THERMORESPONSIVE PROPERTIES OF GOLD HYBRID NANOPARTICLES OF POLY(DI(ETHYLENE GLYCOL) METHYL ETHER METHACRYLATE) (PDEGMA) AND ITS BLOCK COPOLYMERS WITH DIFFERENT ANCHORING REGIMES

Thermo-responsive materials have been of interest for many years, and have been studied mostly as thermally stimulated drug delivery vehicles. Recently acrylate and methacrylates with pendant ethylene glycol methyl ethers been studied as thermo responsive materials. This work explores thermo response properties of hybrid nanoparticles of one of these methacrylates (DEGMA) and a block copolymer with one of the acrylates (OEGA), with gold nanoparticle cores of different sizes. We were interested in the effects of gold core size, number and type of end groups that anchored the chains to the gold cores, and location of bonding sites on the thermo-response of the polymer. To control the number and location of anchoring groups we using a type of controlled radical polymerization called Reversible Addition Fragmentation Transfer (RAFT) Polymerization. Smaller gold cores did not show the thermo responsive behavior of the polymer but the gold cores did seem to self-assemble. Polymer anchored to larger gold cores did show thermo responsivity. The anchoring end group did not alter the thermoresponsivity but thiol-modified polymers stabilized gold cores less well than chains anchored by dithioester groups, allowing gold cores to grow larger. Use of multiple bonding groups stabilized the gold core. Using block copolymers we tested the effects of number of thiol groups and the distance between them. We observed that the use of multiple anchoring groups on the block copolymer with a sufficiently large gold core did not prevent thermo responsive behavior of the polymer to be detected which allows a new type of thermo-responsive hybrid nanoparticle to be used and studied for new applications.

Model to describe the mode I fracture of steel fiber reinforced ultra-high performance concrete

Ultra-high performance fiber reinforced concrete (UHPFRC) has arisen from the implementation of a variety of concrete engineering and materials science concepts developed over the last century. This material offers superior strength, serviceability, and durability over its conventional counterparts. One of the most important differences for UHPFRC over other concrete materials is its ability to resist fracture through the use of randomly dispersed discontinuous fibers and improvements to the fiber-matrix bond. Of particular interest is the materials ability to achieve higher loads after first crack, as well as its high fracture toughness. In this research, a study of the fracture behavior of UHPFRC with steel fibers was conducted to look at the effect of several parameters related to the fracture behavior and to develop a fracture model based on a non-linear curve fit of the data. To determine this, a series of three-point bending tests were performed on various single edge notched prisms (SENPs). Compression tests were also performed for quality assurance. Testing was conducted on specimens of different cross-sections, span/depth (S/D) ratios, curing regimes, ages, and fiber contents. By comparing the results from prisms of different sizes this study examines the weakening mechanism due to the size effect. Furthermore, by employing the concept of fracture energy it was possible to obtain a comparison of the fracture toughness and ductility. The model was determined based on a fit to P-w fracture curves, which was cross referenced for comparability to the results. Once obtained the model was then compared to the models proposed by the AFGC in the 2003 and to the ACI 544 model for conventional fiber reinforced concretes.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.