Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra

Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitative step of evaluating diffraction spectra. Subsequent to phase identification, Rietveld refinement may be employed to extract the abundance of quantitative, material-specific parameters hidden within powder data. These characterization procedures are yet time-consuming and inhibit efficiency in materials science workflows. The ever-increasing popularity and propulsion of data science techniques has provided an obvious solution on the course towards materials analysis automation. Deep learning has become a prime focus for predicting crystallographic parameters and features from X-ray spectra. However, the infeasibility of curating large, well-labelled experimental datasets means that one must resort to a large number of theoretic simulations for powder data augmentation to effectively train deep models. Herein, we are interested in conventional supervised learning algorithms in lieu of deep learning for multi-label crystalline phase identification and quantitative phase analysis for a biomedical application. First, models were trained using very limited experimental data. Further, we incorporated simulated XRD data to assess model generalizability as well as the efficacy of simulation-based training for predictive analysis in a real-world X-ray diffraction application

Inositol phosphate pathway evolution and synthesis in Dictyostelium discoideum

Inositol phosphates (InsPs) are polar water-soluble derivatives of the six- carbon cyclitol inositol. They are synthesized, through phosphorylation reactions, by kinases of four distinct families: IPK, IP5-2K, ITPK1 and PPIP5K, which are thought to be present across all eukaryotes. A pleiotropy of functions has been ascribed to InsPs, from nutrient storage as phytate (InsP6) in plant seeds to the regulation of energy metabolism for the highly phosphorylated inositol pyrophosphates (PP-InsPs). PP-InsPs were first identified and structurally described in the slime mould D. discoideum, in part due to the high concentrations of these molecules in amoeba. However, the amoeba knockout strains for the homologous enzymes synthesising PP-InsPs in humans (and yeast), IP6K (kcs1) and PPIP5K (vip1), do not present clear phenotypes. The work presented in this thesis expanded our biochemical understanding of these kinases by identifying additional isomers of inositol pyrophosphates and IpkA as the main source of InsP8 in the social amoeba. These findings shed light into the evolution of the inositol phosphate pathway and suggested an increased complexity of isomers and enzymes. Inositol phosphate kinase functions were identified in all domains of the tree of life as well as certain viruses, increasing our understanding of the origin and diversification of the inositol phosphate pathway

Automated computation of materials properties

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo

Aerospace Medicine and Biology: A continuing bibliography (supplement 160)

This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976

Novel hydroxyapatite/carboxymethylchitosan composite scaffolds prepared through an innovative ‘‘autocatalytic’’ electroless coprecipitation route

A developmental composite scaffold for bone tissue engineering applications composed of hydroxyapatite (HA) and carboxymethylchitosan (CMC) was obtained using a coprecipitation method, which is based on the ‘‘autocatalytic’’ electroless deposition route. The results revealed that the pores of the scaffold were regular, interconnected, and possess a size in the range of 20–500 lm. Furthermore, the Fourier transform infra-red spectrum of the composite scaffolds exhibited all the characteristic peaks of apatite, and the appearance of typical bands from CMC, thus showing that coprecipitation of both organic and inorganic phases was effective. The X-ray diffraction pattern of composite scaffolds demonstrated that calciumphosphates consisted of crystalline HA. From microcomputed tomography analysis, it was possible to determine that composite scaffolds possess a 58.9% 6 6% of porosity. The 2D morphometric analysis demonstrated that on average the scaffolds consisted of 24% HA and 76% CMC. The mechanical properties were assessed using compressive tests, both in dry and wet states. Additionally, in vitro tests were carried out to evaluate the wateruptake capability, weight loss, and bioactive behavior of the composite scaffolds. The novel hydroxyapatite/ carboxymethylchitosan composite scaffolds showed promise whenever degradability and bioactivity are simultaneously desired, as in the case of bone tissue-engineering scaffolding applications.Contract grant sponsor: European Union (STREP Project HIPPOCRATES); contract grant number: NMP3-CT-2003-50575

Recent advances in chemical sensors for soil analysis: a review

The continuously rising interest in chemical sensors' applications in environmental monitoring, for soil analysis in particular, is owed to the sufficient sensitivity and selectivity of these analytical devices, their low costs, their simple measurement setups, and the possibility to perform online and in-field analyses with them. In this review the recent advances in chemical sensors for soil analysis are summarized. The working principles of chemical sensors involved in soil analysis; their benefits and drawbacks; and select applications of both the single selective sensors and multisensor systems for assessments of main plant nutrition components, pollutants, and other important soil parameters (pH, moisture content, salinity, exhaled gases, etc.) of the past two decades with a focus on the last 5 years (from 2017 to 2021) are overviewed

Sol-gel derived hydroxyapatite, fluorhydroxyapatite and fluorapatite coatings for titanium implants

Currently, most titanium implant coatings are made using hydroxyapatite and a plasma-spraying technique. There are however limitations associated with the plasma-spraying process including; poor adherence, high porosity and cost. An alternative - the sol-gel technique offers many potential advantages but is currently lacking research data for this application. Hydroxyapatite (HA), fluorhydroxyapatite (FHA) and fluorapatite (FA) have been synthesised by a sol-gel method. Calcium nitrate and triethyl phosphite were used as precursors under an ethanol-water based solution. Different amounts of ammonium fluoride (NH4F) were incorporated for the preparation of the FHA and FA sol-gels. Optimisation and characterisation of the sol-gels was carried out using, X-ray Diffraction (XRD), High Temperature X-Ray Diffraction (HTXRD), Fourier Transform Infrared Analysis (FTIR) and Differential Thermal Analysis (DTA). Rheology and hydrophilicity of the sol-gels showed that increasing fluoride ion substitution caused an increase in viscosity and contact angle. The dissolution (Ca2+ and PO4 3-rates) rates of the fluoride-substituted powders from the sol-gels were considerably lower than that of HA and all rates could be decreased by increasing the sintering temperature. This suggests the possibility of tailoring the solubility of any coatings made from the sol-gels through fluoride ion substitution and increased sintering temperature. A spin coating protocol has been established for coating the sol-gels onto titanium. Increasing the coating speed decreased the porosity and thickness of the coatings. Bond strengths to titanium were investigated. Fluoride substitution and sintering temperature were shown to be important factors. Cellular proliferation studies revealed that increasing the level of fluoride substitution in the apatite structure significantly increased the biocompatibility of the material. The sol-gel technique may be an alternative to plasma spraying for coating titanium implants. Furthermore it may also be suitable for producing HA, FHA and FA as bone grafting materials