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Record W4206131550 · doi:10.1002/cjce.24370

Stability studies of <scp>PtSn</scp> structured catalysts supported on thin layers of <scp> MAl <sub>2</sub> O <sub>4</sub> </scp> ( <scp>M: Mg, or Zn</scp> ) for paraffins dehydrogenation reactions

2022· article· en· W4206131550 on OpenAlexvenueno aff
Adriana Ballarini, Patricia D. Zgolicz, Sergio de Miguel, Sonia Bocanegra

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsnot available
FundersUniversidad Nacional del LitoralConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsCatalysisDehydrogenationBimetallic stripX-ray photoelectron spectroscopyButaneMaterials scienceChemical engineeringMetalDispersion (optics)ChemistryInorganic chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In order to determine the catalytic stability, PtSn structured catalysts supported on thin films of MgAl 2 O 4 or ZnAl 2 O 4 deposited on α‐Al 2 O 3 spheres were studied through five reaction‐regeneration cycles in the n ‐butane dehydrogenation. Bimetallic catalysts show good catalytic stability along the five reaction‐regeneration cycles. Among the different synthesized catalysts, the PtSn/Sp‐Zn‐CN catalyst showed the best catalytic behaviour, showing very good values of yields to butenes and excellent catalytic stability along the cycles, even better than the one of a structured commercial catalyst used industrially for the dehydrogenation of paraffins. The characterization results by temperature‐programmed reduction and X‐ray photoelectron spectroscopy showed some modifications in the metallic phase of the catalysts after the cycles, mainly in the Sn/Pt surface ratios and in segregation effects in some catalysts. However, the transmission electron microscopy (TEM) results are conclusive in the sense that, after the cycles, the bimetallic catalysts maintained a very high proportion of particles with sizes between 1–2 nm, and therefore, preserved a high metallic dispersion.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.235
Teacher spread0.213 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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