Biological materials : Part A. tuning LCST of raft copolymers and gold/copolymer hybrid nanoparticles and Part B. biobased nanomaterials

The research described in this dissertation is comprised of two major parts. The first part studied the effects of asymmetric amphiphilic end groups on the thermo-response of diblock copolymers of (oligo/di(ethylene glycol) methyl ether (meth)acrylates, OEGA/DEGMA) and the hybrid nanoparticles of these copolymers with a gold nanoparticle core. Placing the more hydrophilic end group on the more hydrophilic block significantly increased the cloud point compared to a similar copolymer composition with the end group placement reversed. For a given composition, the cloud point was shifted by as much as 28 °C depending on the placement of end groups. This is a much stronger effect than either changing the hydrophilic/hydrophobic block ratio or replacing the hydrophilic acrylate monomer with the equivalent methacrylate monomer. The temperature range of the coil-globule transition was also altered. Binding these diblock copolymers to a gold core decreased the cloud point by 5-15 °C and narrowed the temperature range of the coil-globule transition. The effects were more pronounced when the gold core was bound to the less hydrophilic block. Given the limited numbers of monomers that are approved safe for in vivo use, employing amphiphilic end group placement is a useful tool to tune a thermo-response without otherwise changing the copolymer composition. The second part of the dissertation investigated the production of value-added nanomaterials from two biorefinery “wastes”: lignin and peptidoglycan. Different solvents and spinning methods (melt-, wet-, and electro-spinning) were tested to make lignin/cellulose blended and carbonized fibers. Only electro-spinning yielded fibers having a small enough diameter for efficient carbonization ( Peptidoglycan (a bacterial cell wall material) was copolymerized with poly-(3-hydroxybutyrate), a common polyhydroxyalkanoate produced by bacteria with the objective of determining if a useful material could be obtained with a less rigorous work-up on harvesting polyhydroxyalkanoates. The copolyesteramide product having 25 wt.% peptidoglycan from a highly purified peptidoglycan increased thermal stability by 100-200 °C compared to the poly-(3-hydroxybutyrate) control, while a less pure peptidoglycan, harvested from B. megaterium (ATCC 11561), gave a 25-50 °C increase in thermal stability. Both copolymers absorbed more moisture than pure poly-(3-hydroxybutyrate). The results suggest that a less rigorously harvested and purified polyhydroxyalkanoate might be useful for some applications.

STUDY OF HYBRID ELECTRIC VEHICLE BATTERY MODELING AND CONTROL USING AUTONOMIE

This report presents the research results of battery modeling and control for hybrid electric vehicles (HEV). The simulation study is conducted using plug-and-play powertrain and vehicle development software, Autonomie. The base vehicle model used for testing the performance of battery model and battery control strategy is the Prius MY04, a power-split hybrid electric vehicle model in Autonomie. To evaluate the battery performance for HEV applications, the Prius MY04 model and its powertrain energy flow in various vehicle operating modes are analyzed. The power outputs of the major powertrain components under different driving cycles are discussed with a focus on battery performance. The simulation results show that the vehicle fuel economy calculated by the Autonomie Prius MY04 model does not match very well with the official data provided by the department of energy (DOE). It is also found that the original battery model does not consider the impact of environmental temperature on battery cell capacities. To improve battery model, this study includes battery current loss on coulomb coefficient and the impact of environmental temperature on battery cell capacity in the model. In addition, voltage losses on both double layer effect and diffusion effect are included in the new battery model. The simulation results with new battery model show the reduced fuel economy error to the DOE data comparing with the original Autonomie Prius MY04 model.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

Monitoring protein-protein interactions between the mammalian integral membrane transporters and PDZ-interacting partners using a modified split-ubiquitin membrane yeast two-hybrid system

PDZ-binding motifs are found in the C-terminal tails of numerous integral membrane proteins where they mediate specific protein-protein interactions by binding to PDZ-containing proteins. Conventional yeast two-hybrid screens have been used to probe protein-protein interactions of these soluble C termini. However, to date no in vivo technology has been available to study interactions between the full-length integral membrane proteins and their cognate PDZ-interacting partners. We previously developed a split-ubiquitin membrane yeast two-hybrid (MYTH) system to test interactions between such integral membrane proteins by using a transcriptional output based on cleavage of a transcription factor from the C terminus of membrane-inserted baits. Here we modified MYTH to permit detection of C-terminal PDZ domain interactions by redirecting the transcription factor moiety from the C to the N terminus of a given integral membrane protein thus liberating their native C termini. We successfully applied this "MYTH 2.0" system to five different mammalian full-length renal transporters and identified novel PDZ domain-containing partners of the phosphate (NaPi-IIa) and sulfate (NaS1) transporters that would have otherwise not been detectable. Furthermore this assay was applied to locate the PDZ-binding domain on the NaS1 protein. We showed that the PDZ-binding domain for PDZK1 on NaS1 is upstream of its C terminus, whereas the two interacting proteins, NHERF-1 and NHERF-2, bind at a location closer to the N terminus of NaS1. Moreover NHERF-1 and NHERF-2 increased functional sulfate uptake in Xenopus oocytes when co-expressed with NaS1. Finally we used MYTH 2.0 to demonstrate that the NaPi-IIa transporter homodimerizes via protein-protein interactions within the lipid bilayer. In summary, our study establishes the MYTH 2.0 system as a novel tool for interactive proteomics studies of membrane protein complexes.

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper's task of selecting a subset of the submitted bids which efficiently trades off total procurement cost against expected carrier performance. To solve this bi-objective winner determination problem, we propose a Pareto-based greedy randomized adaptive search procedure (GRASP). As a post-optimizer we use a path relinking procedure which is hybridized with branch-and-bound. Several variants of this algorithm are evaluated by means of artificial test instances which comply with important real-world characteristics. The two best variants prove superior to a previously published Pareto-based evolutionary algorithm.