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

Facile and Rapid Synthesis Method of Dielectric Nanocomposite for Simultaneous Enhancement of Electromagnetic Wave Absorbing/Shielding Characteristics DOI
Yunhe Zou, Shudong Li, Ali Hassan

et al.

Colloids and Surfaces A Physicochemical and Engineering Aspects, Journal Year: 2024, Volume and Issue: 694, P. 134158 - 134158

Published: May 3, 2024

Language: Английский

Citations

2
Achieving high-efficiency electromagnetic wave absorption performance in a 3-layer hierarchical porous magneto-dielectric nanocomposite DOI
Sagr Alamri

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: 140, P. 330 - 342

Published: May 29, 2024

Language: Английский

Citations

2
Facile and scalable preparation of gradient multilayer nanocomposite structure with preeminent microwave absorption and radar cross section reduction capability DOI
Ali A. Rajhi

Ceramics International, Journal Year: 2024, Volume and Issue: 50(21), P. 43044 - 43053

Published: Aug. 9, 2024

Language: Английский

Citations

2
Characterization and ballistic performance of hybrid jute and aramid reinforcing graphite nanoplatelets in high-density polyethylene nanocomposites DOI Creative Commons
Ulisses Oliveira Costa, Fábio da Costa Garcia Filho, T. Gómez-del Rı́o

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 28, P. 1570 - 1583

Published: Dec. 14, 2023

Hybrid nanocomposites have emerged as a promising solution for engineering applications associated with reduced costs and weight aiming to enhance performance in special areas such ballistic protection. This study delves into the development of hybrid featuring high-density polyethylene matrix modified graphite nanoplatelets reinforced by aramid jute fabrics. The incorporation GNP significantly influences crystalline structure GNP/HDPE matrix, evidenced Raman X-ray diffraction analyses. Furthermore, it assumes an important role modifying crystallization glass transition temperatures (Tc Tg) influencing dynamic mechanical behavior. Specifically, increases viscoelastic stiffness, raises storage modulus more than 30 %, reduces tanδ value. In addition, replacing 5 layers fabric equivalent number maintains comparable 20-layer nanocomposite. substitution yields remarkable 659.41 J absorbed energy limit velocity 405.72 m/s. Additionally, nanocomposite comprising 10 each showcases impressive resistance against 9 mm caliber ammunition, achieving 419.84 320.13 Scanning electron microscopy analysis exposes intricate fracture mechanisms, encompassing phenomena crazing, fibrillation, fiber rupture, debonding. These mechanisms are significant composite's absorption during projectile impact. Moreover, cost-benefit underscores potential protection simultaneously reducing both helmet cost 7 % 40 respectively.

Language: Английский

Citations

5
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

1