Advanced Imaging Techniques for Point-Measurement Analysis of Pharmaceutical Materials

Drugs are an essential element protecting human lives from many diseases such as cancer, diabetes, and cardiovascular disorders. One of the highlights in drug development in recent years is the establishment of rational drug design: a collection of various multi-disciplinary approaches that at the core, focus on designing molecules with specific properties for identified targets and biomolecules with known functional roles and structural information. The candidate molecules will then go through a series of examinations to characterize their physiochemical properties, and an iterative process is used to improve the design of the drug to achieve desirable attributes. The time consuming and highly expensive nature of drug development constantly calls for new analytical techniques that have increasingly higher throughput, faster analysis speed, richer chemical and structural information, and lower risk and cost. Conventional analytical methods for pharmaceutical materials, such as X-ray diffraction analysis and Raman spectroscopy, often suffer from prolonged measurement time. In many cases, the identification of regions of interest within the sample is non-trivial in itself. Nonlinear optical imaging techniques, including second harmonic generation (SHG) microscopy and two-photon excited ultraviolet fluorescence (TPE-UVF) microscopy were developed as fast, real-time, and non-destructive methods for selective identification and characterization of crystalline materials present in pharmaceutical samples. These techniques were integrated with synchrotron X-ray diffraction analysis and Raman spectroscopy to significantly reduce the overall measurement time of these structure characterization techniques. In the meanwhile, with the now increased speed of measurement, the amount of experimental data acquired per unit time has also drastically increased. The rate at which data are analyzed, digested, and interpreted is becoming the bottleneck in data-driving decision-making. Novel electronics that only collect data at the most information-rich time points were employed to significantly increase the signal-to-noise ratio (SNR) during data acquisition, reducing the total amount of data needed for material characterization. Advanced sampling algorithms to reduce the total amount of measurements required for perfect data space reconstruction, automated programs for data acquisition and analysis, and efficient data analysis algorithms based on machine learning were developed for accelerated data processing for nonlinear optical imaging analysis, Raman spectra processing, and X-ray diffraction indexing

Quantitative electron microscopy for microstructural characterisation

Development of materials for high-performance applications requires accurate and useful analysis tools. In parallel with advances in electron microscopy hardware, we require analysis approaches to better understand microstructural behaviour. Such improvements in characterisation capability permit informed alloy design. New approaches to the characterisation of metallic materials are presented, primarily using signals collected from electron microscopy experiments. Electron backscatter diffraction is regularly used to investigate crystallography in the scanning electron microscope, and combined with energy-dispersive X-ray spectroscopy to simultaneusly investigate chemistry. New algorithms and analysis pipelines are developed to permit accurate and routine microstructural evaluation, leveraging a variety of machine learning approaches. This thesis investigates the structure and behaviour of Co/Ni-base superalloys, derived from V208C. Use of the presently developed techniques permits informed development of a new generation of advanced gas turbine engine materials.Open Acces

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We demonstrate it through typical examples including polycrystalline BaTiO$_3$, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Lab. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern by pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning prospective for the development of suitable feature extraction, clustering and labeling algorithms.Comment: 29 pages, 25 figures under the second round of review by Acta Crystallographica

Crystal Cartography: Mapping Nanostructure with Scanning Electron Diffraction

Nanostructure describes the network of defective and distorted atomic structure existing on the nanoscale within materials. This nanostructure bridges the gap between idealised crys- talline structure and real materials, playing a deterministic role in tailoring physico-chemical properties, as well as providing a basis for mechanistic understanding of complex processes such as mechanical deformation and phase transformation. Characterising nanostructure, to develop understanding of materials, requires experimental techniques capable of probing the structure with spatial resolution on the order of nanometres and across regions of interest up to micrometres. Recent developments in electron microscopy, enabling the acquisition of numerous diffraction patterns in a spatially resolved manner, combined with modern computational power, provides a route to meet this need as developed in this work. Scanning electron diffraction (SED) involves the acquisition of a two-dimensional elec- tron diffraction pattern at each probe position in a two-dimensional scan of a specimen. An information rich 4-dimensional (4D-SED) dataset is obtained that can be analysed extensively post-facto using a wide-range of computational methods. The acquisition of such 4D-SED data from the specimen at numerous orientations may also enable the reconstruction of nanostructure in three-dimensions via tomographic methods. In this work, methods for the acquisition and analysis of 4D-SED data are developed and applied to reveal nanostructure in two and three-dimensions. These methods are applied to various prototypical characterisation challenges in materials science, particularly: strain mapping in two and three dimensions, revealing inter-phase crystallographic relationships, mapping grains in two-dimensional materials, and probing nanostructure in polyethylene

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy

Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Application of Radial Distribution Functions to Diffraction and Imaging Data: Interfacial Structures, Amorphous, Disordered Materials

The central theme of this thesis is the application of radial and pair distribution function analysis to materials characterisation problems for nanotechnology. These concepts are introduced in Chapter 1, and the associated methods are described in Chapter 2. Chapter 3 details the first of the results which discusses the design and development of a software tool called ImageDataExtractor. This auto-extracts microscopy images and then analyses them to afford quantitative information regarding particles in a sample, such as shape, size and distribution. It realises an opportunity for data-mining the ubiquity of readily available images in the literature. Chapter 4 presents results of the development and execution of a novel experimental technique, called glancing-angle pair distribution function (gaPDF) analysis, applied to the structure of the working electrode in dye-sensitised solar cells (DSSCs). This structure was successfully observed, validating this novel method. The investigation also suggested preferred binding modes of the carboxylic acid anchoring groups present in this interfacial structure. Chapters 5 and 6 demonstrate the application of PDF analysis to synchrotron-based powder diffraction data of two material case studies: the rare earth phosphate glass (REPG) (Gd2O3)0.230(P2O5)0.770, and four Ru based photo-isomers. The closest R…R rare earth separation, which governs optical properties of REPGs, was determined to be 4.2(1) Å, aided by various statistical techniques. Analysis on four Ru-based photo-isomers confirmed: the existence of local structure in such compounds, their ability to be photo-isomerised in powder form, the theoretical models constructed using computational techniques, and the lack of heterogeneity in photo-isomerisation throughout a given light-induced sample. Chapter 7 concludes the work and offers a future outlook

Fabrication, characterization of high-entropy alloys and deep learning-based inspection in metal additive manufacturing

Alloying has been used to confer desirable properties to materials. It typically involves the addition of small amounts of secondary elements to a primary element. In the past decade, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high- entropy alloys (HEAs) has been in vogue. In the first part, the investigation focused on the fabrication process and property assessment of the additive manufactured HEA to broaden its engineering applications. Additive manufacturing (AM) is based on manufacturing philosophy through the layer-by-layer method and accomplish the near net-shaped components fabrication. Attempt was made to coat AlCoCrFeNi HEA on an AISI 304 stainless steel substrate to integrate their properties, however, it failed due to the cracks at the interface. The implementation of an intermediate layer improved the bond and eliminated the cracks. Next, an AlCoCrFeNiTi0.5 HEA coating was fabricated on the Ti6Al4V substrate, and its isothermal oxidation behavior was studied. The HEA coating effectively improved the Ti6Al4V substrate\u27s oxidation resistance due to the formation of continuous protective oxides. In the second part, research efforts were made on the deep learning-based quality inspection of additive manufactured products. The traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. A neural-network approach was developed toward robust real-world AM anomaly detection. The results indicate the promising application of the neural network in the AM industry --Abstract, page iv