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Record W2047551515 · doi:10.1139/v04-017

The catalytic effect of boron substitution in MCM-41-type molecular sieves

2004· article· en· W2047551515 on OpenAlexvenueno aff
I. Eswaramoorthi, N. Lingappan

Bibliographic record

VenueCanadian Journal of Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsIsomerizationBoronChemistryCatalysisCalcinationMolecular sieveHexene1-HexenePyridineAdsorptionNuclear chemistrySelectivityInorganic chemistryEthyleneOrganic chemistry

Abstract

fetched live from OpenAlex

A series of B-MCM-41 samples has been synthesized with a wide range of boron content (SiO 2 :B 2 O 3 ratio from 20 to 200), using ethyl silicate ester-40 (ES-40) as the silica source and characterized by XRD, BET, FT-IR, 11 B-MAS NMR, SEM, pyridine adsorption, TPDA, and chemical analysis. The interplanar d 100 spacing varies from 40 to 45 Å, depending on the Si:B ratio. On calcination, a significant amount of four-coordinated boron is converted into less stable three-coordinated boron, and some boron is removed from the framework. The degree of deboronation increases with an increase of boron content of the sample. The B substitution in the MCM-41 framework results in only weak and mild acid sites. The isomerization of 1-hexene is found to be influenced by the boron content in the framework. The isomerization leads to both a hydrogen shift and skeletal rearrangement. The selectivity ratios of cis-2-hexene to trans-2-hexene and 2-hexene to 3-hexene were found to decrease with an increase of temperature and a decrease of the SiO 2 :B 2 O 3 ratio of the catalysts. Skeletal isomerization starts at 250 °C, forming secondary products, and increases with an increase of temperature and an increase of boron content of the catalysts. Key words: ES-40, B-MCM-41, deboronation, catalyst characterization, XRD, BET, FT-IR, 11 B-MAS NMR, SEM, 1-hexene isomerization, 2-hexene.

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 score0.008
Threshold uncertainty score0.434

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 science0.0000.000
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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

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