Structural studies of organic and organometallic compounds using x-ray and neutron techniques

This thesis is sub-divided into two parts. Part (i) is entitled 'Structure / Property Relationships in Non-linear Optical Materials' (chapters 1-8) whilst part (ii) is entitled 'Structural Studies of imido, (bis)imido and aryloxide group VA and VIA transition metal complexes' (chapters 9-10).Chapters 1, 2 and 3 provide an introduction to non-linear optics, X-ray and neutron experimental techniques used in this thesis and charge density studies respectively. Chapters 4 to 8 describe the investigations of the part (i) topic. These include bond length alternation studies on a series of tetracyanoquinodimethane derivatives and a charge density study of one of these compounds in chapter 4. Several other charge density studies are reported in chapters 5 and 6 which concentrate on methyl- nitropyridine and nitroaniline derivatives and the compound, 3-( 1,1 -dicyanoethenyl)-l-phenyl-4,5- dihydro-1 H-pyrazole (DCNP) respectively. Chapter 5 also deals with the effect of intermolecular interactions on the non-linear optical phenomenon whilst in chapter 6, a detailed analysis of the thermal motion present in DCNP is also given. Investigations on intermolecular interactions are also reported in chapters 7 and 8 which studies the compounds, N-methylurea and zinc(tris)thiourea sulphate respectively. In the former case, the neutron derived structure of N-methylurea is reported at two temperatures and it is revealed that disorder is present at the higher temperature. In the latter case, neutron results from an instrument presently in the testing stages of its development are reported and contrasted with those obtained using a well established instrument. Chapters 9 and 10 describe the investigations of the part (ii) topic. These concentrate on the structural features of two series of organometallic compounds which have potential use as polymerization catalysts. Relationships between structure and reactivity are reported

Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction.

Large auto-generated databases of magnetic materials properties have the potential for great utility in materials science research. This article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Néel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%. Therein, records processed with the text-mining toolkit, ChemDataExtractor, were assisted by a modified Snowball algorithm, whose original binary relationship extraction capabilities were extended to quaternary relationship extraction. Consequently, its machine learning component can now train with ≤ 500 seeds, rather than the 4,000 originally used. Data processed with the modified Snowball algorithm affords 82% precision. Database records are available in MongoDB, CSV and JSON formats which can easily be read using Python, R, Java and MatLab. This makes the database easy to query for tackling big-data materials science initiatives and provides a basis for magnetic materials discovery

SOARING Towards Positive Transformation and Change

The SOAR strategic thinking and planning framework is a dynamic, modern, and innovative approach for framing strategic thinking, assessing individual and team performance, building strategy, and creating strategic plans. SOAR stands for strengths, opportunities, aspirations, and results. As a framework, SOAR focuses on the formulation and implementation of a positive strategy by identifying strengths, building creativity in the form of opportunities, encouraging individuals and teams to share aspirations, and determining measurable and meaningful results. This article presents the SOAR framework’s evolution from the fields of strategy, organization development and change, and Appreciative Inquiry (AI) to the discipline of positive organizational scholarship (POS)

Preferred Molecular Orientation of Coumarin 343 on TiO2 Surfaces: Application to Dye-Sensitized Solar Cells.

The dye···TiO2 interfacial structure in working electrodes of dye-sensitized solar cells (DSCs) is known to influence its photovoltaic device performance. Despite this, direct and quantitative reports of such structure remain sparse. This case study presents the application of X-ray reflectometry to determine the preferred structural orientation and molecular packing of the organic dye, Coumarin 343, adsorbed onto amorphous TiO2. Results show that the dye molecules are, on average, tilted by 61.1° relative to the TiO2 surface, and are separated from each other by 8.2 Å. These findings emulate the molecular packing arrangement of a monolayer of Coumarin 343 within its crystal structure. This suggests that the dye adsorbs onto TiO2 in one of its lowest energy configurations; that is, dye···TiO2 self-assembly is driven more by thermodynamic rather than kinetic means. Complementary DSC device tests illustrate that this interfacial structure compromises photovoltaic performance, unless a suitably sized coadsorbant is interdispersed between the Coumarin 343 chromophores on the TiO2 surface.J. M.-G. acknowledges ANSTO for a part-funded PhD studentship. J. M. C. is grateful to the 1851 Royal Commission for the 2014 Design Fellowship, and Argonne National Laboratory where work done was supported by DOE Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acsami.5b0357

A database of battery materials auto-generated using ChemDataExtractor

Funder: University of Cambridge | Christ's College, University of Cambridge (Christ's College); doi: https://doi.org/10.13039/501100000590Abstract: A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database

Nanooptomechanical Transduction in a Single Crystal with 100% Photoconversion.

Materials that exhibit nanooptomechanical transduction in their single-crystal form have prospective use in light-driven molecular machinery, nanotechnology, and quantum computing. Linkage photoisomerization is typically the source of such transduction in coordination complexes, although the isomers tend to undergo only partial photoconversion. We present a nanooptomechanical transducer, trans-[Ru(SO2)(NH3)4(3-bromopyridine)]tosylate2, whose S-bound η1-SO2 isomer fully converts into an O-bound η1-OSO photoisomer that is metastable while kept at 100 K. Its 100% photoconversion is confirmed structurally via photocrystallography, while single-crystal optical absorption and Raman spectroscopies reveal its metal-to-ligand charge-transfer and temperature-dependent characteristics. This perfect optical switching affords the material good prospects for nanooptomechanical transduction with single-photon control

ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature.

The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present in figures, tables, and textual paragraphs, successful information extraction often depends on the ability to interpret all of these domains simultaneously. We present a complete toolkit for the automated extraction of chemical entities and their associated properties, measurements, and relationships from scientific documents that can be used to populate structured chemical databases. Our system provides an extensible, chemistry-aware, natural language processing pipeline for tokenization, part-of-speech tagging, named entity recognition, and phrase parsing. Within this scope, we report improved performance for chemical named entity recognition through the use of unsupervised word clustering based on a massive corpus of chemistry articles. For phrase parsing and information extraction, we present the novel use of multiple rule-based grammars that are tailored for interpreting specific document domains such as textual paragraphs, captions, and tables. We also describe document-level processing to resolve data interdependencies and show that this is particularly necessary for the autogeneration of chemical databases since captions and tables commonly contain chemical identifiers and references that are defined elsewhere in the text. The performance of the toolkit to correctly extract various types of data was evaluated, affording an F-score of 93.4%, 86.8%, and 91.5% for extracting chemical identifiers, spectroscopic attributes, and chemical property attributes, respectively; set against the CHEMDNER chemical name extraction challenge, ChemDataExtractor yields a competitive F-score of 87.8%. All tools have been released under the MIT license and are available to download from http://www.chemdataextractor.org

Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning

Abstract: Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications

Distinction of disorder, classical and quantum vibrational contributions to atomic mean-square amplitudes in dielectric pentachloronitrobenzene

The solid-state molecular disorder of pentachloronitrobenzene (PCNB) and its role in causing anomalous dielectric properties are investigated. Normal coordinate analysis (NCA) of atomic mean-square displacement parameters (ADPs) is employed to distinguish disorder contributions from classical and quantum-mechanical vibrational contributions. The analysis relies on multitemperature (5-295 K) single-crystal neutron-diffraction data. Vibrational frequencies extracted from the temperature dependence of the ADPs are in good agreement with THz spectroscopic data. Aspects of the static disorder revealed by this work, primarily tilting and displacement of the molecules, are compared with corresponding results from previous, much more in-depth and time-consuming Monte Carlo simulations; their salient findings are reproduced by this work, demonstrating that the faster NCA approach provides reliable constraints for the interpretation of diffuse scattering. The dielectric properties of PCNB can thus be rationalized by an interpretation of the temperature-dependent ADPs in terms of thermal motion and molecular disorder. The use of atomic displacement parameters in the NCA approach is nonetheless hostage to reliable neutron data. The success of this study demonstrates that state-of-the-art single-crystal Laue neutron diffraction affords sufficiently fast the accurate data for this type of study. In general terms, the validation of this work opens up the field for numerous studies of solid-state molecular disorder in organic materials.Comment: Now published in Physical Review