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京都大学 / 新制・課程博士 / 博士(理学) / 甲第24397号 / 理博第4896号 / 新制||理||1699(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 早川 尚男, 教授 佐々 真一, 准教授 藤 定義 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Three- different groups of materials were chosen to investigate the uniaxial compaction of particulate solids. Dentritic and cubic sodium chloride were chosen as plastically deforming, dicalcium phosphcte and sugar as fragmentary and styrocell, homopolymer and copolyrinier as non-compactable materials. The uniaxial compaction of the materials was continuously followed by measurement. of 1-.h e applied force, the force transmitted radially to the die wall and the upper punch displacement. The data obtained was presented in the form of Mohr circles, stress pathways (shear-mean compaction stress planes) and a three dimensional representation in mean compaction stress, shear stress and volume change. The yield loci evaluated from Mohr circles and shear-mean compaction stress relationships of compactable and non-compactable materials were found to be similar in shape. The unloading stress profiles were however more informative. All unloading shear-mean compaction stres's curves of the compactable materials cross the mean compaction stress axis to give negative values of shear stress and reach a minimum value of T min' which was material and compaction p.,- essure dependent. The unloading curves of non-compactable materials gaye approximately zero shear. The parameters evaluated from the characteristic stress profiles were correlated to the tensile strength and hardness of compacts. Mathematical expressions have been proposed for the shear-mean compaction stress relationships of the materials investigated. TI he materials were characterised before and after compaction in terms of specific surface aroa, porosity and mechanical strength of compacts with ccrnpaction pressure. / Sudan Government and the Institution of Chemical Engineers.

The overall goal of this thesis is to present a comprehensive understanding of the Nottawasaga River system. In the first chapter, we will examine how landscape features (geomorphology and land cover) drive spatial variation in nutrient and sediment loading from 11 sub-watersheds to the Nottawasaga River. The second chapter will relate how tributary loading and other in-stream processes (riffles, substrate, dissolved oxygen) contribute to the longitudinal variation in water quality along with middle and lower reaches of the Nottawasaga River. Finally, in the last chapter we use 13 water quality variables to develop a Stream Water Quality Index (SWQI) to identify critical areas in the NRW that are most at risk. This thesis will provide environmental agencies with useful information to help implement management strategies to improve the health of riverine systems at a watershed scale. / Eutrophication from agricultural runoff is a global problem, often resulting in formation of anoxic zones in receiving water bodies. The Nottawasaga River Watershed (2,900 km2) is dominated by agricultural land-use, and is a major source of nutrients and sediment to Nottawasaga Bay, Georgian Bay (Lake Huron). The primary objective of our study was to develop a holistic understanding of the different sources and processes that influence spatial variation of water quality across the Nottawasaga River (121 km). In our first chapter, we use landscape features to develop 6 models that predict daily base flow loading rates of total phosphorus (TP) and total suspended solids (TSS) from 11 sub-watersheds. We found that drainage area and % pasture land were the most significant predictive variables driving spatial variability in TP and TSS loading. We also found a significant positive relationship between TP and % wetland, suggesting that the Minesing Wetlands (largest inland wetland in southern Ontario) are a source of nutrients to the river. In our second chapter, we evaluate how tributary inputs and in-stream processes contribute to the longitudinal variation in water quality along the Nottawasaga River. We found that tributary concentration and discharge significantly predict downstream turbidity (TURB), but do not predict downstream TP. We also found that riffles improve water clarity, and that silt and clay substrate is significantly associated with high TURB. In our third chapter, we develop a Stream Water Quality Index (SWQI) using 13 variables collected at 15 stations along the Nottawasaga River. To predict SWQI scores for any site, we have provided 9 equations that use various combinations of available variables. Understanding landscape variables, as well as tributary and in-stream processes that influence water quality will enhance the development of restoration initiatives to improve ecosystem health in lotic systems at a watershed scale. / Thesis / Master of Science (MSc) / Eutrophication from agricultural runoff is a global problem, often resulting in formation of anoxic zones. The Nottawasaga River Watershed is dominated by agricultural land-use, and is a major source of nutrients and sediment to Georgian Bay, Lake Huron. The objective of our study was to develop a holistic understanding of sources and processes that influence spatial variation of water quality across the Nottawasaga River. We found that landscape features (drainage area, pasture, wetland), tributary inputs, and in-stream processes (riffles, substrate) significantly influence water quality. Our results will enhance restoration initiatives to improve health of riverine systems at a watershed scale.

Little historical water quality data is available for the Upper Pearl River Basin (UPRB), yet there are UPRB waters listed as impaired. Objectives of this research were to measure pesticide and sediment concentrations in UPRB surface waters and validate the Annualized Agricultural Nonpoint-Source (AnnAGNPS) runoff model with the measured data for a portion of the UPRB. An additional objective was to quantify effects of land use changes on UPRB surface waters from 1987 to 2002 using AnnAGNPS. Of the fifteen compounds analyzed, hexazinone was most frequently detected, in 94% of samples, followed by metolachlor, tebuthiuron, and atrazine. Metribuzin was detected in only 6% of samples. Total dissolved solids (TDS) concentrations were highest at Carthage, which drains the largest area of three sites sampled for TDS. Most samples measured below Environmental Protection Agency (EPA) standards for pesticides and TDS in drinking water and also below levels toxic to aquatic organisms. For eight of twelve months analyzed between October 2001 and January 2003, average monthly sediment loadings for measured and AnnAGNPS-simulated data differed no more than 109%, resulting in an R&178; value of 0.328. A comparison of measured and simulated atrazine and metolachlor loadings by event resulted in R&178; values of 0.095 and 0.062, respectively. Most daily atrazine and metolachlor loadings for measured and predicted data were very low. On May 18, 2003, AnnAGNPS predicted a metolachlor loading of 80 mg, while measured data showed a loading of 5.6 mg. Measured data showed an earlier spike on January 20, 2003 that was not mirrored by the model. Atrazine comparisons followed the same trend, except measured loadings did not spike until February 22, 2003. The 2002 AnnAGNPS simulation resulted in 15% more average annual runoff than the 1987 simulation, although both simulations had the same precipitation. The 2002 simulation also had higher values for sediment and organic carbon loading. Nitrogen loading was the only runoff or pollutant loading category that was less for 2002 than for 1987. Urban land cover contributed more runoff and pollutant loadings from 1987 to 2002, while traditional row crop agriculture had less of an impact on pollutant loadings.

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Over the course of the last century, electronic structure theory (or, alternatively, computational quantum chemistry) has grown from being a fledgling field to being a "full partner with experiment" [Goddard Science 1985, 227 (4689), 917--923]. Numerous instances of theory matching experiment to very high accuracy abound, with one excellent example being the high-accuracy ab initio thermochemical data laid out in the 2004 work of Tajti and co-workers [Tajti et al. J. Chem. Phys. 2004, 121, 11599] and another being the heats of formation and molecular structures computed by Feller and co-workers in 2008 [Feller et al. J. Chem. Phys. 2008, 129, 204105]. But as the authors of both studies point out, this very high accuracy comes at a very high cost. In fact, at this point in time, electronic structure theory does not suffer from an accuracy problem (as it did in its early days) but a cost problem; or, perhaps more precisely, it suffers from an accuracy-to-cost ratio problem. We can compute electronic energies to nearly any precision we like, as long as we are willing to pay the associated cost. And just what are these high computational costs? For the purposes of this work, we are primarily concerned with the way in which the computational cost of a given method scales with the system size; for notational purposes, we will often introduce a parameter, N, that is proportional to the system size. In the case of Hartree-Fock, a one-body wavefunction-based method, the scaling is formally N⁴, and post-Hartree-Fock methods fare even worse. The coupled cluster singles, doubles, and perturbative triples method [CCSD(T)], which is frequently referred to as the "gold standard" of quantum chemistry, has an N⁷ scaling, making it inapplicable to many systems of real-world import. If highly accurate correlated wavefunction methods are to be applied to larger systems of interest, it is crucial that we reduce their computational scaling. One very successful means of doing this relies on the fact that electron correlation is fundamentally a local phenomenon, and the recognition of this fact has led to the development of numerous local implementations of conventional many-body methods. One such method, the DLPNO-CCSD(T) method, was successfully used to calculate the energy of the protein crambin [Riplinger, et al. J. Chem. Phys 2013, 139, 134101]. In the following work, we discuss how the local nature of electron correlation can be exploited, both in terms of the occupied orbitals and the unoccupied (or virtual) orbitals. In the case of the former, we highlight some of the historical developments in orbital localization before applying orbital localization robustly to infinite periodic crystalline systems [Clement, et al. 2021, Submitted to J. Chem. Theory Comput.]. In the case of the latter, we discuss a number of different ways in which the virtual space can be compressed before presenting our pioneering work in the area of iteratively-optimized pair natural orbitals ("iPNOs") [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. Concerning the iPNOs, we were able to recover significant accuracy with respect to traditional PNOs (which are unchanged throughout the course of a correlated calculation) at a comparable truncation level, indicating that our improved PNOs are, in fact, an improved representation of the coupled cluster doubles amplitudes. For example, when studying the percent errors in the absolute correlation energies of a representative sample of weakly bound dimers chosen from the S66 test suite [Řezác, et al. J. Chem. Theory Comput. 2011, 7 (8), 2427--2438], we found that our iPNO-CCSD scheme outperformed the standard PNO-CCSD scheme at every truncation threshold (τ_PNO) studied. Both PNO-based methods were compared to the canonical CCSD method, with the iPNO-CCSD method being, on average, 1.9 times better than the PNO-CCSD method at τ_PNO = 10⁻⁷ and more than an order of magnitude better for τ_PNO < 10⁻¹⁰ [Clement, et al. J. Chem. Theory Comput 2018, 14 (9), 4581--4589]. When our improved PNOs are combined with the PNO-incompleteness correction proposed by Neese and co-workers [Neese, et al. J. Chem. Phys. 2009, 130, 114108; Neese, et al. J. Chem. Phys. 2009, 131, 064103], the results are truly astounding. For a truncation threshold of τ_PNO = 10⁻⁶, the mean average absolute error in binding energy for all 66 dimers from the S66 test set was 3 times smaller when the incompleteness-corrected iPNO-CCSD method was used relative to the incompleteness-corrected PNO-CCSD method [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. In the latter half of this work, we present our implementation of a limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) based Pipek-Mezey Wannier function (PMWF) solver [Clement, et al. 2021 }, Submitted to J. Chem. Theory Comput.]. Although orbital localization in the context of the linear combination of atomic orbitals (LCAO) representation of periodic crystalline solids is not new [Marzari, et al. Rev. Mod. Phys. 2012, 84 (4), 1419--1475; Jònsson, et al. J. Chem. Theory Comput. 2017, 13} (2), 460--474], to our knowledge, this is the first implementation to be based on a BFGS solver. In addition, we are pleased to report that our novel BFGS-based solver is extremely robust in terms of the initial guess and the size of the history employed, with the final results and the time to solution, as measured in number of iterations required, being essentially independent of these initial choices. Furthermore, our BFGS-based solver converges much more quickly and consistently than either a steepest ascent (SA) or a non-linear conjugate gradient (CG) based solver, with this fact demonstrated for a number of 1-, 2-, and 3-dimensional systems. Armed with our real, localized Wannier functions, we are now in a position to pursue the application of local implementations of correlated many-body methods to the arena of periodic crystalline solids; a first step toward this goal will, most likely, be the study of PNOs, both conventional and iteratively-optimized, in this context. / Doctor of Philosophy / Increasingly, the study of chemistry is moving from the traditional wet lab to the realm of computers. The physical laws that govern the behavior of chemical systems, along with the corresponding mathematical expressions, have long been known. Rapid growth in computational technology has made solving these equations, at least in an approximate manner, relatively easy for a large number of molecular and solid systems. That the equations must be solved approximately is an unfortunate fact of life, stemming from the mathematical structure of the equations themselves, and much effort has been poured into developing better and better approximations, each trying to balance an acceptable level of accuracy loss with a realistic level of computational cost and complexity. But though there has been much progress in developing approximate computational chemistry methods, there is still great work to be done. Many chemical systems of real-world import (particularly biomolecules and potential pharmaceuticals) are simply too large to be treated with any methods that consistently deliver acceptable accuracy. As an example of the difficulties that come with trying to apply accurate computational methods to systems of interest, consider the seminal 2013 work of Riplinger and co-workers [Riplinger, et al. J. Chem. Phys. 2013, 139, 134101]. In this paper, they present the results of a calculation performed on the protein crambin. The method used was DLPNO-CCSD(T), an approximation to the "gold standard" computational method CCSD(T). The acronym DLPNO-CCSD(T) stands for "`domain-based local pair natural orbital coupled cluster with singles, doubles, and perturbative triples." In essence, this method exploits the fact that electron-electron interactions ("electron correlation") are a short-range phenomenon in order to represent the system in a mathematically more compact way. This focus on the locality of electron correlation is a crucial piece in the effort to bring down computational cost. When talking about computational cost, we will often talk about how the cost scales with the approximate system size N. In the case of CCSD(T), the cost scales as N⁷. To see what this means, consider two chemical systems A and B. If system B is twice as large as system A, then the same calculation run on both systems will take 2⁷ = 128 times longer on system B than on system A. The DLPNO-CCSD(T) method, on the other hand, scales linearly with the system size, provided the system is sufficiently large (we say that it is "asymptotically linearly scaling"), and so, for our example systems A and B, the calculation run on system B should only take twice as long as the calculation run on system A. But despite the favorable scaling afforded by the DLPNO-CCSD(T) method, the time to solution is still prohibitive. In the case of crambin, a relatively small protein with 644 atoms, the calculation took a little over 30 days. Clearly, such timescales are unworkable for the field of biochemical research, where the focus is often on the interactions between multiple proteins or other large biomolecules and where many more data points are required. In the work that follows, we discuss in more detail the genesis of the high costs that are associated with highly accurate computational methods, as well as some of the approximation techniques that have already been employed, with an emphasis on local correlation techniques. We then build off this foundation to discuss our own work and how we have extended such approximation techniques in an attempt to further increase the possible accuracy to cost ratio. In particular, we discuss how iteratively-optimized pair natural orbitals (the PNOs of the DLPNO-CCSD(T) method) can provide a more accurate but also more compact mathematical representation of the system relative to static PNOs [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. Additionally, we turn our attention to the problem of periodic infinite crystalline systems, a class of materials less commonly studied in the field of computational chemistry, and discuss how the local correlation techniques that have already been applied with great success to molecular systems can potentially be applied in this domain as well [Clement, et al. 2021, Submitted to J. Chem. Theory Comput.].