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Record W4387145618 · doi:10.1039/d3sc00688c

Palladium nanoparticles on gallium nitride as a Mott–Schottky catalyst for efficient and durable photoactivation of unactivated alkanes

2023· article· en· W4387145618 on OpenAlexafffund
Lida Tan, Xianghua Kong, Mingxin Liu, Hui Su, Hong Guo, Chao‐Jun Li

Bibliographic record

VenueChemical Science · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersGuangdong Science and Technology DepartmentCompute CanadaNational Natural Science Foundation of ChinaNatural Science Foundation of Shenzhen CityMcGill University
KeywordsPalladiumCatalysisGalliumMaterials scienceNanoparticleGallium nitrideNanotechnologyChemical engineeringChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

)-N bond formation of unactivated alkanes is reported with a GaN based Mott-Schottky catalyst under photocatalytic reaction conditions. Long term stability and reaction efficiency (up to 92%) were achieved with this photocatalyst. The deposition of a Pd co-catalyst on the surface of GaN significantly enhanced the reaction efficiency. Microscopic investigation suggested a remarkable interaction in the Pd/GaN Schottky junction, giving a significant Pd/GaN depletion layer. In addition, density functional theory (DFT) calculations were performed to show the distinct performance of Pd nanoparticles at the atomic level.

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)0.000
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 score0.007
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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