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Record W2533379678 · doi:10.1021/acs.jpcc.6b09428

New Insights into Formation of Molecular Sieve SAPO-34 for MTO Reactions

2016· article· en· W2533379678 on OpenAlexafffund
Lu Zhang, Yining Huang

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTriclinic crystal systemMolecular sieveCrystallinityCrystallizationSelectivityChemistryCrystallographyZeoliteProduct distributionChemical engineeringPhase (matter)CatalysisOrganic chemistryCrystal structure

Abstract

fetched live from OpenAlex

Small-pore molecular sieve SAPO-34 is well-known as the most promising and effective catalyst for the conversion of methanol to olefins (MTO). In this paper, we have investigated the formation of SAPO-34 under different dry gel conversion (DGC) conditions using two types of synthesis gels with one containing hydrofluoric acid (HF) and the other not. Particular attention was paid to the Si incorporation and distribution in final SAPO-34 products. The results indicate that under DGC (with HF) conditions SAPO-34 is formed from the transformation of a highly crystalline, layered prephase that is held by covalent bonds. The final SAPO-34 product is a mixture of triclinic and trigonal phases containing six framework Si species. The Si distribution in triclinic and trigonal SAPO-34 is drastically different. The majority of Si species are located in the aluminosilicate domains in trigonal phase. Conversely, under DGC (without HF) conditions, the crystallization of SAPO-34 involves three transformational stages. Initially, a layered, crystalline intermediate forms and then transforms to a semicrystalline phase. The structures of both intermediates are held by weak noncovalent bonding interactions. The final SAPO-34 product is in pure trigonal form and contains two major Si species. Despite the difference in Si distribution, the SAPO-34 products prepared by using different gels have similar bulk Si contents. MTO reaction tests show that before 50 min of time-on-stream the DGC (with HF) product gives lower ethylene and propylene selectivity but higher propane selectivity than the DGC (without HF) product. After 50 min on stream, similar light olefin and propane selectivities are observed over the two SAPO-34 samples.

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 score0.011
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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