MétaCan
Menu
Back to cohort
Record W2093424804 · doi:10.2202/1542-6580.1134

Influence of the Preparation Method and Metal Precursor Compound on Alumina-Supported Pd Catalysts

2004· article· en· W2093424804 on OpenAlexaff
Gabriela Marta Tonetto, Daniel E. Damiani

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2004
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsWestern University
FundersUniversidad Nacional del Sur
KeywordsCatalysisIncipient wetness impregnationPalladiumTemperature-programmed reductionInorganic chemistryChemisorptionMetalChemistryStoichiometryHeterogeneous catalysisMaterials scienceNuclear chemistryOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

Supported palladium catalysts were prepared by two different methods: wet impregnation and incipient-wetness impregnation. Commercial gamma-Al2O3 and sol-gel derived alumina were used as support. The synthesis of alumina xerogel was carried out by hydrolysis of aluminum isopropoxide (AIP) in 2-propanol solution. Organometallic -Pd(C5H7O2)2- and inorganic precursor -Pd(NH3)4Cl2.H2O- were used for the synthesis of the catalytic systems.Samples were characterized by BET surface area measurement, atomic absorption spectroscopy (AAS), X-ray diffraction (XRD), temperature-programmed reduction (TPR), and hydrogen chemisorption. The activity of these catalysts was studied for the stoichiometric reduction of NO by methane. The decomposition of NO and methane oxidation were also used as a test reaction. Catalyst prepared by wet impregnation method using Pd(C5H7O2)2 and alumina xerogel presented the best performance for the studied reaction. The activity of Pd/alumina catalysts for NO-CH4 reaction is related to the facility for reduction/oxidation of the metal. This property is affected by the nature of the support as well as the metal precursor.

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 score0.009
Threshold uncertainty score0.322

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 communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.281
Teacher spread0.273 · 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
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Chemical Reactor EngineeringSame topicCatalytic Processes in Materials ScienceFrench-language works237,207