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Record W2796740108 · doi:10.1002/aic.16177

Co‐doped ZnO thin films grown by pulsed electron beam ablation as model nano‐catalysts in fischer‐tropsch synthesis

2018· article· en· W2796740108 on OpenAlexafffund
Asghar Ali, Redhouane Henda, James Aluha, Nicolas Abatzoglou

Bibliographic record

VenueAIChE Journal · 2018
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversité de SherbrookeLaurentian University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceCatalysisChemical engineeringFischer–Tropsch processThin filmSubstrate (aquarium)Scanning electron microscopeMicrostructureDopingNanotechnologySelectivityComposite materialOrganic chemistryChemistryOptoelectronics

Abstract

fetched live from OpenAlex

A single‐step deposition of cobalt‐doped zinc oxide (Co‐ZnO) thin film nano‐composites on three different crystalline substrates, viz., Al 2 O 3 (c‐sapphire), silicon (100) (Si), and SiO 2 (quartz) is reported, using pulsed electron beam ablation (PEBA). The results indicate that the type of substrate has no effect on Co‐ZnO films stoichiometry, morphology, microstructure, and film thickness. The findings show the presence of hexagonal close‐packed metallic Co whose content increases in the films deposited on Al 2 O 3 and Si substrates relatively to SiO 2 substrate. The potential of the films as model nano‐catalysts has been evaluated in the context of the Fischer‐Tropsch (FT) process. Fuel fractions, which have been observed in FT liquid products, are rich in diesel and waxes. Specifically, Co‐ZnO/Al 2 O 3 nano‐catalyst shows a selectivity of ∼4%, 31%, and 65% towards gasoline, diesel, and waxes, respectively, while Co‐ZnO/SiO 2 nano‐catalyst shows a selectivity of ∼12%, 51%, and 37%, for gasoline, diesel, and waxes, respectively. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3332–3340, 2018

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)0.001
metaresearch head score (Gemma)0.000
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 score0.028
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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