Genevieve Dion
Drexel University
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Genevieve Dion.
Energy and Environmental Science | 2011
Kristy Jost; Carlos R. Perez; John K. McDonough; Volker Presser; Min Heon; Genevieve Dion; Yury Gogotsi
This paper describes a flexible and lightweight fabric supercapacitor electrode as a possible energy source in smart garments. We examined the electrochemical behavior of porous carbon materials impregnated into woven cotton and polyester fabrics using a traditional printmaking technique (screen printing). The porous structure of such fabrics makes them attractive for supercapacitor applications that need porous films for ion transfer between electrodes. We used cyclic voltammetry, galvanostatic cycling and electrochemical impedance spectroscopy to study the capacitive behaviour of carbon materials using nontoxic aqueous electrolytes including sodium sulfate and lithium sulfate. Electrodes coated with activated carbon (YP17) and tested at ∼0.25 A·g−1 achieved a high gravimetric and areal capacitance, an average of 85 F·g−1 on cotton lawn and polyester microfiber, both corresponding to ∼0.43 F·cm−2.
Energy and Environmental Science | 2013
Kristy Jost; Daniel Stenger; Carlos R. Perez; John K. McDonough; Keryn Lian; Yury Gogotsi; Genevieve Dion
The field of energy textiles is growing but continues to face two main challenges: (1) flexible energy storage does not yet exist in a form that is directly comparable with everyday fabrics including their feel, drape and thickness, and (2) in order to produce an “energy textile” as part of a garment, it must be fabricated in a systematic manner allowing for multiple components of e-textiles to be integrated simultaneously. To help address these issues, we have developed textile supercapacitors based on knitted carbon fibers and activated carbon ink. We show capacitances as high as 0.51 F cm−2 per device at 10 mV s−1, which is directly comparable with those of standard activated carbon film electrodes tested under the same conditions. We also demonstrate the performance of the device when bent at 90°, 135°, 180° and when stretched. This is the first report on knitting as a fabrication technique for integrated energy storage devices.
Journal of Materials Chemistry | 2014
Kristy Jost; Genevieve Dion; Yury Gogotsi
Research on flexible and wearable electronics has been gaining momentum in recent years, ranging in use from medical to military and everyday consumer applications. Yet to date, textile electronics still lack integrated energy storage solutions. This paper provides an overview and perspective on the field of textile energy storage with a specific emphasis on devices made from textiles or made as a fabric themselves. While other types of flexible energy storage devices are discussed, the focus is on coated, fibre, woven as well as knitted supercapacitors and batteries.
IEEE Transactions on Biomedical Circuits and Systems | 2016
Damiano Patron; William M. Mongan; Timothy P. Kurzweg; Adam K. Fontecchio; Genevieve Dion; Endla Anday; Kapil R. Dandekar
Recent advancements in conductive yarns and fabrication technologies offer exciting opportunities to design and knit seamless garments equipped with sensors for biomedical applications. In this paper, we discuss the design and application of a wearable strain sensor, which can be used for biomedical monitoring such as contraction, respiration, or limb movements. The system takes advantage of the intensity variations of the backscattered power (RSSI) from an inductively-coupled RFID tag under physical stretching. First, we describe the antenna design along with the modeling of the sheet impedance, which characterizes the conductive textile. Experimental results with custom fabricated prototypes showed good agreement with the numerical simulation of input impedance and radiation pattern. Finally, the wearable sensor has been applied for infant breathing monitoring using a medical programmable mannequin. A machine learning technique has been developed and applied to post-process the RSSI data, and the results show that breathing and non-breathing patterns can be successfully classified.
Journal of Applied Polymer Science | 2017
Ariana Levitt; Chelsea Knittel; Richard Vallett; Michael Koerner; Genevieve Dion; Caroline L. Schauer
Higher ordered structures of nanofibers, including nanofiber-based yarns and cables, have a variety of potential applications, including wearable health monitoring systems, artificial tendons, and medical sutures. In this study, twisted assemblies of polyacrylonitrile (PAN), polyvinylidene fluoride trifluoroethylene (PVDF-TrFE), and polycaprolactone (PCL) nanofibers were fabricated via a modified electrospinning setup, consisting of a rotating cone-shaped copper collector, two syringe pumps, and two high voltage power supplies. The fiber diameters and twist angles varied as a function of the rotary speed of the collector. Mechanical testing of the yarns revealed that PVDF-TrFe and PCL yarns have a higher strain-to-failure than PAN yarns, reaching 307% for PCL nanoyarns. For the first time, the porosity of nanofiber yarns was studied as a function of twist angle, showing that PAN nanoyarns are more porous than PCL yarns.
ieee international conference on smart computing | 2016
William M. Mongan; Endla Anday; Genevieve Dion; Adam K. Fontecchio; Kelly Joyce; Timothy P. Kurzweg; Yuqiao Liu; Owen Montgomery; Ilhaan Rasheed; Cem Sahin; Shrenik Vora; Kapil R. Dandekar
We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.
wireless and microwave technology conference | 2014
Damiano Patron; Timothy P. Kurzweg; Adam K. Fontecchio; Genevieve Dion; Kapil R. Dandekar
Intensity variations of the backscattered power from an RFID tag have been demonstrated to be a potential wireless solution to measure material deformation. This paper discusses the design and performance of a flexible tag antenna equipped with novel inductively-coupled RFID microchip for use as a wireless strain sensor. Dielectric characterization of a flexible substrate has been carried out to properly design and simulate the proposed antenna design. Due to the balanced nature of this radiating element, differential scattering parameter measurements were performed to characterize the antenna input impedance. Finally, measurements of the backscattered power as a function of radial deformations are also shown as a qualitative analysis of the strain sensing capabilities.
ieee conference on antenna measurements applications | 2016
Yuqiao Liu; Ariana Levitt; Christina Kara; Cem Sahin; Genevieve Dion; Kapil R. Dandekar
In this study, we built a lumped component model for knit tag antennas. Comfortable, seamless, and wireless antennas were manufactured using conductive yarns and RFID technology. Knitting fabrication enabled rapid prototyping of these wearable antennas. Using the lumped component model, we optimized the geometry and knit structure of the antenna, resulting in improved radiation efficiency, reading range, and sensitivity.
ieee signal processing in medicine and biology symposium | 2015
William M. Mongan; Kapil R. Dandekar; Genevieve Dion; Timothy P. Kurzweg; Adam K. Fontecchio
Wearable smart devices have become ubiquitous, with powered devices capable of collecting real-time biometric information from its users. Typically, these devices require a powered component to be worn and maintained, such as a battery-powered sensor, Bluetooth communications device, or glasses. Pregnancy and infant monitoring devices may be uncomfortable to the mother or baby and are subject to signal loss if the patient changes position or becomes mobile because the device must remain tethered to the patient by a belt and plugged into a wall for power. Our wearable, wireless, smart garment devices are knitted into the fabric using conductive thread to which a Radio Frequency Identification (RFID) chip within the fabric is inductively coupled. Our work utilizes the Received Signal Strength Indication (RSSI), which changes as the knitted antenna is deformed due to stretching of the garment, to determine different types of motion in the inductively-coupled chip and knit antenna structure as it is moved by the wearer.
ieee signal processing in medicine and biology symposium | 2016
William M. Mongan; Ilhaan Rasheed; Khyati Ved; Ariana Levitt; Endla Anday; Kapil R. Dandekar; Genevieve Dion; Timothy P. Kurzweg; Adam K. Fontecchio
Signal processing of time-series properties of Radio Frequency Identification (RFID) tags and novel work in textile knitted antennas for garment devices have enabled real-time detection of motion-based artifacts through unobtrusive, wireless, wearable devices. Capturing the Received Signal Strength Indicator (RSSI) as a time-series signal, we classify whether the subject is breathing or not, estimate the rate at which the subject is breathing, and classify whether the tag is moving in a linear, non-stretched fashion. We improve upon previous efforts to classify subject state from RSSI signals by eliminating the need to train the classifier with both breathing and non-breathing sample data (which is biologically infeasible). To test our approach, we use a programmable breathing infant mannequin yielding accurate detection of cessation of respiratory activity within 5 seconds, and a maximum root-mean-square error of 7 per minute when computing the respiratory rate.