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Record W2037235356 · doi:10.1007/s13204-014-0370-x

Synthesis of nanosized ZSM-5 zeolite using extracted silica from rice husk without adding any alumina source

2014· article· en· W2037235356 on OpenAlexaff
Zahra Ghasemi, Habibollah Younesi, Hossein Kazemian

Bibliographic record

VenueApplied Nanoscience · 2014
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsWestern University
FundersTarbiat Modares UniversityIran Nanotechnology Initiative CouncilSanten
KeywordsAnalcimeHuskZeoliteMaterials scienceCrystallizationCrystallinityChemical engineeringNanochemistryScanning electron microscopeZSM-5Fourier transform infrared spectroscopyAmorphous solidRaw materialMineralogyCatalysisNanotechnologyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The synthesis of analcime and nanosized ZSM-5 zeolites was carried out by a hydrothermal method with silica extracted from rice husk, available as an inexpensive local biowaste, and without the use of an extra alumina source. Amorphous silica (with 88 wt% of SiO2) was extracted from rice husk ash by a suitable alkali solution. The effects of crystallization temperature, time and SiO2/Al2O3 ratio of the initial system on the properties of final products were investigated. For the characterization of the synthesized product, X-ray diffraction, scanning electron microscope, energy dispersive X-ray techniques, Fourier transform infrared and Brunauer-Emmett-Teller method were applied. Crystallinity percentages of analcime and nanosized ZSM-5 were 95.86 and 89.56, respectively, with specific surface area of 353.5 m2 g−1 for ZSM-5. The experimental results revealed that the synthesis of analcime and nanosized ZSM-5 zeolites was more practical with using silica extracted from inexpensive raw materials, while the whole crystallization process was accomplished without adding any alumina source during.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations57
Published2014
Admission routes1
Has abstractyes

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