13 Atomlu Cu-Ag-Au Üçlü Nanoalaşımların Kimyasal Sıralama ve Yapısal Özelliklerinin İncelenmesi

Bu çalışmada, 13 atomlu Cu-Ag-Au üçlü metal nanoalaşımların kimyasal sıralama ve yapısal özellikleri Gupta ve DFT düzeylerinde ve üç farklı kompozisyon sisteminde incelenmiştir. Cu-Ag-Au üçlü nanoalaşımların Gupta düzeyindeki lokal optimizasyonları Basin-Hopping algoritması kullanılarak gerçekleştirilmiştir. Optimizasyon sonuçları Ag atomlarının yüzeye yerleşmeyi tercih ettiğini göstermektedir. Cu ve Au atomlarının nanoalaşımların yüzeyine veya merkezine ayrışma eğilimlerinin ise kompozisyon sistemine göre değiştiği bulunmuştur. Cu-Ag-Au nanoalaşımlarının tüm kompozisyonları için en kararlı kimyasal düzene sahip yapılar DFT relaksasyonu ile yeniden optimize edilmiştir ve Gupta ve DFT düzeylerinin karışma enerjileri karşılaştırılmıştır. Karışma enerjisi analizi, Gupta seviyesinde bulunan Ag1AunCu12-n (n=0-12) ve Au1AgnCu12-n (n=0-12) kompozisyon sistemlerinin en kararlı yapısının DFT ile uyuşmadığını göstermiştir

Antimycobacterial activity of ascidian fungal symbionts

Biomedical Sciences: Molecular Biology and Human Genetic

Determining Material Structures and Surface Chemistry by Genetic Algorithms and Quantum Chemical Simulations

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and DFT calculations, for the latter, are developed and used for the in silico exploration of materials chemistry. Genetic algorithms were first theorized in 1975 by John Holland and over the years subsequently expanded and developed for a variety of purposes. The first application to chemistry came in the early 1990’s and surface chemistry, specifically, appeared soon after. To complement the ability of a GA to explore chemical space is a second algorithmic technique: machine learning (ML) wherein a program is able to categorize or predict properties of an input after reviewing many, many examples of similar inputs. ML has more nebulous origins than GA, but applications to chemistry also appeared in the 1990’s. A history perspective and assessment of these techniques towards surface chemistry follows in this work. A GA designed to find the crystal structure of layered chemical materials given the material’s X-ray diffraction pattern is then developed. The approach reduces crystals into layers of atoms that are transformed and stacked until they repeat. In this manner, an entire crystal need only be represented by its base layer (or two, in some cases) and a set of instructions on how the layers are to be arranged and stacked. Molecules that may be present may not quite behave in this fashion, and so a second set of descriptors exist to determine the molecule’s position and orientation. Finally, the lattice of the unit cell is specified, and the structure is built to match. The GA determines the structure’s X-ray diffraction pattern, compares it against a provided experimental pattern, and assigns it a fitness value, where a higher value indicates a better match and a more fit individual. The most fit individuals mate, exchanging genetic material (which may mutate) to produce offspring which are further subjected to the same procedure. This GA can find the structure of bulk, layered, organic, and inorganic materials. Once a material’s bulk structure has been determined, surfaces of the material can be derived and analyzed by DFT. In this thesis, DFT is used to validate results from the GA regarding lithium-aluminum layered double hydroxide. Surface chemistry is more directly explored in the prediction of adsorbates on surfaces of lithiated nickel-manganese-cobalt oxide, a common cathode material in lithium-ion batteries. Surfaces are evaluated at the DFT+U level of theory, which reduces electron over-delocalization, and the energies of the surfaces both bare and with adsorbates are compared. By applying first-principles thermodynamics to predict system energies under varying temperatures and pressures, the behavior of these surfaces in experimental conditions is predicted to be mostly pristine and bare of adsorbates. For breadth, this thesis also presents an investigation of the electronic and optical properties of organic semiconductors via DFT and time-dependent DFT calculations

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química

Raman spectroscopy based strategies for prevention and early detection of respiratory tract infections in susceptible individuals

Respiratory tract infections (RTIs) are responsible for the significant part of deaths worldwide. At the special danger of lethal outcome are individuals with higher susceptibility to RTIs, this includes people with any type of immunosuppression, individuals suffering from chronic respiratory tract diseases, persons with some genetic disorders affecting the respiratory organs, children till the age of five and elderly generation. Within the framework of this thesis, the potential role of Raman spectroscopic techniques (RST) for improving prevention and diagnostic strategies of RTIs in such susceptible individuals was assessed, highlighting the need of careful choice of the technique according to the desired clinical task. Firstly, using UV-Resonance Raman spectroscopy (UV-RRS) combined with multivariate analysis, the successful identification of fungal spores especially dangerous to susceptible individuals was demonstrated. Next, utilizing mouse model of aspergillosis, conventional Raman spectroscopy of urine was introduced as an easy tool for screening the RTI and its diagnostic performance was compared to that for other diseases. Lastly, the SERS detection of the metabolite of P. aeruginosa bacterium directly in complex matrixes of saliva and artificial sputum using easily prepared SERS substrates was presented

NEGATIVE ION PHOTOELECTRON SPECTROSCOPY: ANTIOXIDANTS, ACTINIDE CLUSTERS, MOLECULAR ACTIVATION, AND SUPERATOMS

Negative ion photoelectron spectroscopy (PES) utilizes photons to examine the electronic and geometric properties of negative ions and their corresponding neutrals. A diverse range of topics spanning biology, chemistry, physics, and material science were investigated, including antioxidation abilities of antioxidants, electronic structure of actinide-containing clusters, mechanism of activation reactions, design of superatoms, multiple Rydberg anions, and electron induced proton transfer. The insight acquired from anion photoelectron spectroscopy has provided understanding into the above-mentioned topics at a molecular level. After briefly introducing the PES technique, Chapters II to VI present these studies in detail. In Chapter II, the antioxidation abilities of two famous antioxidants in the body and food, ascorbic acid and gallic acid, were measured spectroscopically and compared to computations. In Chapter III, we studied the interactions of hydrogen, oxygen, or gold atoms with thorium or uranium atoms; chemical bonding between thorium and thorium atoms in clusters; and electron affinity of the uranium atom. Chapter IV discusses the small molecule activation, such as water, carbon dioxide, methane, or hydroxylamine, by single metal anions or metal hydride anions. With the help of high-level quantum chemistry calculations, reaction mechanisms were revealed at a molecular level. Finally, Chapter V shows that the electronic spectra in cobalt sulfide superatomic clusters are tunable via ligand substitution, shedding light on novel material designs

Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces

Heterogeneous catalysis is an area of active research, because many industrially relevant reactions involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the field has become more intense due to the development of surface science and simulation techniques that allow for acquiring deeper insight into these catalysts, with the goal of producing more active, cheaper and less toxic catalytic materials. One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi- mental results by providing an atom-by-atom representation of the system. This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate potentials that would allow for reaching larger systems and longer simulated time scales. A promising possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the tools of machine learning to reproduce the potential energy surface of a system under study, as sampled by an expensive electronic structure reference method of choice. One early and fruitful example of such machine learning force fields are neural network potentials, as initially developed by Behler and Parrinello. In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for the industrial catalyst. This potential was subsequently utilized to perform a number of simulations. Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and a number of structural trends observed. These clusters revealed the first hints of the structure of the Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500 atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the support. Additionally, in these simulations the effect of strain induced by the support can be observed, with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports. Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer. This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de- posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed. Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and curating of reference sets for the neural network potential, and can also be used to evaluate the quality of atomic descriptor sets.2021-10-0