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Record W2954287805 · doi:10.1016/j.joule.2019.05.010

Quantum-Dot-Derived Catalysts for CO2 Reduction Reaction

2019· article· en· W2954287805 on OpenAlexafffund
Min Liu, Mengxia Liu, Xiaoming Wang, Sergey M. Kozlov, Zhen Cao, Phil De Luna, Hongmei Li, Xiaoqing Qiu, Kang Liu, Junhua Hu, Chuankun Jia, Peng Wang, Huimin Zhou, Jun He, Miao Zhong, Xinzheng Lan, Yansong Zhou, Zhiqiang Wang, Jun Li, Ali Seifitokaldani, Cao‐Thang Dinh, Hongyan Liang, Chengqin Zou, Daliang Zhang, Yang Yang, Ting‐Shan Chan, Yu Han, Luigi Cavallo, Tsun‐Kong Sham, Bing‐Joe Hwang, Edward H. Sargent

Bibliographic record

VenueJoule · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsWestern UniversityCanada Research ChairsUniversity of Toronto
FundersInnovation-Driven Project of Central South UniversityFonds de recherche du Québec – Nature et technologiesNational Natural Science Foundation of ChinaState Key Laboratory of Powder MetallurgyCanadian Institute for Advanced ResearchScience, Technology and Innovation Commission of Shenzhen MunicipalityMinistry of Science and Technology, TaiwanUniversity of TorontoCentral South UniversityUniversity of Science and Technology of ChinaKing Abdullah University of Science and TechnologyNatural Sciences and Engineering Research Council of CanadaHunan Provincial Science and Technology Department
KeywordsQuantum dotCatalysisReduction (mathematics)Materials scienceNanotechnologyChemical engineeringChemistryEngineeringMathematicsGeometryOrganic chemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations145
Published2019
Admission routes2
Has abstractno

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