Dumont M. Jones

Crystallization behaviour and morphological features of LARC-CPI

This work describes the crystallization behaviour and morphological features of the semicrystalline polyimide known as LARC-CPI. The study considers several features affecting crystallization behaviour such as inherent viscosity, crystallization temperature, melt temperature and time in the melt. Crystallization kinetics data were analysed using an Avrami analysis in conjunction with differential scanning calorimetry. Complications encountered when evaluating the crystallization exotherm for this analysis are also discussed. Morphological features were investigated using several techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), wide-angle X-ray scattering (WAXS), and small-angle X-ray scattering (SAXS). A permanganic etching technique combined with SEM successfully revealed morphological detail further supported by TEM results. This technique is also used to estimate the isothermal linear crystalline growth rate, G. The SEM and TEM data lend support to the Avrami data obtained by crystallization kinetics. SAXS results reflect the influence of crystallization temperature on the long spacing. SAXS also reveals the presence of a broad second scattering peak for semicrystalline samples which appears in the same position regardless of crystallization temperature or inherent viscosity. Molecular modelling predicts a low-energy helical conformation with a near-periodic repeat distance corresponding to that of the second SAXS peak. The periodic character of this conformation is offered as a hypothesis for that peak. This work also considers the effect of potential nucleating agents on the recrystallizability of a higher inherent viscosity LARC-CPI. One of the several nucleators investigated appears effective in enhancing the bulk crystallization rate.

Design Rules for Ce-Activated Scintillating Radiation Detection Materials: Compromises Between Luminosity and Stopping Power

This paper presents new development methods for property-screening design rules, using structure-property relationships for two fundamental properties of activated scintillating based gamma radiation detection-luminosity and stopping power. The first and most evident goal in developing screening models of luminosity and stopping power, as indicated by the weight and electron densities, is to obtain new candidate cerium scintillating materials. However, a second and more strategic goal is to extract design rules, which define the structural limitations on materials consistent with desirable detector properties. These design rules are based on our capability to predict the luminescence and stopping power of a material from a set of structural descriptors. Predictive models are generated using statistical multiple linear regression over a set of 24 descriptors. We find that within a set of ten cerium-doped scintillator materials we can quantitatively predict luminosity with a correlation coefficient of ~0.94 based on 4 of the 24 descriptors, improving to ~0.99 with 6 descriptors; and electron density to ~0.99 with 3 descriptors. Furthermore, we show in this circumstance that the luminosity and stopping power are only nominally related. In particular, luminosity depends largely on matrix valence electron properties and their coupling to activator sites-properties that do not require high atomic masses or atomic numbers per se, requirements for high stopping power.

Semiconducting material property relationships: trends and their impact on design of radiation detection materials

Materials properties important to the design and performance of semiconducting gamma detectors, such as band gap, density, mobility, and crystal cell anisotropy, can depend on similar underlying physics. The resulting property correlations limit the number of design variable and the place effective bounds on the range of physical properties available to gamma-detection materials However, trend correlations can also limit the dependence of error in structure-property relationships and information gaps when considering new candidate materials. Trend analysis complements property estimation via data regression techniques, increasing the generality and certainty of information-based conclusions.

Generic materials property data storage and retrieval for the semiconducting materials knowledge base

Informatics-based identification of candidate semiconducting radiation detection materials depends upon the development of a robust knowledge base of materials properties. However, the accuracy and integrity of the knowledge base are often affected by information loss due to incomplete entry and loss of context. We describe our methods for materials property data storage and retrieval, in support of semiconductor development for gamma radiation detection materials informatics applications. Analysis-ready data representations vary with each materials design problem, and are often inconsistent with accurate generic property storage. The proposed approach provides simple, strongly-typed generic storage for as-measured properties, with tools for assessing as-measured properties and converting them to analysis-ready representations. This process simplifies property data stewardship, and allows fine control over the assumptions of data fusion, system characterization, and property representation employed in property-estimation models.

Data-Driven Exploration of the Ionization-Phonon Partitioning in Scintillating Radiation Detector Materials

An information-based approach to scintillating materials development has been applied to ranking the alkali halide and alkaline earth halide series in terms of their energy conversion efficiency. The efficiency of scintillating radiation detection materials can be viewed as the product of a consecutive series of electronic processes (energy conversion, transfer, and luminescence) as outlined by Lempicki and others. Relevant data are relatively sparse, but sufficient for the development of forward mapping of materials properties through materials signatures. These mappings have been used to explore the limits of the branching ratio between the ionization and phonons (K) in the Lempicki model with chemical composition, and examine its relationship with another common design objective, density. The alkali halides and alkaline earth halide compounds separate themselves into distinct behavior classes favoring heavier cations and anions for improved values of the K ratio. While the coupling of ionization is strongly related to the optical phonon modes, both dielectric and band gap contributions cannot be ignored. When applied as a candidate screen, the resulting model for K suggests design rules - simple structural restrictions - on scintillating radiation detector materials.

Trade-Offs and Compromises in Carrier Properties of Semiconducting Materials

While carrier transport properties are critical to semiconductor efficiency, estimations for new materials based upon prior mobility measurements can be problematic. As with all new-materials screens, carrier transport screens must be based only on properties readily available prior to synthesis, such as composition. For semiconducting radiation detectors, transport is characterized by the mu-tau product and its carrier mobility (mu) and lifetime (tau) components. Because the time to pure-material synthesis is generally long, and due to the associated problems with fully-characterizing impure and defect-containing early-stage materials, it is advantageous to consider “ultimate” properties appropriate to the projected performance of a more advanced material. Here, ultimate properties and their application to materials screening of electron mobilities of semiconductors is discussed within the context of optical polaron scattering. The use of ultimate properties for electron mobility and lifetime in screening semiconducting radiation detectors is assessed to determine whether required inputs for electron mobility and carrier lifetime are likely to be accessible to screenable form for new-materials.

Materials Discovery: Informatic Strategies for Optical Materials

Information-based materials discovery offers a structured method to evolve materials signatures based upon their physical properties, and to direct searches using performance-based criteria. In this current paper, we focus on the crystal structure aspects of an optical material and construct an information-based model to determine the proclivity of a particular AB composition to exhibit multiple crystal system behavior. Exploratory data methods used both supervised (support-vector machines) and unsupervised (disorder-reduction and principal-component) classification methods for structural signature development; revealing complementary valid signatures. Examination of the relative contributions of the materials chemistry descriptors within these signatures indicates a strong role for Mendeleev number chemistry which must be balanced against the cationic/anionic radius ratio and electronegativity differences of constituents within the unit cell.

Information-Based Development of New Radiation Detection Materials

With our present concern for a secure environment, the development of new radiation detection materials has focused on the capability of identifying potential radiation sources at increased sensitivity levels. As the initial framework for a materials-informatics approach to radiation detection materials, we have explored the use of both supervised (Support Vector Machines – SVM and Linear Discriminant Analysis – LDA) and unsupervised (Principal Component Analysis – PCA) learning methods for the development of structural signature models. Application of these methods yields complementary results, both of which are necessary to reduce parameter space and variable degeneracy. Using a crystal structure classification test, the use of the nonlinear SVM significantly increases predictive performance, suggesting trade-offs between smaller descriptor spaces and simpler linear models.