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962 results on '"Naguib, Hani E."'

7. Development and modeling of an ultra-robust TPU-MWCNT foam with high flexibility and compressibility

Author

Ghahramani Pardis, Behdinan Kamran, Moradi-Dastjerdi Rasool, and Naguib Hani E.

Subjects
chemical blowing agent, experimental characterization, theoretical modeling, elastic modulus, tpu/mwcnt nanocomposite, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

Developing a cost-effective industrially scalable manufacturing method that can improve the mechanical properties of nanocomposite foams with higher flexibility, compressibility, and, at the same time, mechanically robustness is of significant interest. In this study, porous thermoplastic polyurethane (TPU)/multiwalled carbon nanotube (MWCNT) was fabricated with the chemical blowing agent (CBA) by a combination of compounding-compression molding methods. The effects of CBA and MWCNT contents on the foam morphology, porosity, foam cell size, Young’s modulus, and compressibility of fabricated samples were investigated. Through conducting cyclic compressive tests, it was observed that nanocomposite foams exhibited consistent mechanical responses across multiple compressive cycles and demonstrated notable characteristics, including high compressibility (up to 76.4% compressive strain) and high elastic modulus (up to 8.8 ± 2.6 MPa). Moreover, theoretical approaches were employed to predict the elastic modulus of solid and foam TPU/MWCNT. For solid MWCNT/TPU, a specific micromechanical model based on different modifications of the Halpin-Tsai (HT) approach was used, which showed a good agreement with experimental data at different MWCNT contents. Furthermore, the constant parameters of Gibson and Ashby’s method were found to successfully predict the elastic modulus of foam TPU/MWCNT at different MWCNT and CBA percentages.

Published
2024
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9. Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modes

Author

Effati, Meysam, Sun, Yu-Chen, Naguib, Hani E., and Nejat, Goldie

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The COVID-19 pandemic is one of the most challenging healthcare crises during the 21st century. As the virus continues to spread on a global scale, the majority of efforts have been on the development of vaccines and the mass immunization of the public. While the daily case numbers were following a decreasing trend, the emergent of new virus mutations and variants still pose a significant threat. As economies start recovering and societies start opening up with people going back into office buildings, schools, and malls, we still need to have the ability to detect and minimize the spread of COVID-19. Individuals with COVID-19 may show multiple symptoms such as cough, fever, and shortness of breath. Many of the existing detection techniques focus on symptoms having the same equal importance. However, it has been shown that some symptoms are more prevalent than others. In this paper, we present a multimodal method to predict COVID-19 by incorporating existing deep learning classifiers using convolutional neural networks and our novel probability-based weighting function that considers the prevalence of each symptom. The experiments were performed on an existing dataset with respect to the three considered modes of coughs, fever, and shortness of breath. The results show considerable improvements in the detection of COVID-19 using our weighting function when compared to an equal weighting function., Comment: 5 Pages, 1 Figure, To appear in The 7th International Conference on Wireless and Mobile Computing, Networking and Communication (IEEE eHPWAS - 17th IEEE WiMob - rank B), 2021

Published
2021

39. Enhancing Mechanical Performance of High-Density Polyethylene at Different Environmental Conditions with Outstanding Foamability through In-Situ Rubber Nanofibrillation: Exploring the Impact of Interface Modification

Author

Kheradmandkeysomi, Mohamad, primary, Salehi, Amirmehdi, additional, Jalali, Amirjalal, additional, Omranpour, Hosseinali, additional, Tafreshi, Omid Aghababaei, additional, Naguib, Hani E., additional, and Park, Chul B., additional

Published
2024
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40. Theoretical and experimental investigation of MWCNT dispersion effect on the elastic modulus of flexible PDMS/MWCNT nanocomposites

Author

Ghahramani Pardis, Behdinan Kamran, Moradi-Dastjerdi Rasool, and Naguib Hani E.

Subjects
flexible pdms-mwcnt nanocomposite, young’s modulus, compression test, theoretical approaches, cnt agglomeration, cnt aspect ratio, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

In this article, Young’s modulus of a flexible piezoresistive nanocomposite made of a certain amount of multiwalled carbon nanotube (MWCNT) contents dispersed in polydimethylsiloxane (PDMS) has been investigated using theoretical and experimental approaches. The PDMS/MWCNT nanocomposites with the governing factor of MWCNT weight fraction (e.g., 0.1, 0.25, and 0.5 wt%) were synthesized by the solution casting fabrication method. The nanocomposite samples were subjected to a standard compression test to measure their elastic modulus using Instron Universal testing machine under force control displacement mode. Due to the costs and limitations of experimental tests, theoretical predictions on the elasticity modulus of such flexible nanocomposites have also been performed using Eshelby–Mori–Tanaka (EMT) and Halpin–Tsai (HT) approaches. The theoretical results showed that HT’s approach at lower MWCNT contents and EMT’s approach at higher MWCNT contents have a better agreement to experimental results in predicting the elastic modulus of PDMS/MWCNT nanocomposites. The experimental results indicated that the inclusion of MWCNT in the PDMS matrix resulted in a noticeable improvement in Young’s modulus of PDMS/MWCNT nanocomposite at small values of MWCNT contents (up to w f = 0.25%); however, exceeding this nanofiller content did not elevate Young’s modulus due to the emergence of MWCNT agglomerations in the nanocomposite structure.

Published
2021
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46. Piezoresistive Sensors Array for Multijoint Motion Estimation Application

Author

Cen, Zinan, Robinson, Fraser, Nejat, Goldie, and Naguib, Hani E.

Abstract

With the emergence of digital healthcare, comes the need for an unobtrusive method for long-term motion monitoring. In recent years, wearable sensors have been utilized for motion monitoring to replace the conventional camera-based systems. Despite several attempts at measuring joint angles, designs for affordable and low power-consuming systems were lacking. This article explored the usage of ten low-cost, energy-efficient conductive polymer composite-based strain sensors composed of thermoplastic polyurethane elastomer matrix and multiwalled carbon nanotube (CNT) to create a smart clothing system for the measurement of elbow and shoulder joint angles. To overcome the time-varying and nonlinear behavior of the proposed strain sensor, a novel architecture of a convolutional neural network was designed to enhance the mapping of sensor signals to joint angles by extracting inter-sensor spatial and temporal information. Strain sensors with different concentrations were fabricated and characterized. It was found that 4 wt% CNT produced the highest sensitivity due to the highest degree of macrostructural damage. Motion monitoring performance was evaluated on one volunteer performing different actions and overall normalized root mean squared errors for elbow angle and shoulder Euler angles were 6.77%, 7.19%, 6.31%, and 8.22%, respectively.

Published
2024
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48. Conductive Bio‐based Hydrogel for Wearable Electrodes via Direct Ink Writing on Skin.

Author

Chen, Jia Xi Mary, Chen, Tianhao, Zhang, Yixin, Fang, Weiqing, Li, Wenxuan Evelyn, Li, Terek, Popovic, Milos R., and Naguib, Hani E.

Subjects
SILVER chloride, 3-D printers, GELATION, HUMAN body, HYDROGELS, ELECTRIC stimulation
Abstract

Non‐invasive electrodes for recording and delivering electric signals to the human body are crucial for health monitoring and rehabilitation applications. However, high‐fidelity signal recording or delivery with epidermal electrodes remains a challenge due to the need for shape customization, to account for the variance of body morphology among individuals, and the need for conformal contact, to accommodate creviced skin surfaces, intricate curves, and moving bodies. In this study, a conductive and self‐adhesive hydrogel for direct ink writing of wearable electrodes on the skin is presented, utilizing physical cross‐linking mechanisms between bio‐based polymers. With a fast gelation time and a facile fabrication method, the printed hydrogel achieves a 0.40 mm resolution via handheld 3D printers. Compared with silver/silver chloride (Ag/AgCl) coated gel electrode standards, the hydrogel electrode formed in situ achieves a higher signal‐to‐noise ratio by 88%, for the monitoring of forearm muscle biopotential and decreases the required current from 3.5 to 2.25 mA, for the functional electrical stimulation for eye closure. The lowered contact impedance of the hydrogel electrode is attributed to its sol–gel transition in situ on the skin, demonstrating its potential to enable future healthcare applications with improved personalization, efficiency, and comfort. [ABSTRACT FROM AUTHOR]

Published
2024
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