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Record W2794045761 · doi:10.1002/cphc.201800021

Copper Affects the Location of Zinc in Bimetallic Ion‐Exchanged Mordenite

2018· article· en· W2794045761 on OpenAlexafffund
Allen A.C. Reule, Jing Shen, Natalia Semagina

Bibliographic record

VenueChemPhysChem · 2018
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsUniversity of Alberta
FundersCanada Foundation for InnovationEnerkem
KeywordsBimetallic stripMordeniteCopperChemistryInorganic chemistryZincIon exchangeZeoliteCatalysisX-ray photoelectron spectroscopyFerrieriteTemperature-programmed reductionIonChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Bimetallic ion exchange on a zeolite often impacts its catalytic properties compared to its monometallic counterparts. Here, we address the synergistic effect of simultaneous copper and zinc ion exchange on mordenite (MOR), as found earlier for dimethyl ether (DME) carbonylation. Samples with various Cu/Zn ratios were characterized by diffuse‐reflectance infrared Fourier‐transform spectroscopy (DRIFTS) in the 3600 and 720 cm −1 regions, pore distribution analysis through Ar physisorption, X‐ray photoelectron spectroscopy (XPS), temperature‐programmed reduction (TPR), and transmission electron microscopy (TEM). When ion‐exchanged alone, copper preferentially occupies 12‐membered rings, whereas zinc occupies 8‐membered rings. In bimetallic combinations, the zinc addition was found to prevent the copper from sintering into nanoparticles and to increase its coordination strength to the zeolite. At a Cu/Zn ratio of 0.25 (for MOR with Si/Al=6.5), copper promotes zinc ion exchange into 12‐membered rings, more specifically, into T4 sites that are known for the formation of the coke precursor in DME carbonylation on a MOR. The sites became blocked during the bimetallic ion exchange, leading to suppressed catalyst deactivation. The study contributes to the understanding of mutual ion effects in bimetallic exchanged zeolites and highlights the major role of copper as a governing factor in determining the location of co‐exchanged zinc on a MOR.

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 score0.021
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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
Published2018
Admission routes2
Has abstractyes

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