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Effects of Co Particle Size on the Stability of Co/Al<sub>2</sub>O<sub>3</sub> and Re–Co/Al<sub>2</sub>O<sub>3</sub> Catalysts in a Slurry-Phase Fischer-Tropsch Reactor

2016· article· ar· W2521088471 on OpenAlexafffund
Pooneh Ghasvareh, Kevin J. Smith

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languagear
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisFischer–Tropsch processParticle sizeCarbon fibersDeposition (geology)Particle (ecology)ChemistryVolumetric flow rateChemical engineeringCarbon monoxideMaterials scienceAnalytical Chemistry (journal)SelectivityPhysical chemistryOrganic chemistryThermodynamicsComposite material

Abstract

fetched live from OpenAlex

The stability of a series of Co/Al 2 O 3 and Re–Co/Al 2 O 3 Fischer–Tropsch (FT) catalysts, with varying Co particle size, was measured in a continuous flow, stirred tank reactor operated at 220 °C, 2.1 MPa and with a H 2 /CO = 2/1 synthesis gas for periods up to 190 h time-on-stream (TOS). Results showed that catalyst stability was dependent upon the Co particle size, the degree-of-reduction (DOR) of the catalyst precursor, and the CO conversion. At the chosen operating conditions, carbon deposition was the main cause of catalyst deactivation and the initial rate of carbon deposition per active Co site increased with increased Co particle size ( d Co = 2–22 nm) when measured at approximately the same CO conversion level. On the 15 wt % Co/Al 2 O 3 catalyst the initial rate of carbon deposition increased with CO conversion (CO conversion ≤40%) whereas, on the 1.2 wt %Re-12 wt %Co/Al 2 O 3 catalyst, the initial rate of carbon deposition decreased with increased CO conversion (CO conversions >60%) due to high concentrations of H 2 O and CO 2 in the reactor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.242
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2016
Admission routes2
Has abstractyes

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