A microfabricated dielectrophoretic micro-organism concentrator

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2004."May 2004."Includes bibliographical references (leaves 83-85).This project focuses on the development of a micro-organism concentrator. Pathogen detection, particularly MEMS based detection, is often limited by sample concentration. The proposed concentrator will interface with a pathogen detector. This type of pathogen concentrator can be useful for many kinds of applications including water purification systems, medical applications and biological warfare agent detection. Due to the nature of these applications, the concentrator must be able to operate under real-world conditions, and be robust to particulates and variations in solution conductivity. The concentrator is an active filter, which concentrate bacteria in solution using negative dielectrophoresis, which pushes objects away from the electrodes toward field minima. An electric field barrier is set up to guide cells toward a concentrated outlet flow path while the bulk of the fluid, which permeates the electric field barrier, is sent to a waste outlet. The cells are collected at the outlet and selectively released by turning off the applied voltage. I have fully designed and modeled the characteristics of the proposed concentrator and successfully fabricated the design. I have characterized the system throughput using polystyrene beads and I have characterized the system electrically using lumped circuit element models.by Rikky Muller.M.Eng.and S.B

Wireless ear EEG to monitor drowsiness.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications

Fast non-iterative algorithm for 3D point-cloud holography

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, exceeding 100,000x at 512x512 array format.Comment: 22 pages, 11 figures, manuscript and supplemen

A micromirror array with annular partitioning for high-speed random-access axial focusing

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics, and material processing. However, the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to drive (ex: liquid crystal, elastomeric or optofluidic lenses) suffer from low (~ 100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as their dwelling capacity (ex: acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (e.g., deformable mirrors). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion and 48 um-pitch micromirror pixels that provide 2pi phase shifting for wavelengths shorter than 1 100 nm with 10-90 % settling in 64.8 us (i.e., 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires less than 30 V per channel. Optical experiments demonstrated the array's wide focusing range with a measured ability to target 29 distinct, resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.Comment: 38 pages, 8 figure

Low Power, Scalable Platforms for Implantable Neural Interfaces

Clinically viable and minimally invasive neural interfaces stand to revolutionize disease care for patients with neurological conditions. For example, recent research in Brain-Machine Interfaces has shown success in using electronic signals from the motor cortex of the brain to control artificial limbs, providing hope for patients with spinal cord injuries. Currently, neural interfaces are large, wired and require open-skull operation. Future, less invasive interfaces with increased numbers of electrodes, signal processing and wireless capability will enable prosthetics, disease control and completely new user-computer interfaces.The first part of this thesis presents a signal-acquisition front end for neural recording that uses a digitally intensive architecture to reduce system area and enable operation from a 0.5V supply. The entire front-end occupies only 0.013mm2 while including "per-pixel" digitization, and enables simultaneous recording of LFP and action potentials for the first time. The second part presents the development of a minimally invasive yet scalable wireless platform for electrocorticography (ECoG), an electrophysiological technique where electrical potentials are recorded from the surface of the cerebral cortex, greatly reducing cortical scarring and improving implant longevity. A high-density flexible MEMS electrode array is tightly integrated with active circuits and a power-receiving antenna to realize a fully implantable system in a very small footprint. Building on the previously developed digitally intensive architecture, an order of magnitude in circuit area reduction is realized with 3x improvement in power efficiency over state-of-the-art enabling a scalable platform for 64-channel recording and beyond