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Record W3174685129 · doi:10.1007/s12274-021-3662-y

Engineering the atomic interface of porous ceria nanorod with single palladium atoms for hydrodehalogenation reaction

2021· article· en· W3174685129 on OpenAlexaff
Zhijun Li, Mingyang Zhang, Lili Zhang, Xiuli Dong, Leipeng Leng, J. Hugh Horton, Jun Wang

Bibliographic record

VenueNano Research · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsQueen's University
Fundersnot available
KeywordsPalladiumNanorodCatalysisMaterials scienceAtom (system on chip)MetalDensity functional theoryPorosityNanotechnologyElectronic structureAtomic unitsChemical engineeringChemical physicsChemistryComputational chemistryOrganic chemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Tuning the electronic properties of single atom catalysts (SACs) between the central metal and the neighboring surface atoms has emerged as an efficient strategy to boost catalytic efficiency and metal utilization. Here we describe a simple and efficient approach to create atomically dispersed palladium atoms supported over defect-containing porous ceria nanorod containing palladium up to 0.26 wt.%. The existence of singly dispersed palladium atoms is confirmed by spherical aberration correction electron microscopy and extended X-ray absorption fine structure measurements. This catalyst shows excellent efficiency in hydrodehalogenation reactions at low H2 pressure under mild conditions, along with satisfactory recyclability and scalability. Density functional theory (DFT) calculations reveal that the high activity stems from the spatial isolation of palladium atoms and the modified electronic structure of palladium confined in defect-containing ceria nanorod. This work may lay the foundation for the facile creation of single atom catalysts within the synthetic community and shed light on the possibility for scale-up production.

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 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)0.003
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 score0.003
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.333
Teacher spread0.292 · 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 teacher head, 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

Citations28
Published2021
Admission routes1
Has abstractyes

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