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Record W3011201166 · doi:10.3390/catal10030316

Ru-Promoted Ni/Al2O3 Fluidized Catalyst for Biomass Gasification

2020· article· en· W3011201166 on OpenAlexafffund
Alán Rubén Calzada Hernandez, Daniel Gibran González Castañeda, Adriana Sánchez Enríquez, Hugo de Lasa, Benito Serrano

Bibliographic record

VenueCatalysts · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsCatalysisCokeFluidized bedMaterials scienceChemical engineeringNickelDesorptionBET theoryTemperature-programmed reductionChemisorptionChemistryAdsorptionMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Fluidizable catalysts based on Ni/Al2O3 with added Ru were used for the gasification of a lignin surrogate (2-methoxy-4-methylphenol) in a fluidized CREC Riser Simulator reactor. This was done in order to quantify lignin surrogate conversion and lignin surrogate products (H2, CO, CO2 and CH4) as well as the coke deposited on the catalyst. The catalysts that were evaluated contained 5% wt. Ni with various Ru loadings (0.25%, 0.5% and 1% wt). These catalysts were synthesized using an incipient Ni and Ru co-impregnation. Catalysts were characterized using XRD, N2 adsorption-desorption (BET Surface Area, BJH), Temperature Programmed Reduction (TPR), Temperature Programmed Desorption (TPD) and H2 chemisorption. Catalytic steam gasification took place at 550, 600 and 650 °C using 0.5, 1.0 and 1.5, steam/biomass ratios. The results obtained showed that Ru addition helped to decrease both nickel crystallite site sizes and catalyst acid site density. Moreover, it was observed that coke on the catalyst was reduced by 60%. This was the case when compared to the runs with the Ni/Al2O3 free of Ru.

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)0.000
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 score0.010
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.219
Teacher spread0.198 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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