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Record W4293150102 · doi:10.1002/aenm.202201461

Insight Into Heterogeneous Electrocatalyst Design Understanding for the Reduction of Carbon Dioxide

2022· article· en· W4293150102 on OpenAlexafffund
Xiaoyan Zhang, Zhen Zhang, Haibo Li, Rui Gao, Meiling Xiao, Jianbing Zhu, Ming Feng, Zhongwei Chen

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsElectrocatalystRational designNanotechnologyMaterials scienceBiochemical engineeringElectrodeChemistryEngineeringElectrochemistry

Abstract

fetched live from OpenAlex

Abstract The carbon dioxide reduction reaction (CO 2 RR) is a promising route to convert CO 2 into value‐added chemicals and fuels by utilizing renewable electrical energy, mitigating the greenhouse effect and depletion of fossil fuels for sustainability. Electrocatalyst plays a critical role in CO 2 RR whereas their rational design for achieving high activity, durability, and selectivity toward specific products confronts great challenge. In this review, rational CO 2 RR electrocatalyst design as well as essential understanding of nanomaterials in atomic‐, nanoscale‐, and microscale‐level are highlighted. Besides, basic concepts and setup factors related to CO 2 RR are systematically outlined to provide a clear and comprehensive understanding as guidance. More importantly, the authors discuss and try to uncover the electrocatalyst structure–function relationship with the assistance of electrokinetic studies, in situ characterizations, and computational techniques. Finally, current challenges and prospects are offered to shed light on future design of advanced CO 2 RR electrocatalysts.

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: none
Teacher disagreement score0.537
Threshold uncertainty score0.604

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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

Citations72
Published2022
Admission routes2
Has abstractyes

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