Integration and applications of fluorocarbon phase change liquids (FPCL) with MEMS and microfluidics

Advances in polymeric microfabrication and hybrid integration technologies have enabled the miniaturization of sensing and actuation systems with a simultaneous increase in their functionality and performance. In addition, incorporation of smart materials into such platforms has led to further improvements in applications where energy source and power supply is limited. In this thesis, I describe design, fabrication, and characterization of several MEMS and microfluidic devices in which fluorocarbon liquids (FCLs) are introduced/integrated as a smart material in order to enhance their functionalities in terms of energy harvesting, improved transduction sensitivity, and mechanical/fluidic performance. The first device is a capacitive transducer in which integrated fluorocarbon containing chambers lead to a 42.5% increase in sensitivity. Furthermore, by using the same device as the basic module for capacitive energy harvesting, one can increase the energy gained per conversion cycle by 42.3%. The second device is a transdermal micropump/drug dispenser which operates exclusively through the evaporation of the FCL induced by body heat, attaining 28.8 &mgr;L/min flow rate and 28.9 kPa of backpressure. Finally, I present a thermoelectric power generator in which a FCL of low boiling point (34 °C) is used to increase the body-heat-contact harvested energy through evaporative cooling by 226% compared to a control device encapsulated in air

Atomic force microscopy-coupled microcoils for cellular-scale nuclear magnetic resonance spectroscopy

We present the coupling of atomic force microscopy (AFM) and nuclear magnetic resonance (NMR) technologies to enable topographical, mechanical, and chemical profiling of biological samples. Here, we fabricate and perform proof-of-concept testing of radiofrequency planar microcoils on commercial AFM cantilevers. The sensitive region of the coil was estimated to cover an approximate volume of 19.4 x 10(3) mu m(3) (19.4 pl). Functionality of the spectroscopic module of the prototype device is illustrated through the detection of H-1 resonance in deionized water. The acquired spectra depict combined NMR capability with AFM that may ultimately enable biophysical and biochemical studies at the single cell level. (C) 2013 AIP Publishing LL

Polymeric microdevices for transdermal and subcutaneous drug delivery

Low cost manufacturing of polymeric microdevices for transdermal and subcutaneous drug delivery is slated to have a major impact on next generation devices for administration of biopharmaceuticals and other emerging new formulations. These devices range in complexity from simple microneedle arrays to more complicated systems incorporating micropumps, micro-reservoirs, on-board sensors, and electronic intelligence. In this paper, we review devices currently in the market and those in the earlier stages of research and development. We also present two examples of the research in our laboratory towards using phase change liquids in polymeric structures to create disposable micropumps and the development of an elastomeric reservoir for MEMS-based transdermal drug delivery systems. (C) 2012 Elsevier B.V. All rights reserved

Thermoelectric Energy Scavenging with Temperature Gradient Amplification

In this paper, we demonstrate the application of fluorocarbon evaporative cooling in thermoelectric energy scavenging. The fabrication and performance characterization of a prototype micro-device is presented. The device consists of a thermoelectric generator mounted on a silicon substrate and encapsulated in a poly(dimethylsiloxane) chamber with a flexible cover. By filling the chamber with a fluorocarbon liquid of low boiling point (34 degrees C), we were able to increase the body heat contact harvested energy by 226% compared to a device encapsulated in air. The availability of a variety of fluorocarbon liquids with different boiling points allows this harvesting amplification scheme to be used in a wide range of applications

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance