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Record W3175599846 · doi:10.1002/asia.202100583

Recent Advances in Bimetallic Cu‐Based Nanocrystals for Electrocatalytic CO<sub>2</sub> Conversion

2021· review· en· W3175599846 on OpenAlex
Biva Talukdar, Shruti Mendiratta, Michael H. Huang, Chun‐Hong Kuo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemistry - An Asian Journal · 2021
Typereview
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Calgary
FundersMinistry of Science and Technology, Taiwan
KeywordsBimetallic stripNanomaterial-based catalystNanotechnologyReuseNanomaterialsMaterials scienceNanocrystalRenewable energyEnergy transformationEnvironmental scienceNanoparticleMetallurgyWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract An elevated level of anthropogenic CO 2 has been the major cause of global warming, and significant efforts are being made around the world towards the development of CO 2 capture, storage and reuse technologies. Among various CO 2 conversion technologies, electrochemical CO 2 reduction (CO 2 RR) by nanocrystals is one of the most promising strategies as it is facile, quick, and can be integrated with other renewable energy techniques. Judiciously designed catalytic nanomaterials promise to be the next generation of electrochemical electrodes that offer cutting‐edge performance, low energy consumption and aid in reducing overall carbon footprint. In this minireview, we highlight the recent developments related to the bimetallic Cu‐based nanocatalysts and discuss their structure‐property relationships. We focus on the design principles and parameters required for the enhancement of CO 2 conversion efficiency, selectivity, and stability.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.312
Teacher spread0.287 · 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