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Record W4214767920 · doi:10.1002/ange.201912857

Understanding the Origin of Highly Selective CO<sub>2</sub> Electroreduction to CO on Ni,N‐doped Carbon Catalysts

2020· article· en· W4214767920 on OpenAlexaff
David M. Koshy, Shucheng Chen, Dong Un Lee, Michaela Burke Stevens, Ahmed Abdellah, Samuel Dull, Gan Chen, Dennis Nordlund, Alessandro Gallo, Christopher Hahn, Drew Higgins, Zhenan Bao, Thomas F. Jaramillo

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcMaster University
FundersOffice of ScienceNational Science Foundation
KeywordsCatalysisCarbon fibersElectrochemistryInorganic chemistryNitrogenMetalPyrolysisPorphyrinActive siteMaterials sciencePolyacrylonitrileChemistryPhysical chemistryPhotochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Ni,N‐doped carbon catalysts have shown promising catalytic performance for CO 2 electroreduction (CO 2 R) to CO; this activity has often been attributed to the presence of nitrogen‐coordinated, single Ni atom active sites. However, experimentally confirming Ni−N bonding and correlating CO 2 reduction (CO 2 R) activity to these species has remained a fundamental challenge. We synthesized polyacrylonitrile‐derived Ni,N‐doped carbon electrocatalysts (Ni‐PACN) with a range of pyrolysis temperatures and Ni loadings and correlated their electrochemical activity with extensive physiochemical characterization to rigorously address the origin of activity in these materials. We found that the CO 2 R to CO partial current density increased with increased Ni content before plateauing at 2 wt % which suggests a dispersed Ni active site. These dispersed active sites were investigated by hard and soft X‐ray spectroscopy, which revealed that pyrrolic nitrogen ligands selectively bind Ni atoms in a distorted square‐planar geometry that strongly resembles the active sites of molecular metal–porphyrin catalysts.

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 score0.031
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.267
Teacher spread0.223 · 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

Citations53
Published2020
Admission routes1
Has abstractyes

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