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Record W4386590086 · doi:10.1021/acsami.3c08545

Ultrathin Atomic Layer Deposited Al<sub>2</sub>O<sub>3</sub> Overcoat Stabilizes Al<sub>2</sub>O<sub>3</sub>-Pt/Ni-Foam Hydrogenation Catalysts

2023· article· en· W4386590086 on OpenAlexafffund
Azina Rahmani, Maksim Sultanov, Kemah Kamiru-White, Lorianne R. Shultz‐Johnson, Brian E. Butkus, Shaohua Xie, Fudong Liu, Điệp Thị Hồng Nguyễn, Noémie Wilson-Faubert, Ali Nazemi, Parag Banerjee, Lei Zhai, Massimiliano Delferro, Jianguo Wen, Titel Jurca

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaNational Science FoundationUniversity of Central FloridaU.S. Department of EnergyDivision of Materials ResearchOffice of ScienceBasic Energy SciencesDivision of Electrical, Communications and Cyber SystemsFord Foundation
KeywordsMaterials scienceCatalysisAtomic layer depositionLayer (electronics)Chemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Galvanic exchange seeds the growth of Pt nanostructures on the Ni foam monolith. Subsequent atomic layer deposition of ultrathin Al 2 O 3 followed by annealing under air affords supported Pt catalysts with ultralow loading (0.020 ppm). In addition to the expected enhancement of the stability of the Pt particles on the surface, the ∼2 nm Al 2 O 3 overcoat appears to also play a crucial role in the overall structural integrity of the NiO x nanoplates that grow on the Ni foam surface as a result of the preparative route. The resulting material is physically robust toward repeated handling and showcases retention of catalytic activity over 10 standard catalyst recycling trials, standing in marked contrast to the uncoated samples. Catalyst activity was tested via the hydrogenation of various functionalized styrenes at low temperatures and low hydrogen pressure in ethanol as a solvent, with a TOF as high as 9.5 × 10 6 h –1 for unfunctionalized styrene. Notably, the catalysts show excellent tolerance toward F, Cl, and Br substituents and no hydrogenation of the aromatic ring.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.009

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 head0.247
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2023
Admission routes2
Has abstractyes

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