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Record W1975906375 · doi:10.2202/1542-6580.1301

Plasma-Prepared Ni/Al2O3: Preparation, Characterization and Toluene Hydrogenation

2006· article· en· W1975906375 on OpenAlexafffund
Lianhui Ding, Zisheng Jason Zhang, Ying Zheng, Zhikun Zhang, Zbigniew Ring, Jinwen Chen

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of FrederictonUniversity of New BrunswickUniversity of Ottawa
FundersAtlantic Canada Opportunities Agency
KeywordsNickelCatalysisMicroreactorTolueneMaterials scienceChemical engineeringBimetallic stripHydrogenInorganic chemistryNanoparticleNuclear chemistryChemistryMetallurgyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Nano-nickel particles were prepared using hydrogen arc plasma method and new nano-nickel hydrogenation catalysts were prepared through calcinating mixtures of the plasma-prepared nano-nickel particles and a ?-alumina support. The catalysts were characterized by using SEM, XRD, and TPR. The activities of the catalysts were evaluated through the hydrogenation of toluene in a fixed be microreactor. Catalysts prepared by conventional comulling and impregnation methods were also examined for comparative purposes. The characterization results showed that the nano-nickel catalysts had an egg-shell structure in terms of nickel-alumina distribution, with most of the nickel located at the outer surface of the catalyst particles. The hydrogenation experiments showed that the nano-nickel catalyst provided much higher activities than those co-precipitated and impregnated nickel catalysts with similar nickel contents. However, both types of catalysts followed first-order reaction kinetics and similar activation energies were observed.

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 score0.008
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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