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Record W4313339520 · doi:10.1021/acsnano.2c10707

Mo<sub>2</sub>TiC<sub>2</sub> MXene-Supported Ru Clusters for Efficient Photothermal Reverse Water–Gas Shift

2022· article· en· W4313339520 on OpenAlexaff
Zhiyi Wu, Jiahui Shen, Chaoran Li, Chengcheng Zhang, Kai Feng, Zhiqiang Wang, Xuchun Wang, Debora Meira, Mujin Cai, Dake Zhang, Shenghua Wang, Mingyu Chu, Jinxing Chen, Yuyao Xi, Liang Zhang, Tsun‐Kong Sham, Alexander Genest, Günther Rupprechter, Xiaohong Zhang, Le He

Bibliographic record

VenueACS Nano · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsWestern University
FundersNational Postdoctoral Program for Innovative TalentsCollaborative Innovation Center of Suzhou Nano Science and TechnologySoochow UniversityHigher Education Discipline Innovation ProjectNatural Science Foundation of Jiangsu ProvinceChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaSuzhou Key Laboratory of Functional Nano and Soft MaterialsNational Natural Science Foundation of ChinaU.S. Department of EnergyAustrian Science Fund
KeywordsCatalysisWater-gas shift reactionCarbon monoxidePhotothermal therapyMaterials scienceMethaneCarbon dioxideCarbon fibersDesorptionNanoparticleChemical engineeringPhotochemistryNanotechnologyChemistryAdsorptionPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

, which is among the best reported so far for photothermal RWGS catalysts. Detailed studies suggest that the production of methane is kinetically inhibited by the rapid desorption of CO from the surface of the Ru clusters.

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 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 score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.010
GPT teacher head0.235
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 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

Citations150
Published2022
Admission routes1
Has abstractyes

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