This thesis reports experimental observations of electric fields using Rydberg atoms, including dc field measurements near the surface of an atom chip, and demonstration of measurement techniques for ac fields far from the surface. Associated theoretical results are also presented, including Monte Carlo simulations of the decoherence of Rydberg states in electric field noise as well as an analytical calculation of the statistics of dc electric field inhomogeneity near polycrystalline metal surfaces. DC electric fields were measured near the heterogeneous metal and dielectric surface of an atom chip using optical spectroscopy on cold atoms released from the trapping potential. The fields were attributed to charges accumulating in the dielectric gaps between the wires on the chip surface. The field magnitude and direction depend on the details of the dc biasing of the chip wires, suggesting that fields may be minimized with appropriate biasing. Techniques to measure ac electric fields were demonstrated far from the chip surface, using the decay of a coherent superposition of two Rydberg states of cold atoms. We have used the decay of coherent Rabi oscillations to place some bounds on the magnitude and frequency dependence of ac field noise. The rate of decoherence of a superposition of two Rydberg states was calculated with Monte Carlo simulations. The states were assumed to have quadratic Stark shifts and the power spectrum of the electric field noise was assumed to have a power-law dependence of the form 1/f^κ. The decay is exponential at long times for both free evolution of the superposition and and Hahn spin-echo sequences with a π refocusing pulse applied to eliminate the effects of low-frequency field noise. This decay time may be used to calculate the magnitude of the field noise if κ is known. The dc field inhomogeneity near polycrystalline metal surfaces due to patch potentials on the surface has been calculated, and the rms field scales with distance to the surface as 1/z^2. For typical evaporated metal surfaces the magnitude of the rms field is comparable to the image field of an elementary charge near the surface.

Particleboard (PB) is a widely used panel material because of its physical properties and low cost. Unfortunately, cutting can degrade its surface creating rejects and increasing manufacturing costs. A major challenge is PB’s internal variability. Different particle and glue bond strength combinations can sometimes create high quality surfaces in one area and defects such as edge chipping in nearby areas. This research examines methods of improving surface quality by examining PB characteristics and their interactions with the cutting tool. It also develops an analytical model and software tool that allows the effects of these factors to be simulated, thereby giving practical guidance and reducing the need for costly experiments. When PB is cut and the glue bond strength is weaker than the particle strength, particles are pulled out, leading to surface defects. When instead the glue bond strength is stronger than the particle strength, particles are smoothly cut, leading to a high quality surface. PB is modeled as a matrix of particles each with stochastically assigned material and glue bond strengths. The PB model is layered allowing particles to be misaligned. Voids are modeled as missing particles. PB cutting is modeled in three zones. In the finished material and tool tip zones, particles are compressed elastically and then crushed at constant stress. After failure, chip formation occurs in the chip formation zone. At large rake angles, the chip is modeled as a transversely loaded beam that can fail by cleavage at its base or tensile failure on its surface. At small rake angles, the chip is modeled as the resultant force acting on the plane from the tool tip through to the panel surface. Experimental and simulation results show that cutting forces increase with depth of cut, glue content and particle strength. They decrease with rake angle. Glue bond strength can be increased to the equivalent particle strength through the selection of particle geometry and the subsequent increased glue bond efficiency, which increases the cut surface quality without the need for additional glue. Minimizing the size and frequency of voids and using larger rake angles can also increase surface quality.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

Digital microfluidics is a new technology that permits manipulation of liquid droplets on an array of electrodes. Using this technology, nanoliter to microliter size droplets of different samples and reagents can be dispensed from reservoirs, moved, split, and merged together. Digital microfluidics is poised to become an important and useful tool for biomedical applications because of its capacity to precisely and automatically carry out sequential chemical reactions. In this thesis, a set of tools is presented to accelerate the integration of digital microfluidics into Lab-on-a-Chip platforms for a wide range of applications. An important contribution in this thesis is the development of three rapid prototyping techniques, including the use of laser printing to pattern flexible printed circuit board (PCB) substrates, to make the technology accessible and less expensive. Using these techniques, both digital and channel microfluidic devices can be produced in less than 30 minutes at a minimal cost. These rapid prototyping techniques led to a new method for manipulating liquid droplets on non-planar surfaces. The method, called All Terrain Droplet Actuation (ATDA), was used for several applications, including DNA enrichment by liquid-liquid extraction. ATDA has great potential for the integration of different physico-chemical environments on Lab-on-a-Chip devices. A second important contribution described herein is the development of a new microfluidic format, hybrid microfluidics, which combines digital and channel microfluidics on the same platform. The new hybrid device architecture was used to perform biological sample processing (e.g. enzymatic digestion and fluorescent labeling) followed by electrophoretic separation of the analytes. This new format will facilitate complete automation of Lab-on-a-Chip devices and will eliminate the need for extensive manual sample processing (e.g. pipetting) or expensive robotic stations. Finally, numerical modeling of droplet actuation on single-plate digital microfluidic devices, using electrodynamics, was used to evaluate the droplet actuation forces. Modeling results were verified experimentally using an innovative technique that estimates actuation forces based on resistive forces against droplet motion. The results suggested a list of design tips to produce better devices. It is hoped that the work presented in this thesis will help introduce digital microfluidics to many of the existing Lab-on-a-Chip applications and inspire the development of new ones.

The increased demand for on-chip communication bandwidth as a result of the multi-core trend has made packet-switched networks-on-chip (NoCs) a more compelling choice for the communication backbone in next-generation systems. NoC designs are sensitive to many design parameters—hence the study of new NoCs can be time-intensive. We propose DART, a fast and flexible FPGA-based NoC simulation architecture. Rather than laying the NoC out directly on the FPGA like previous approaches, DART virtualizes the NoC by mapping its components to a generic NoC simulation engine. This approach has two main advantages: (i) since it is virtualized it can simulate any NoC; and (ii) any NoC can be mapped to the engine without the time-consuming process of rebuilding the FPGA design. We demonstrate that an implementation of DART on a Virtex-II Pro FPGA achieves over 100x speedup over the cycle-based software simulator Booksim, while maintaining the same level of simulation accuracy.

In this thesis, I present a microfluidic platform that enables automated image-based assessment of biological structure and function. My work focuses on assessing intact resistance arteries from the mouse cerebral vascular bed with a diameter of approximately 120µm in vitro. The experimental platform consists of a microfluidic device and a world-to-chip fluidic interconnect that minimizes unwanted dead volumes and eliminates the need for any liquid-filled peripheral equipment. The integrated platform is computer controlled and capable of fully automated operation once a small blood vessel segment is loaded onto the chip. Robust operation of the platform was demonstrated through a series of case studies that assessed small artery function and changes therein induced by incubation with the drug nifedipine, a dihydropyridine calcium channel blocker. In addition artery segments were stained for L-type calcium channels, F-actin and nuclei, from which structural information about cell alignment and shape was quantified.

The increased demand for on-chip communication bandwidth as a result of the multi-core trend has made packet-switched networks-on-chip (NoCs) a more compelling choice for the communication backbone in next-generation systems. NoC designs are sensitive to many design parameters—hence the study of new NoCs can be time-intensive. We propose DART, a fast and flexible FPGA-based NoC simulation architecture. Rather than laying the NoC out directly on the FPGA like previous approaches, DART virtualizes the NoC by mapping its components to a generic NoC simulation engine. This approach has two main advantages: (i) since it is virtualized it can simulate any NoC; and (ii) any NoC can be mapped to the engine without the time-consuming process of rebuilding the FPGA design. We demonstrate that an implementation of DART on a Virtex-II Pro FPGA achieves over 100x speedup over the cycle-based software simulator Booksim, while maintaining the same level of simulation accuracy.

In this thesis, I present a microfluidic platform that enables automated image-based assessment of biological structure and function. My work focuses on assessing intact resistance arteries from the mouse cerebral vascular bed with a diameter of approximately 120µm in vitro. The experimental platform consists of a microfluidic device and a world-to-chip fluidic interconnect that minimizes unwanted dead volumes and eliminates the need for any liquid-filled peripheral equipment. The integrated platform is computer controlled and capable of fully automated operation once a small blood vessel segment is loaded onto the chip. Robust operation of the platform was demonstrated through a series of case studies that assessed small artery function and changes therein induced by incubation with the drug nifedipine, a dihydropyridine calcium channel blocker. In addition artery segments were stained for L-type calcium channels, F-actin and nuclei, from which structural information about cell alignment and shape was quantified.

Following several terrorist attacks that have occurred during this decade, there is an urgent need to develop new technologies for the detection of highly energetic materials that can represent an explosive hazard. In an effort to contribute to the development of these new technologies, this work presents the design aspects of a chip-scale calorimeter that can be used to detect an explosive material by calorimetric methods. The aim of this work is to apply what has been done in the area of chip-scale calorimetry to the screening of highly energetic materials. The prototypes presented here were designed using computer assisted design and finite element analysis tools. The design parameters were set to satisfy the requirements of a sensor that can be integrated into a portable system (handheld) for field applications. The design approach consisted of developing a sensor with thick silicon membranes that can hold micro-size samples and that can operate at high temperatures, while keeping the cost of the sensor low. Contrary to other high resolution systems based on thin-film membranes, our prototypes exhibit a contribution from addenda that is comparable to that from the sample, and hence they have lower sensitivity. However, using thick membranes offers the advantage of producing sensors strong enough for this application and that have significantly lower cost. Once the prototypes were designed, the fabrication was performed using standard microfabrication techniques. Finally, the operation of our prototypes was demonstrated by conducting thermal analysis of different liquid and solid samples.

The detection of particular genetic sequences aids in the early detection and diagnosis of disease; permits monitoring of the health and state of the natural environment; and informs forensic investigations. To date, gene detection has relied on enzymatic amplification followed by optical readout. Though these technologies have advanced dramatically, the instruments and assays are costly and lack portability. The work presented herein addresses an urgent challenge: molecular diagnostics at the point-of-need. This work reports the first electronic chip capable of analyzing - directly, without amplification, and with clinically-relevant sensitivity - multiple genes of interest present in a clinical sample. It reports a dramatic acceleration in sample-to-answer times, with clinically actionable findings in minutes where legacy techniques take hours or days. The key to the sensitivity and speed of the biosensors reported herein lies in their architecture and morphology on multiple lengthscales. It is proven that hybridization-based assays employing a nucleic probe attached to a solid surface can only achieve efficient performance when displayed on a nanotextured surface. It is also discovered that these same sensing elements must reach tens of micrometers into solution to achieve rapid, sensitive detection of nucleic acids in clinical samples. As a result, the materials integrated onto the sensing chip reported herein are engineered on multiple lengthscales - from the nanometers to the tens of micrometers. Engineering is done through a combination of low-cost, convenient top-down photolithographic patterning; combined with hierarchically-designed bottom-up growth of electrodeposited sensing elements. The capstone of this work is a chip that distinguishes among different types of bacteria in an unpurified sample. The chip gives accurate answers in under half an hour when detecting bacteria at a level of 1.5 colony-forming-unit (cfu) per microliter. These speeds and sensitivies enable the application of this technology in point-of-need assays for infectious disease detection. Ultimately, the work showcases the power of bringing together techniques and principles from materials chemistry, biochemistry, applied physics, and electrical engineering to the solution of an important problem relevant to human health.