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Sonochemical synthesis of zeolite NaP from clinoptilolite

2015· article· en· W1579374481 on OpenAlexaff
Jamshid Behin, Hossein Kazemian, Sohrab Rohani

Bibliographic record

VenueUltrasonics Sonochemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Synthesis and Characterization
Canadian institutionsWestern University
Fundersnot available
KeywordsZeoliteClinoptiloliteCrystallinitySonicationMaterials scienceCrystallizationChemical engineeringHydrothermal synthesisCrystalliteAutoclaveHydrothermal circulationMineralogyParticle sizeChemistryMetallurgyComposite materialOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

In the present work, natural clinoptilolite was converted to zeolite NaP using ultrasonic energy, in which the transformation time shortened remarkably. The effect of post-synthesis treatment using conventional hydrothermal was also investigated. The synthesized powders were characterized by XRD, TGA/DTA, SEM, and PSD analysis. The results showed that, increasing the sonication time (energy) has no significant effect on the product's morphology. The crystallinity of the synthesized samples increased slightly with increasing sonication time, but their yield remained relatively unchanged. Furthermore, post-synthesis hydrothermal treatment showed very little influence on properties of the final product. Because the ultrasonic irradiation creates acoustic cavitation cracks on the surface structure of clinoptilolite particulates and increases the concentration of soluble alumino-silicate species, which favors the prevailing super-saturation, crystallization and crystal growth of zeolite NaP happen faster. The particles of zeolite NaP synthesized by ultrasonic irradiation consist of small crystallites of uniform size.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.213
Teacher spread0.200 · 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

Citations56
Published2015
Admission routes1
Has abstractno

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