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Record W3167503671 · doi:10.1021/acsnano.1c02094

Low-Temperature Synthesis of Single Palladium Atoms Supported on Defective Hexagonal Boron Nitride Nanosheet for Chemoselective Hydrogenation of Cinnamaldehyde

2021· article· en· W3167503671 on OpenAlex

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

VenueACS Nano · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsQueen's University
FundersNortheast Petroleum UniversityChina Postdoctoral Science Foundation
KeywordsNanosheetMaterials sciencePalladiumVacancy defectCatalysisHydrodenitrogenationDensity functional theoryBoronBoron nitrideNanotechnologyChemical engineeringCrystallographyHydrodesulfurizationChemistryOrganic chemistryComputational chemistryMetallurgySulfur

Abstract

fetched live from OpenAlex

Metal-support interactions are of great importance in determining the support-activity in heterogeneous catalysis. Here we report a low-temperature synthetic strategy to create atomically dispersed palladium atoms anchored on defective hexagonal boron nitride (h-BN) nanosheet. Density functional theory (DFT) calculations suggest that the nitrogen-containing B vacancy can provide stable anchoring sites for palladium atoms. The presence of single palladium atoms was confirmed by spherical aberration correction electron microscopy and extended X-ray absorption fine structure measurement. This catalyst showed exceptional efficiency in chemoselective hydrogenation of cinnamaldehyde, along with excellent recyclability, sintering-resistant ability, and scalability. We anticipate this synthetic approach for the synthesis of high-quality SACs based on h-BN support is amenable to large-scale production of bench-stable catalysts with maximum atom efficiency for industrial applications.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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