The primary objective of this research is to study the mechanics of blood sucking by a mosquito, material and structural characterization of the parts of mosquito anatomy used needed in accomplishing the task, and translate the understanding into engineering specifications for a ‘synthetic mosquito’ or a “swarm of mosquitoes” made up of several synthetic mosquitoes operating in parallel, for painless drawing of blood from a live host, including humans. The second objective is to explore novel material systems and manufacturing processes for the mass production of synthetic fascicle and proboscis. Both analytical and experimental approaches will be used to understand the fundamentals of blood sucking by a mosquito, structural behavior of the fascicle, and the manufacture of synthetic fascicle. The research proposed for creating an artificial mosquito blood draw system, offers an excellent opportunity to integrate many different fields of science, such as biomaterials, micro-scale mechanics, MEMS, nano-manufacturing, and biology through an inter-disciplinary team. The proposed research is likely result in a novel and revolutionary blood collection method to make the process painless for millions of people around the world, especially, small children and babies requiring blood draw several times a day for monitoring several constituents, for example, glucose or bilirubin levels.

In this project, we are designing novel sensors and actuation systems for recognizing chemical content in paper in real-time and sort them from a mixed waste stream. This project is funded by the Department of Energy-Agenda2020 program. We have come up with a novel sensor to identify the lignin content in paper which is dominant in newspaper as opposed to writing paper. The operating principle consists of exciting the sample surface with a 532 nm laser light source and measuring the intensity of fluorescence at 650nm and comparing it with a standard sample. The sensor principle has been demonstrated. We are now working on sensor integration into a comprehensive sensing system that consists of color sensor, gloss sensor to recognize plastics on the surface, and the fusion of sensor information through a neural network to classify the samples, moving at 60 feet per second. The project also involves recognizing different shades of color based on captured image process ing and tracking the sample during its motion on the conveyor. Pneumatic actuation systems are being designed as well for precisely moving a light, flexible sample moving at high speed from one position to the other.