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Record W4406832805 · doi:10.1007/s11244-025-02051-w

AuPd/TiO2 Catalysts in the CO Oxidation: Insights into the Synthesis Procedure and In-situ Spectroscopy Studies

2025· article· en· W4406832805 on OpenAlexfundno aff
Daniel G. Araiza, M. Fernández, Mariana Sánchez-Ugalde, Dora Alicia Solís-Casados, Viridiana Maturano-Rojas, Rodolfo Zanella

Bibliographic record

VenueTopics in Catalysis · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoCanadian Institute for Theoretical Astrophysics
KeywordsCatalysisIn situSpectroscopyChemistryMaterials scienceInorganic chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract A series of bimetallic AuPd/TiO 2 catalysts (Au/Pd = 1) were prepared through either the impregnation or the deposition-precipitation in urea (DPU) approach and tested in the CO oxidation reaction. Among these, the sample synthesized through a sequential impregnation (Pd) followed by DPU (Au), with an intermediate thermal treatment in air, presented a remarkable CO conversion at sub-ambient temperatures and an enhanced catalytic stability, compared to the monometallic samples. The ex-situ characterization revealed that the synthesis procedure led to the formation of well-dispersed bimetallic AuPd nanoparticles over the TiO 2 support. The in-situ characterization helped to propose that bimetallic NPs were composed of an intermetallic Au-Pd phase with both atoms available in the surface. Both in-situ FTIR and UV-vis spectroscopies helped to recognize the active sites during the reaction: Au in close interaction with the TiO 2 at low temperatures, and step/edges Pd sites at high temperatures. Finally, the pivotal role of the TiO 2 reducibility in the CO oxidation reaction, promoted by the bimetallic AuPd NPs, was determined through in-situ Raman spectroscopy.

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)0.002
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 score0.070
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.315
Teacher spread0.298 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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