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Record W3080770276 · doi:10.1039/d0nr04391e

Single Ni atoms with higher positive charges induced by hydroxyls for electrocatalytic CO<sub>2</sub> reduction

2020· article· en· W3080770276 on OpenAlexaff
Xiao Yang, Jun Cheng, Baizeng Fang, Xiaoxu Xuan, Niu Liu, Xiàn Yáng, Junhu Zhou

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNational Natural Science Foundation of China
KeywordsFaraday efficiencyX-ray photoelectron spectroscopyCatalysisDensity functional theoryMaterials scienceScanning transmission electron microscopySpectroscopyTransmission electron microscopyRedoxChemistryElectrochemistryCrystallographyInorganic chemistryElectrodePhysical chemistryNanotechnologyComputational chemistryChemical engineering

Abstract

fetched live from OpenAlex

To promote the faradaic efficiency of the electrocatalytic CO2 reduction reaction (CO2RR) with low-cost catalysts, single Ni atoms with higher positive charges induced by hydroxyls were proposed to form an atomically dispersed Ni-N4 structure in a cheap honeycomb-like carbon matrix for electrocatalytic CO2 reduction. Extended X-ray absorption fine structure spectroscopy, aberration-corrected High-angle annular dark-field scanning transmission electron microscopy and X-ray photoelectron spectroscopy measurements confirmed that the active-center structure consists of single Ni atoms and the adjacent hydroxyl via hydrothermal treatment (H-Ni/NC). Density functional theory calculations indicated that the isolated Ni atoms with higher positive charges induced by the hydroxyl decreased the free energy of the rate-limiting step to 1.05 eV for the CO2RR. The faradaic efficiency (FE) of CO2 reduction into CO was ≥88.0% over the H-Ni/NC catalyst in the potential range of -0.5 to -0.9 V (vs. RHE). The peak CO FE reached 97% at -0.7 V due to the synergistic effect between the unsaturated Ni-N4 active sites and the hydroxyl species.

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 score0.011
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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