Efforts to adapt and extend graphic arts printing techniques for demanding device applications in electronics, biotechnology and microelectromechanical systems have grown rapidly in recent years. Here, we describe the use of electrohydrodynamically induced fluid flows through fine microcapillary nozzles for jet printing of patterns and functional devices with submicrometre resolution. Key aspects of the physics of this approach, which has some features in common with related but comparatively low-resolution techniques for graphic arts, are revealed through direct high-speed imaging of the droplet formation processes. Printing of complex patterns of inks, ranging from insulating and conducting polymers, to solution suspensions of silicon nanoparticles and rods, to single-walled carbon nanotubes, using integrated computer-controlled printer systems illustrates some of the capabilities. High-resolution printed metal interconnects, electrodes and probing pads for representative circuit patterns and functional transistors with critical dimensions as small as 1 mum demonstrate potential applications in printed electronics.

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We have studied the so-called roof collapse in soft lithography. Roof collapse is due to the adhesion between the PDMS stamp and substrate, and it may affect the quality of soft lithography. Our analysis accounts for the interactions of multiple punches and the effect of elastic mismatch between the PDMS stamp and substrate. A scaling law among the stamp modulus, punch height and spacing, and work of adhesion between the stamp and substrate is established. Such a scaling law leads to a simple criterion against the unwanted roof collapse. The present study agrees well with the experimental data.

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I terative learning control (ILC) is based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions (trials, iterations, passes). For instance, a basketball player shooting a free throw from a fixed position can improve his or her ability to score by practicing the shot repeatedly. During each shot, the basketball player observes the trajectory of the ball and consciously plans an alteration in the shooting motion for the next attempt. As the player continues to practice, the correct motion is learned and becomes ingrained into the muscle memory so that the shooting accuracy is iteratively improved. The converged muscle motion profile is an open-loop control generated through repetition and learning. This type of learned open-loop control strategy is the essence of ILC.We consider learning controllers for systems that perform the same operation repeatedly and under the same operating conditions. For such systems, a nonlearning con-troller yields the same tracking error on each pass. Although error signals from previous iterations are information rich, they are unused by a nonlearning controller. The objective of ILC is to improve performance by incorporating error information into the control for subsequent iterations. In doing so, high performance can be achieved with low transient tracking error despite large model uncertainty and repeating disturbances. ILC differs from other learning-type control strategies, such as adaptive control, neural networks, and repetitive control (RC). Adaptive control strategies modify the controller, which is a system, whereas ILC modifies the control input, which is a signal [1]. Additionally, adaptive controllers typically do not take advantage of the information contained in repetitive command signals. Similarly, neural network learning involves the modification of controller parameters rather than a control signal; in this case, large networks of nonlinear neurons are modified. These large networks require extensive training data, and fast convergence may be difficult to

This thesis details the efforts to develop a dynamic model of a transcritical vapor compression system suitable for multivariable control design purposes. The modeling approach is described and the developed models are validated with experimental data. The models are nonlinear, independent of fluid type, and based on first principles. Linearized versions of the nonlinear models are presented. Analysis of the linearized models and empirical models created using system identification techniques suggest that lower order models are adequate for the prediction of dominant system dynamics. Singular perturbation techniques are used to justify model reduction.Based on the reduced order models, the dominant dynamics of these systems are identified and described in terms of physical phenomena. Although all results presented are for a transcritical vapor compression cycle with carbon dioxide as the working fluid, the methodology and results can be extended to both subcritical and transcritical systems.iv