MétaCan
Menu
Back to cohort
Record W2987038921 · doi:10.1021/acs.accounts.9b00439

Molecular Control of Heterogeneous Electrocatalysis through Graphite Conjugation

2019· article· en· W2987038921 on OpenAlexfundno aff
Megan N. Jackson, Yogesh Surendranath

Bibliographic record

VenueAccounts of Chemical Research · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchResearch Corporation for Science AdvancementBasic Energy SciencesAlfred P. Sloan Foundation
KeywordsElectrocatalystChemistryCatalysisRedoxNanotechnologyDelocalized electronElectrochemistryActive siteMoleculeChemical physicsMaterials scienceElectrodeInorganic chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Conspectus The efficient interconversion of electrical and chemical energy requires catalysts capable of accelerating multielectron reactions at or near electrified interfaces. These reactions can be performed at metallic surface sites on heterogeneous electrocatalysts or through redox mediation at molecular electrocatalysts. The relative ease of synthesis and characterization for homogeneous catalysts has allowed for molecular-level control over the active site and permitted systematic tuning of activity and selectivity. Similar control is difficult to achieve with heterogeneous electrocatalysts, because they typically exhibit a distribution of active site geometries and local electronic structures, which are challenging to modify with molecular precision. However, metallic heterogeneous electrocatalysts benefit from a continuum of electronic states that distribute the redox burden of multielectron transformations, enabling more efficient catalysis. We envisioned that we could combine the attractive properties of molecular and heterogeneous catalysts by integrating tunable molecular active sites into the delocalized band states of a conductive solid. The Surendranath group has developed a class of electrocatalysts in which molecules are strongly electronically coupled to graphitic electrodes through a conductive, aromatic pyrazine linkage such that they behave like metallic surface active sites. In this Account, we discuss the dual role of these graphite-conjugated catalysts (GCCs) as a platform with which to answer molecular-level questions of metallic active sites and as a tool with which to fundamentally alter the mechanism and enhance the performance of molecular active sites. We begin by describing the electrochemical and spectroscopic studies that demonstrated that GCC sites behave like metallic active sites rather than simply as redox mediators attached to electrode surfaces. We then discuss how electrochemical studies of a series of graphite-conjugated acids enabled the construction of a molecular model for the thermochemistry of proton-coupled electron transfer reactions at GCC sites based on the p K a of the molecular analogue of the conjugated site and the potential of zero free charge of the electrode. In the final section, we discuss the effects of graphite conjugation on the mechanism and rate of oxygen reduction, hydrogen evolution, and carbon dioxide reduction catalysis across four different GCC platforms involving N-heterocycle, organometallic, and metalloporphyrin active sites. We discuss how molecular-level tuning at graphite-conjugated active sites directly correlates to changes in catalytic activity for the oxygen reduction reaction. We demonstrate that graphite-conjugated porphyrins show enhanced catalytic oxygen reduction activity over amide-linked porphyrins. Lastly, we describe how catalysis at graphite-conjugated sites proceeds through mechanisms involving concerted electron transfer and substrate activation, in stark contrast to the mechanisms observed for molecular analogues. Overall, we showcase how GCCs provide a rich platform for controlling heterogeneous catalysis at the molecular level.

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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.320
Teacher spread0.303 · 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

Citations140
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAccounts of Chemical ResearchSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207