipersberger, Sepand K. Nistanaki, Aila V. Rothweiler, Martin Head‐Gordon, Eva M. Nichols, Christopher J. Chang

Bibliographic record

VenueJournal of the American Chemical Society · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
FundersCanadian Institute for Advanced ResearchStanford UniversityLawrence Berkeley National LaboratoryNational Science Foundation
KeywordsChemistryBicarbonateCatalysisPorphyrinElectrochemistryInorganic chemistryElectrolyteCoordination sphereElectrocatalystCombinatorial chemistryPhotochemistryMetalOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Bicarbonate-based electrolytes are ubiquitous in aqueous electrochemical CO2 reduction, particularly in heterogenous catalysis, where they demonstrate improved catalytic performance relative to other buffers. In contrast, the presence of bicarbonate in organic electrolytes and its roles in homogeneous electrocatalysis remain underexplored. Here, we investigate the influence of bicarbonate on iron porphyrin-catalyzed electrochemical CO2 reduction. We show that bicarbonate is a viable proton donor in organic electrolyte (pKa = 20.8 in dimethyl sulfoxide) and that urea pendants in the second coordination sphere can be used to template bicarbonate in the vicinity of a molecular iron porphyrin catalyst. The templated binding of bicarbonate increases its acidity, resulting in a 1500-fold enhancement in catalytic rates relative to unmodified parent iron porphyrin. This work emphasizes the importance of bicarbonate speciation in wet organic electrolytes and establishes second-sphere bicarbonate templating as a design strategy to harness this adventitious acid and enhance CO2 reduction catalysis.

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)0.001
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 score0.010
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.233
Teacher spread0.227 · 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

Citations110
Published2022
Admission routes2
Has abstractyes

Explore more

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