Investigation of halloysite thermal decomposition through differential thermal analysis (DTA): Mechanism and kinetics assessment DOI

A. Raghdi,

M. Heraiz,

Mohammed Rasheed

et al.

Journal of the Indian Chemical Society, Journal Year: 2024, Volume and Issue: unknown, P. 101413 - 101413

Published: Oct. 1, 2024

Language: Английский

Ancient materials for new applications: The combination of montmorillonite with polysaccharides for biomedical uses DOI
Lucilane Gomes Oliveira, Denise B. França, Josy A. Osajima

et al.

Applied Clay Science, Journal Year: 2025, Volume and Issue: 266, P. 107688 - 107688

Published: Jan. 5, 2025

Language: Английский

Citations

0

Development of microstructure-rheological and electrical properties relationship in PS/POE/HNTs blend nanocomposites using machine learning DOI Creative Commons
Sara Estaji,

Homa Akbari,

Mohammad Iman Tayouri

et al.

Polymer Testing, Journal Year: 2024, Volume and Issue: 137, P. 108503 - 108503

Published: June 22, 2024

Halloysite nanotubes (HNTs) and polypropylene-grafted maleic anhydride (PP-g-MA) were studied for their effects in blends of polystyrene (PS) polyolefin elastomer (POE). The method used to prepare PS/POE (90/10 80/20 wt/wt) containing 1, 3, 5 phr HNTs with or without PP-g-MA (a compatibilizer) was melt blending. Structural morphological studies using X-ray diffraction analysis (XRD), scanning electron microscopy assisted energy dispersive spectroscopy (SEM-EDS), transmission (TEM) confirmed a matrix-droplet morphology the sample compatibilizer has better microstructure than other formulations. presence both together been discovered improve viscoelastic properties solid, as evidenced by increased storage modulus complex viscosity. A notable change occurred rheological behavior HNTs. dependence zero-shear viscosity on loading (0 phr) approximated polynomial curve fitting experimental data Carreau-Yasuda model. Computational fluid dynamics (CFD) simulations also study changes flow patterns shear rates. calculated effective viscosities at given rate (0.05 1/s) qualitative agreement results. Moreover, we utilized various machine-learning techniques predict nanocomposites. results showed that Extreme Gradient Boosting (XGBoost) outperformed predictive models based evaluation metrics. Four-point probe measurements found samples HNT had lowest conductivities due aggregated structures. However, homogeneous distribution led sudden rise conductivity PP-g-MA. Computer modeling uniform non-uniform distributions decreased considerably compared distribution.

Language: Английский

Citations

2

Investigation of halloysite thermal decomposition through differential thermal analysis (DTA): Mechanism and kinetics assessment DOI

A. Raghdi,

M. Heraiz,

Mohammed Rasheed

et al.

Journal of the Indian Chemical Society, Journal Year: 2024, Volume and Issue: unknown, P. 101413 - 101413

Published: Oct. 1, 2024

Language: Английский

Citations

0