Projects

Graphene-PVAc degradable piezocapacitive sensors

In this work, we propose a facile method of fabricating electrospun graphene-dispersed PVAc nanofibrous membrane-based piezocapacitive sensors for applications in IoT-enabled wearables and human physiological function monitoring. A series of electrical and material characterization experiments were conducted on both the pristine and graphene-dispersed PVAc nanofibers to understand the effect of graphene addition on nanofiber morphology, dielectric response, and pressure sensing performance. Dynamic uniaxial pressure sensing performance evaluation tests were conducted on the pristine and graphene-loaded PVAc nanofibrous membrane-based sensors for understanding the effect of two-dimensional (2D) nanofiller addition on pressure sensing performance. A marked increase in the dielectric constant and pressure sensing performance was observed for graphene-loaded spin coated membrane and nanofiber webs respectively, and subsequently the micro dipole formation model was invoked to explain the nanofiller-induced dielectric constant enhancement. The robustness and reliability of the sensor have been underscored by conducting accelerated lifetime assessment experiments entailing at least 3000 cycles of periodic tactile force loading. A series of tests involving human physiological parameter monitoring were conducted to underscore the applicability of the proposed sensor for IoT-enabled personalized health care, soft robotics, and next-generation prosthetic devices. Finally, the easy degradability of the sensing elements is demonstrated to emphasize their suitability for transient electronics applications.

Electrospun CNF for skin inspired sensing

In this work, the applicability of electrospun carbon nanofiber (CNF) films in forming flexible, ultra-lightweight, linear yet inexpensive skin-like sensors is demonstrated by fabricating piezoresistive sensors for apparel integrable human motion monitoring and large-area tactile sensing applications. For the first time, an artificial skin sensor system capable of both proprioceptive tactile sensory perception and gesture identification is demonstrated utilizing CNF bundles. Strain and pressure sensing performance of piezoresistive CNFs integrated into various sensor designs are experimentally tested through a series of tests involving quasi-static tactile sensing and comprehensive human motion monitoring tasks. To demonstrate the mimicry of proprioceptive perception, a gesture sensing smart glove comprising of 5 thin-film sensors conformally mounted and secured on a soft nitrile glove was developed and tested for 14 different hand gestures. Furthermore, a large area 16-point touch-sensitive artificial skin was proposed and comprehensive tests were conducted to demonstrate the usability of the proposed sensing element in developing skin-inspired large area 2D pressure sensors. Finally, a smart system comprising of five identical strain sensors mimicking proprioceptors and tactile sensors is conceptualized which has potential for recreating the sense of touch in myoelectric prosthetic skins.

Gesture sensing smart glove

To demonstrate the mimicry of proprioceptive perception, a gesture sensing smart glove comprising of 5 thin-film sensors conformally mounted and secured on a soft nitrile glove was developed and tested for 14 different hand gestures. Furthermore, a large area 16-point touch-sensitive artificial skin was proposed and comprehensive tests were conducted to demonstrate the usability of the proposed sensing element in developing skin-inspired large area 2D pressure sensors. The image above shows the actual gesture sensing smart glove. The grid plot shows the response of the finger to individual finger 'flick' movement. Systems such as the one proposed here will find application in remote assisted surgery, remote bomb disposal and other similar tasks.

A Neuromorphic smart glove

To demonstrate the suitability of the sensors for neuromorphic applications, an approach entailing the coupling of carbon nanofiber (CNF)-polydimethylsiloxane (PDMS) composite based piezoresistive sensors with spiking neural networks, to mimic skin-like sensing is presented. Spiking neural networks are novel neural networks that, by emulating the behavior of real biological neurons and synapses, aim at obtaining computation at the edge with low power and low latency. We experimentally tested the combined architecture using software simulated neurons that receive real sensor data. We demonstrated that by combining sensor signals with this technique, complex tasks can be performed. For instance, coincident activity of several sensors affixed on a glove can be used for gesture tracking .

Graphene-PDMS sponges

This project introduces graphene-PDMS foam as a piezoresistive sensing material for the development of ultra-lightweight, highly flexible, wearable, and skin-mountable sensors tailored for human motion monitoring applications. A facile method for developing a 3D squeezable graphene-polydimethylsiloxane (PDMS) foam-based piezoresistive sensor realized by infusing multi-layered graphene nanoparticles into a sugar scaffolded porous PDMS foam structure has been laid down. Fundamental morphological characterization and sensing performance assessment have been carried out on the piezoresistive foam to establish its suitability as the choicest material for the development of squeezable, flexible, and skin-mountable human motion sensors.

Single CNF flow sensor

For the first time, a single piezoresistive CNF was utilized as a sensing element in a NEMS flow sensor to demonstrate airflow sensing. The mechanism of conductive path change under the influence of external stress was hypothesized to explain the piezoresistive behavior observed in the CNF bundles pyrolyzed at lower temperatures. the piezoresistive behavior of a single CNF was explored by suspending an isolated single CNF on a specially designed microelectromechanical systems (MEMS) substrate and conductive graphitic domain discontinuity model was invoked to explain the observed piezoresistive behavior. 

A bio-inspired flow sensor