PREPARATION OF POLYELECTROLYTE MEMBRANES EMBEDDED WITH ZEOLITE NANOPARTICLES FOR ENHANCED PERFORMANCE IN FORWARD OSMOSIS

Water scarcity is a global issue that has already affected every continent. Membrane technology is considered as one of the most promising candidates for resolving this worsening issue. Among all the membrane processes, the emerging forward osmosis (FO) membrane process is osmotically-driven and has unique advantages compared with other traditional pressure-driven membrane processes. One of the major challenges to advancing the FO membrane process is the lack of a suitable membrane. Polyelectrolyte thin film prepared via layer-by-layer (LbL) technique has demonstrated its excellent performance in many applications including electronics, optics, sensors, etc. Recent studies have revealed the potential of polyelectrolyte thin films in acting as the active separation layer of FO membranes, but significant efforts are still needed to improve the membrane performance and understand the transport mechanisms. This dissertation introduces a novel approach to prepare a zeolite-embedded polyelectrolyte composite membrane for enhanced FO performance. This membrane takes advantages of the versatile LbL process to unprecedentedly incorporate high loading of zeolite nanoparticles, which are anticipated to facilitate water transport due to the uniquely interconnected structure of zeolites. Major topics discussed in this dissertation include: (1) the synthesis and evaluation of the polyelectrolyte-zeolite composite FO membrane, (2) the examination of the fouling resistance to identify its technical limitations, (3) the demonstration of the membrane regenerability as an effective strategy for membrane fouling control, and (4) the investigation of crosslinking effects on the membrane performance to elucidate the transport mechanisms involved in the zeolite-embedded polyelectrolyte membranes. Comparative studies have been made between polyelectrolyte membranes with and without zeolite incorporation. The findings suggest that the zeolite-embedded membrane, although slightly more susceptible to silica scaling, has demonstrated enhanced water flux and separation capability, good resistance to organic fouling, and complete regenerability for fouling control. Additionally, the embedded zeolite nanoparticles are proved to be able to create fast pathways for water transport. Overall, this work provides a novel strategy to create zeolite-polymer composite membranes with enhanced separation performance and unique fouling mitigation properties.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.