Browsing by Subject "Nanosensors"

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    Curriculum innovations through advancement of MEMS/NEMS and wearable devices technologies
    (2017) Shayesteh, S.; Rizkalla, M.E.; El-Sharkawy, M.; Electrical and Computer Engineering, School of Engineering and Technology
    State of the art technologies using both micro- and nano-electromechanical systems (MEMS and NEMS) and wearable and Internet of Things (IoT) devices have impacted our daily lives in applications including wearable devices and sensor technology as applied to renewable energies and health sciences, among others. Several examples are device implants, optical devices, micro and nanomachining, embedded systems and integrated nano sensor systems. The recent Electrical and Computer Engineering (ECE) and Mechanical Engineering (ME) curricula lacked inclusion of these elements within their programs. Close scrutiny to the need of local industry from engineering graduates has emphasized the motivation to develop these materials into the engineering curricula. Within the ECE curriculum, a new senior course was developed to cover MEMS/NEMS devices as well as wearable and IoT devices with Bluetooth and wireless features. The MEMS/NEMS module of the new course integrates software CAD tools and hardware implementations. It is a project-based course where students learn software for the device process, then fabricate the device in the school laboratories. The wearable and IoT devices module introduces the students to Wearable and Internet of Things systems. It covers sensors and sensor fusion, embedded processors, tools for wearable and IoT applications, and design using Bluetooth and wireless IoT systems. The new course development objectives are hands-on practice, and preparation of senior students for industrial and research careers. In addition, an introductory MEMS topic section is added in the sophomore level electrical engineering course offered to mechanical engineering students. It introduces MEMS devices employed as energy conversion devices. Based on our recent feedback, the students have favorably accepted this MEMS addition to the course. This paper details the software and hardware development elements of the new course. It also presents the assessment data for students' satisfaction for both the electrical and computer engineering (ECE), and mechanical engineering (ME) students. © American Society for Engineering Education, 2017.
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    Gaussian Process Regression and Monte Carlo Simulation to Determine VOC Biomarker Concentrations Via Chemiresistive Gas Nanosensors
    (IEEE Xplore, 2021-06) Rivera, Paula Angarita; Woollam, Mark; Siegel, Amanda P.; Agarwal, Mangilal; Mechanical and Energy Engineering, School of Engineering and Technology
    Utilizing chemiresistive gas sensors for volatile organic compound (VOC) detection has been a growing area of investigation in the last decade. VOCs have been extensively studied as potential biomarkers for biomedical applications as they are byproducts of metabolic pathways which are dysregulated by disease. Therefore, sensor arrays have been fabricated in previous studies to detect VOC biomarkers. In the process of testing these sensors, it is highly advantageous to quantify the concentration of the VOC biomarkers with high accuracy to diagnose the disease with high sensitivity and specificity. To investigate, analyze, and understand the relation between the concentrations of the VOC to the sensor resistance response, Gaussian Process (GP) models were implemented to predict the behavior of the data with respect to the resistance when the sensor is exposed to a range of concentrations of VOCs. Additionally, the relation between the concentration and resistance of the sensor was studied to predict the concentration of the VOC when a resistance is obtained. Monte Carlo Simulation Sampling from the GP model was utilized to generate data to further understand the trend. The results demonstrated that the relation between the concentration and resistance is linear. The model was tested with sampling data and its accuracy was evaluated.