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Record W4280494115 · doi:10.1038/s41467-022-30327-2

Atomic overlayer of permeable microporous cuprous oxide on palladium promotes hydrogenation catalysis

2022· article· en· W4280494115 on OpenAlexaff
Kunlong Liu, Lizhi Jiang, Wugen Huang, Guo‐zhen Zhu, Yue‐Jiao Zhang, Chaofa Xu, Ruixuan Qin, Pengxin Liu, Chengyi Hu, Jingjuan Wang, Jian‐Feng Li, Fan Yang, Gang Fu, Nanfeng Zheng

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Manitoba
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationTencent
KeywordsOverlayerCatalysisMicroporous materialPalladiumMaterials scienceNanocrystalMetalOxideCrystal (programming language)NanotechnologyChemical engineeringHeterogeneous catalysisInorganic chemistryChemistryPhysical chemistryMetallurgyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The interfacial sites of metal-support interface have been considered to be limited to the atomic region of metal/support perimeter, despite their high importance in catalysis. By using single-crystal surface and nanocrystal as model catalysts, we now demonstrate that the overgrowth of atomic-thick Cu2O on metal readily creates a two-dimensional (2D) microporous interface with Pd to enhance the hydrogenation catalysis. With the hydrogenation confined within the 2D Cu2O/Pd interface, the catalyst exhibits outstanding activity and selectivity in the semi-hydrogenation of alkynes. Alloying Cu(0) with Pd under the overlayer is the major contributor to the enhanced activity due to the electronic modulation to weaken the H adsorption. Moreover, the boundary or defective sites on the Cu2O overlayer can be passivated by terminal alkynes, reinforcing the chemical stability of Cu2O and thus the catalytic stability toward hydrogenation. The deep understanding allows us to extend the interfacial sites far beyond the metal/support perimeter and provide new vectors for catalyst optimization through 2D interface interaction.

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 score0.001
Threshold uncertainty score0.003

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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

Citations65
Published2022
Admission routes1
Has abstractyes

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