The use of nanoreinforcements in automotive structural composites has provided promising improvements in their mechanical properties. For the first time, a robust statistical design of experiments approach was undertaken to demonstrate how key formulation and processing factors (nanofiber type, use of dispersing agent, mixing method, nanofiber weight fraction, and temperature) affected the dynamic mechanical properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Statistical response surface models were developed to predict nanocomposite storage and loss moduli as functions of significant factors. Only ~0.50 parts of nanofiber per hundred parts resin produced a roughly 20% increase in the storage modulus versus that of the neat VE at room temperature. Optimized nanocomposite properties were predicted as a function of design factors employing this methodology. For example, the use of highshear mixing (one of the mixing methods in the design) with the oxidized VGCNFs in the absence of dispersing agent or arbitrarily with pristine VGCNFs in the presence of dispersing agent was found to maximize the predicted storage modulus over the entire temperature range (30-120 °C). To study the key concept of interphase in thermoset nanocomposites, molecular dynamics simulations were performed to investigate liquid VE resin monomer interactions with the surface of a pristine VGCNF. A liquid resin having a mole ratio of styrene to bisphenol A-diglycidyl dimethacrylate monomers consistent with a 33 wt% styrene VE resin was placed in contact with both sides of pristine graphene sheets, overlapped like shingles, to represent the outer surface of a pristine VGCNF. The relative monomer concentrations were calculated in a direction progressively away from the surface of the graphene sheets. At equilibrium, the styrene/VE monomer ratio was higher in a 5 Å thick region adjacent to the nanofiber surface than in the remaining liquid volume. The elevated styrene concentration near the nanofiber surface suggests that a styrene-rich interphase region, with a lower crosslink density than the bulk matrix, could be formed upon curing. Furthermore, styrene accumulation in the immediate vicinity of the nanofiber surface might, after curing, improve the nanofiber-matrix interfacial adhesion compared to the case where the monomers were uniformly distributed throughout the matrix.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24635号 / 工博第5141号 / 新制||工||1982(附属図書館) / 京都大学大学院工学研究科分子工学専攻 / (主査)教授 佐藤 啓文, 教授 作花 哲夫, 教授 寺村 謙太郎 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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Energy needs are projected to continue to increase in the coming decades, and with the drive to use more clean energy to combat climate change, nuclear energy is poised to become an important player in the energy portfolio of the world. Due to the unique nature of nuclear energy, it is always vital to have safe and efficient generation of that energy. In current light water reactors, the most common fuel is uranium dioxide (UO2), an oxide ceramic. There is also ongoing research examining uranium-based based metallic fuels, such as uranium-molybdenum (U-Mo) fuels with low uranium (U) enrichment for research reactors as part of a broader effort to combat nuclear proliferation, and uranium-zirconium-based fuels for Generation IV fast reactors. Each nuclear fuel has weaknesses that need to be addressed for safer and more efficient use. Two major challenges of using UO¬2 are the fission gas (e.g. xenon) release and the decreasing thermal conductivity with increasing burnup. In UMo alloys, the major weakness is the breakaway swelling that occurs at high fission densities. The challenges presented by both fuel types are heavily impacted by microstructure, and several studies have identified that the initial microstructure of the fuel in particular (e.g. initial grain size and grain aspect ratio) plays a large role in determining when and how quickly these processes occur. Thus, knowledge of how such initial microstructures evolve is paramount in having stable and predictable fission gas release and thermal conductivity decrease (in UO2) and fuel swelling (in UMo alloys). Mobility is a critical grain boundary (GB) property that impacts microstructural evolution. Existing literature examines GB mobility for a few specific boundaries but does not (in general) identify the anisotropy relationships that this property has. This work first examined the anisotropy in GB mobility, specifically identifying the anisotropy trend for the low-index rotation axes for tilt GBs in BCC γ U, and fluorite UO2 via molecular dynamics simulation. GB mobility is calculated using the shrinking cylindrical grain method, which uses the capillary effect induced by the GB curvature to drive grain growth. The mobilities are calculated for different rotation axes, misorientation angles, and temperatures in these systems. The results indicated that the density of the atomic plane perpendicular to the (tilt) GB plane (which is also perpendicular to the rotation axis) significantly impacts which GB rotation axis has the fastest boundaries. Specifically, the atomic plane that has a higher density tends to have a faster mobility, because it is more efficient for atoms moving across the GB along such planes. For example, for body-centered cubic materials, the <110> tilt GBs are determined to have the fastest mobilities, while face-centered cubic (FCC) and FCC-like structures such as fluorite have <111> tilt GBs as the fastest. Knowledge of GB mobility and its anisotropy in pure materials is helpful as a baseline, but real materials have solutes or impurities (both intentionally and unintentionally) which are known to affect GB mobility by processes such as solute drag and Zener pinning. Additionally, in reactors, nuclear fission can produce many fission products, each of which acts as an additional impurity that will interact with the GB in some way. Because the initial microstructure and its subsequent evolution are vital for addressing the challenges of using nuclear fuel as described above, knowledge of the impacts of these impurities on GB mobility is required. Therefore, this work examined the impact of solutes and impurities on GB mobility and its anisotropy. In particular, the solute effect was examined using the UMo alloy system, while the impurity effect was examined using Xe (a very common fission product) in the γ U, UMo, and UO2 systems. It is found that both Mo and Xe can cause a solute drag effect on GB mobility in the γ U system, with the effect of Xe being stronger than Mo at the same solute/impurity concentration. Xe also causes a solute drag effect in UO2, though the magnitude of the effect is interatomic-potential-dependent. The mobility anisotropy trend was found to disappear at high solute and impurity concentrations in the metallic U and UMo systems but was largely unaffected in the UO2 system. These results not only increase our fundamental understanding of GB mobility, its anisotropy, and solute/impurity drag effects, but also can be used as inputs for mesoscale simulations to examine polycrystalline grain growth with anisotropic GB mobility and in turn examine how the fuel performance parameters change with these properties. / Doctor of Philosophy / Worldwide, energy needs continue to increase each year. Concerns related to climate change have led to an increased emphasis on renewable energies such as solar and wind, but the limitations of these resources prevent them from being the only energy sources. Nuclear energy is uniquely positioned to address several energy concerns: it is clean (no carbon emissions and air pollution), reliable (for example, 24/7 energy production, independent of weather), and energy-dense (one kilogram of fissile uranium provides roughly the same amount of energy as 3000 metric tons of coal). Currently, nuclear energy provides roughly 20% of the energy of the United States, but future predictions show a decrease in the total share of energy generation due to aging systems and a limited number of new reactors being built. The safety and efficacy of existing and future reactors are among the primary concerns for being able to allow nuclear energy to increase its energy share. To determine the safety and efficacy of new reactor designs, a computer simulation tool called fuel performance modeling has been used over the last few decades. This tool requires several material properties as input, one of which is how the nuclear reactor fuel microstructure changes based on a variety of conditions. A significant process contributing to microstructural change is grain growth. Grains (crystallites that make up the whole material) meet at interfaces called grain boundaries (GBs), and these GBs have two properties that largely determine how grain growth occurs: energy and mobility. Significant effort is being put into understanding these properties and their anisotropy, or how they change based on the GB character which is the relative mismatch between the two grains. This work contributes additional understanding of GB mobility anisotropy in two nuclear fuels: uranium dioxide (UO2, the primary fuel in current reactors) and a uranium-molybdenum (UMo) alloy (the primary fuel for newer research reactors). In particular, computer simulation is used to determine GB mobility for several unique GB systems. It is found that for pure nuclear fuels, GB mobility anisotropy is largely determined by which atomic plane has the highest density perpendicular to the GB. When the fuel is no longer pure (through the addition of alloying elements or other impurities) the anisotropy changes significantly in UMo fuels, such that at high concentrations of solute or impurities there is little to no anisotropy, while very little change is observed in the anisotropy in UO2.

The structure and dynamics of membranes are crucial to ensure the proper functioning of cells. There are some compounds used in therapeutics that show nonspecific interactions with membranes in addition to their specific molecular target. Among them, two compounds recently used in therapeutics against COVID-19, remdesivir and favipiravir, were subjected to molecular dynamics simulation assays. In these, we demonstrated that the compounds can spontaneously bind to model lipid membranes in the presence or absence of cholesterol. These findings correlate with the corresponding experimental results recently reported by our group. In conclusion, insertion of the compounds into the membrane is observed, with a mean position close to the phospholipid head groups.

In the atmosphere in the height of 18000ft to 25000ft, there are some metastable droplets called supercooled liquid water in the temperature range of 0◦C to 40◦C. When these droplets impinge on the wings of an airplane, a very thin layer of ice is formed on the surface. This natural phenomenon calls “impact icing”. In this research, I studied the nanoscale impact icing on structured graphite surfaces, as the substrates at the atomistic scale using Molecular Dynamics (MD) simulations. This research focuses on the first steps of the development of a predictive multiscale strategy for molecular simulations of impact ice adhesion on nanostructured substrates. Through the simulations, the molecular level physics such as molecular interactions, interfacial energy, and nanoscale surface roughness are processed into a “microscopic ice adhesion strength” that describes the energy cost for breaking the nanoscale interfacial layer. In this work, the simulation strategy is designed based on the postulate that at the nanoscale the fracture strength of impact ice on a given substrate is controlled by the extent of the ice interdigitating the substrate. The interdigitating interfacial structure is then determined by the process of wetting the substrate by a supercooled impinged water droplet and the process of penetrating of supercooled water crystallizing into ice crystals under graphene nanoconfinement. Following this line of reasoning, I divided my impact icing simulations into three separate sections including (1) simulations of dynamic wetting of supercooled water on nanostructured graphene substrate, (2) simulations of water crystallization under nano-confinement, and (3) simulations of fracture of prescribed ice-substrate interfacial structure. Based on the results, it is concluded that the degree of surface hydrophobicity, depth of penetrated water, the order of interlocked water molecules, size of surface roughness, texture structure of the surface, and ice temperature are the key roles that dominate the investigation of fracture strength of impact ice at the solid interface. Furthermore, MD simulation results demonstrate that the surface roughness lower than 3.0nm is enabled to stop water from crystallization, a piece of useful information to design anti-icing surfaces.

The beta2-adrenergic receptor is a G-protein coupled receptor, involved in several physiological processes, which enables signaling through the cell membrane. To study the effect of dopamine and adrenaline binding on the receptor structure and function, we used machine learning methods applied to data from molecular dynamics simulations. We found that the three machine learning methods Random Forest, Kullback-Leibler divergence, and Principal Component Analysis generated results that correspond to previous studies. When comparing the active state of the receptor with or without a ligand bound, we found that residues around Ser203 and Asn301 of the orthosteric binding pocket and residues around Ala91 of the TM2 differed. When instead comparing the active state of the receptor with adrenaline or dopamine bound, we found that residues around Thr68 differed. Additionally, we also found that adrenaline and dopamine cause different structural changes in the intracellular parts of TM5 and TM6. These findings indicate ligand-specific effects on the receptor, providing potentially useful information for the understanding of the interaction of adrenaline and dopamine with the beta2-adrenergic receptor.

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