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Record W2005654554 · doi:10.1002/cjce.20288

Effect of method of preparation on activity of Pd/Al<sub>2</sub>O<sub>3</sub> monolith catalysts

2010· article· en· W2005654554 on OpenAlexvenueno aff
Aswani K. Mogalicherla, Deepak Kunzru

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersIndian Institute of Technology Kanpur
KeywordsSlurryCatalysisMaterials scienceMonolithDispersion (optics)StyrenePalladiumScanning electron microscopeChemical engineeringMetalComposite materialMetallurgyChemistryCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Pd/Al 2 O 3 monolithic catalyst of different washcoat thicknesses were prepared by two methods and tested for the activity of hydrogenation of α‐methyl styrene. These catalysts were prepared by two methods; either the palladium was impregnated on γ‐alumina and this Pd/Al 2 O 3 powder was used to prepare the slurry for washcoating (Cat 1) or γ‐alumina washcoating was followed by impregnation of palladium (Cat 2). The effect of slurry concentration, pH of the slurry, and addition of binders on the catalyst properties was investigated. The monolithic catalysts were characterised by determination of metal dispersion, surface area, scanning electron microscopy, and weight loss of washcoat during ultrasonication. Well‐adhered washcoats were obtained with slurry prepared using milled γ‐alumina, whereas the adhesion of the washcoat prepared using Pd/Al 2 O 3 powders was very poor. Addition of binders significantly improved the adhesion of the washcoats prepared from Pd/Al 2 O 3 . Metal dispersion for Cat 2 decreased with washcoat loading but did not change with loading for Cat 1. The activity tests were conducted at different washcoat loadings and the productivity of the monolithic catalyst prepared in both methods has been compared.

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 score0.072
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.219
Teacher spread0.215 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

Explore more

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