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Record W4294919461 · doi:10.1021/acsami.2c09615

Abundant (110) Facets on PdCu<sub>3</sub> Alloy Promote Electrochemical Conversion of CO<sub>2</sub> to CO

2022· article· en· W4294919461 on OpenAlexaff
Jianwu Dong, Ying‐Yao Cheng, Ying Li, Xianyun Peng, Rui Zhang, Hsiao‐Tsu Wang, Chunyang Wang, Xiaoyan Li, Pengfei Ou, Chih‐Wen Pao, Lili Han, W. F. Pong, Zhang Lin, Jun Luo, Huolin L. Xin

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceAlloyElectrochemistryMetallurgyChemical engineeringCrystallographyPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Electrochemical conversion of CO2 to high-value chemical fuels offers a promising strategy for managing the global carbon balance but faces huge challenges due to the lack of effective electrocatalysts. Here, we reported PdCu3 alloy nanoparticles with abundant exposed (110) facets supported on N-doped three-dimensional interconnected carbon frameworks (PdCu3/NC) as an efficient and durable electrocatalyst for electrochemical CO2 reduction to CO. The catalyst exhibits extremely high intrinsic CO2 reduction selectivity for CO production with a Faraday efficiency of nearly 100% at a mild potential of −0.5 V. Moreover, a rechargeable high-performance Zn–CO2 battery with PdCu3/NC as a cathode is developed to deliver a record-high energy efficiency of 99.2% at 0.5 mA cm–2 and rechargeable stability of up to 133 h. Theoretical calculations elucidate that the exposed (110) facet over PdCu3/NC is the active center for CO2 activation and rapid formation of the key *COOH intermediate.

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 categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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.

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

Citations22
Published2022
Admission routes1
Has abstractyes

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