7,035 research outputs found

    Surface Structure Of Hydrated And Iron(Ii) Reacted Hematite(11(-)02) And (0001)

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    Thesis (Ph.D.) University of Alaska Fairbanks, 2008Reactions on naturally abundant hematite (alpha-Fe2O 3) surfaces significantly influence the transport and bio-availability of a number of important nutrients and contaminants. The surface reactivity of alpha-Fe2O3 is dependent on the surface structure, i.e. the identity and coordination of chemical moieties exposed at the surface. The surface structure is strongly influenced by the presence of water and common aqueous species such as Fe(II). Therefore, it is important to understand how the surface structure evolves in the presence of water and aqueous species (e.g. Fe(II)) in order to model the surface reactivity of hematite in natural aquatic systems. The current study provides a detailed experimental investigation of the surface structure of two predominant natural faces of alpha-Fe2O 3, the (1102) and (0001) surfaces under hydrated conditions in absence and presence of aqueous Fe(II). The surface structure of hydrated alpha-Fe2O3(1102) prepared via a room-temperature wet chemical and mechanical polishing (CMP) procedure is consistent with a surface termination where the top layer of iron atoms is absent compared to the stoichiometric bulk termination. The annealing of CMP prepared alpha-Fe2O3(1 102) in air at 773 K results in transformation of the surface to a structure consistent with the stoichiometric termination. For CMP prepared alpha-Fe2O3(0001), the experimental results show a co-existence of two distinct structural domains on the surface. The first domain corresponds to hydroxylation of surface Fe atoms, and the second domain is formed by complete removal of the surface Fe cation leading to an exposed oxygen layer on the surface. The exposure of CMP prepared alpha-Fe2O3(1 102) and (0001) to aqueous Fe(II) results in structural modification of both surfaces due to adsorption of Fe(II) at crystallographic lattice sites followed by oxidation to Fe(III). Preliminary research conducted to identify the effect of Fe(II) induced surface modification on reactivity using Pb(II) as a reactive probe indicates that the clean and Fe(II)-modified surfaces exhibit significantly different reactivity towards Pb(II). Overall, the systematic structural characterization of hydrated and Fe(II)-modified alpha-Fe 2O3 surfaces presented in the current study will provide a basis to elucidate surface structure-reactivity relationships for hematite and will aid in developing models of mineral-water interfacial reactivity

    High-Resolution Crystal Truncation Rod Scattering: Application to Ultrathin Layers and Buried Interfaces

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    In crystalline materials, the presence of surfaces or interfaces gives rise to crystal truncation rods (CTRs) in their X‐ray diffraction patterns. While structural properties related to the bulk of a crystal are contained in the intensity and position of Bragg peaks in X‐ray diffraction, CTRs carry detailed information about the atomic structure at the interface. Developments in synchrotron X‐ray sources, instrumentation, and analysis procedures have made CTR measurements into extremely powerful tools to study atomic reconstructions and relaxations occurring in a wide variety of interfacial systems, with relevance to chemical and electronic functionalities. In this review, an overview of the use of CTRs in the study of atomic structure at interfaces is provided. The basic theory, measurement, and analysis of CTRs are covered and applications from the literature are highlighted. Illustrative examples include studies of complex oxide thin films and multilayers

    Comparison of Machine Learning Algorithms for Evaluating Building Energy Efficiency Using Big Data Analytics

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    © 2022, Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1108/jedt-05-2022-0238Purpose: This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach: This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings: Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications: While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications: This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value: This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.Peer reviewe

    Accelerated discovery of two crystal structure types in a complex inorganic phase field

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    The discovery of new materials is hampered by the lack of efficient approaches to the exploration of both the large number of possible elemental compositions for such materials, and of the candidate structures at each composition1. For example, the discovery of inorganic extended solid structures has relied on knowledge of crystal chemistry coupled with time-consuming materials synthesis with systematically varied elemental ratios2,3. Computational methods have been developed to guide synthesis by predicting structures at specific compositions4,5,6 and predicting compositions for known crystal structures7,8, with notable successes9,10. However, the challenge of finding qualitatively new, experimentally realizable compounds, with crystal structures where the unit cell and the atom positions within it differ from known structures, remains for compositionally complex systems. Many valuable properties arise from substitution into known crystal structures, but materials discovery using this approach alone risks both missing best-in-class performance and attempting design with incomplete knowledge8,11. Here we report the experimental discovery of two structure types by computational identification of the region of a complex inorganic phase field that contains them. This is achieved by computing probe structures that capture the chemical and structural diversity of the system and whose energies can be ranked against combinations of currently known materials. Subsequent experimental exploration of the lowest-energy regions of the computed phase diagram affords two materials with previously unreported crystal structures featuring unusual structural motifs. This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them

    Synthesis, Properties, and Solid-State Structures of a Series of 6,13-Dicyanoheteropentacene Analogues: Towards New Liquid Crystalline Materials

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    The focus of this thesis is the synthesis of novel heterocyclic pentacene analogs and the investigation of their self-organization for the development of new materials for organic electronics. The thesis consists of two interrelated projects: the first being development of an improved synthesis of a series of liquid crystalline dicyanotetraoxapentacenes (DCTOPs) while the second entails the exploratory synthesis of several novel dicyanoheteropentacene analogues and a preliminary investigation of their photophysical properties and solid-state structures. Both of these projects centre around the use of nucleophilic aromatic substitution reactions on tetrafluoroterephthalonitrile. Soluble, tetrakis(bis(alkoxy)phenyl)-substituted DCTOPs were originally synthesised via a short synthesis complicated by a tedious purification required in the last step. Despite this, derivatives bearing long alkyl chains were prepared which displayed liquid crystalline properties in addition to aggregation-induced emission. Building upon this success, but with the goal of achieving DCTOPs in an efficient synthetic manner for this thesis, changes were made which eliminated the troublesome fourfold Suzuki coupling by changing the order of reactions, which in turn required a protection-deprotection sequence. Purification in the new synthesis was greatly simplified and the target tetraaryl-DCTOPs were accessed in good overall yields and purities. The synthesis and solid state structures of these DCTOPs are discussed in Chapter 2. Building on the methods developed in Chapter 2, several novel pentacene analogues containing combinations of nitrogen, oxygen, and sulfur atoms installed within the pentacene core were also synthesised. These compounds were prepared in good yields, and preliminary photophysical studies show that all the compounds displayed luminescence in solution and the solid state. It was also shown that replacement of O with N leads to a red shift in absorption and emission spectra. The X-ray crystal structures show that several of these compounds exhibit π−stacking in the solid state, which is an important design element for applications in organic electronics. The synthesis, photophysical properties, and solid-state organization of these novel 6,13-dicyanoheteropentacene analogues are discussed in Chapter 3
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