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Record W2901585539 · doi:10.1021/acs.organomet.8b00649

Improved Electrocatalytic CO<sub>2</sub> Reduction with Palladium bis(NHC) Pincer Complexes Bearing Cationic Side Chains

2018· article· en· W2901585539 on OpenAlexafffund
Emile E. DeLuca, Zhen Xu, J. H. W. LAM, Michael O. Wolf

Bibliographic record

VenueOrganometallics · 2018
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCationic polymerizationOniumCatalysisPalladiumSubstituentCarbeneSelectivityLinkerPincer movementAlkylElectrochemistryCombinatorial chemistryPolymer chemistryOrganic chemistryElectrodeIon

Abstract

fetched live from OpenAlex

Stabilizing interactions between charged electrocatalytic intermediates and a series of cationic residues were explored through the synthesis and characterization of six palladium bis( N -heterocyclic carbene) (NHC) complexes bearing unique onium functionalities. The presence of a positively charged, pendant substituent was found to mediate electrode kinetics and facilitate CO 2 coordination to the catalytic center in a systematic fashion. The introduction of cationic moieties into this system is shown to enhance catalytic selectivity for the conversion of CO 2 to CO by as much as 5 times that of an alkyl-bearing analog. A combination of electrochemical experiments and computational analysis demonstrates that catalyst performance benefits most from a bulky onium unit tethered to the catalyst through a flexible linker. This behavior was interpreted as a preference for a wide, hydrophobic reaction pocket that allows for the unhindered formation of catalytic intermediates and mediated interaction with the solution.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations25
Published2018
Admission routes2
Has abstractyes

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