The role of new information and communication technologies (ICTs) in information and communication in science. A conceptual framework and empirical study

Problem This dissertation presents a literature-based framework for communication in science (with the elements partners, purposes, message, and channel), which it then applies in and amends through an empirical study of how geoscientists use two social computing technologies (SCTs), blogging and Twitter (both general use and tweeting from conferences). How are these technologies used and what value do scientists derive from them? Method The empirical part used a two-pronged qualitative study, using (1) purposive samples of ~400 blog posts and ~1000 tweets and (2) a purposive sample of 8 geoscientist interviews. Blog posts, tweets, and interviews were coded using the framework, adding new codes as needed. The results were aggregated into 8 geoscientist case studies, and general patterns were derived through cross-case analysis. Results A detailed picture of how geoscientists use blogs and twitter emerged, including a number of new functions not served by traditional channels. Some highlights: Geoscientists use SCTs for communication among themselves as well as with the public. Blogs serve persuasion and personal knowledge management; Twitter often amplifies the signal of traditional communications such as journal articles. Blogs include tutorials for peers, reviews of basic science concepts, and book reviews. Twitter includes links to readings, requests for assistance, and discussions of politics and religion. Twitter at conferences provides live coverage of sessions. Conclusions Both blogs and Twitter are routine parts of scientists' communication toolbox, blogs for in-depth, well-prepared essays, Twitter for faster and broader interactions. Both have important roles in supporting community building, mentoring, and learning and teaching. The Framework of Communication in Science was a useful tool in studying these two SCTs in this domain. The results should encourage science administrators to facilitate SCT use of scientists in their organization and information providers to search SCT documents as an important source of information.

POLYMER-NANOPARTICLE COMPOSITES FOR APPLICATIONS IN FLEXIBLE ELECTRONICS

The aim of this dissertation was to investigate flexible polymer-nanoparticle composites with unique magnetic and electrical properties. Toward this goal, two distinct projects were carried out. The first project explored the magneto-dielectric properties and morphology of flexible polymer-nanoparticle composites that possess high permeability (µ), high permittivity (ε) and minimal dielectric, and magnetic loss (tan δε, tan δµ). The main materials challenges were the synthesis of magnetic nanoparticle fillers displaying high saturation magnetization (Ms), limited coercivity, and their homogeneous dispersion in a polymeric matrix. Nanostructured magnetic fillers including polycrystalline iron core-shell nanoparticles, and constructively assembled superparamagnetic iron oxide nanoparticles were synthesized, and dispersed uniformly in an elastomer matrix to minimize conductive losses. The resulting composites have demonstrated promising permittivity (22.3), permeability (3), and sustained low dielectric (0.1), magnetic (0.4) loss for frequencies below 2 GHz. This study demonstrated nanocomposites with tunable magnetic resonance frequency, which can be used to develop compact and flexible radio frequency devices with high efficiency. The second project focused on fundamental research regarding methods for the design of highly conductive polymer-nanoparticle composites that can maintain high electrical conductivity under tensile strain exceeding 100%. We investigated a simple solution spraying method to fabricate stretchable conductors based on elastomeric block copolymer fibers and silver nanoparticles. Silver nanoparticles were assembled both in and around block copolymer fibers forming interconnected dual nanoparticle networks, resulting in both in-fiber conductive pathways and additional conductive pathways on the outer surface of the fibers. Stretchable composites with conductivity values reaching 9000 S/cm maintained 56% of their initial conductivity after 500 cycles at 100% strain. The developed manufacturing method in this research could pave the way towards direct deposition of flexible electronic devices on any shaped substrate. The electrical and electromechanical properties of these dual silver nanoparticle network composites make them promising materials for the future construction of stretchable circuitry for displays, solar cells, antennas, and strain and tactility sensors.

POSITIVE FILTERED P_N METHOD FOR LINEAR TRANSPORT EQUATIONS AND THE ASSOCIATED OPTIMIZATION ALGORITHM

We propose a positive, accurate moment closure for linear kinetic transport equations based on a filtered spherical harmonic (FP_N) expansion in the angular variable. The FP_N moment equations are accurate approximations to linear kinetic equations, but they are known to suffer from the occurrence of unphysical, negative particle concentrations. The new positive filtered P_N (FP_N+) closure is developed to address this issue. The FP_N+ closure approximates the kinetic distribution by a spherical harmonic expansion that is non-negative on a finite, predetermined set of quadrature points. With an appropriate numerical PDE solver, the FP_N+ closure generates particle concentrations that are guaranteed to be non-negative. Under an additional, mild regularity assumption, we prove that as the moment order tends to infinity, the FP_N+ approximation converges, in the L2 sense, at the same rate as the FP_N approximation; numerical tests suggest that this assumption may not be necessary. By numerical experiments on the challenging line source benchmark problem, we confirm that the FP_N+ method indeed produces accurate and non-negative solutions. To apply the FP_N+ closure on problems at large temporal-spatial scales, we develop a positive asymptotic preserving (AP) numerical PDE solver. We prove that the propose AP scheme maintains stability and accuracy with standard mesh sizes at large temporal-spatial scales, while, for generic numerical schemes, excessive refinements on temporal-spatial meshes are required. We also show that the proposed scheme preserves positivity of the particle concentration, under some time step restriction. Numerical results confirm that the proposed AP scheme is capable for solving linear transport equations at large temporal-spatial scales, for which a generic scheme could fail. Constrained optimization problems are involved in the formulation of the FP_N+ closure to enforce non-negativity of the FP_N+ approximation on the set of quadrature points. These optimization problems can be written as strictly convex quadratic programs (CQPs) with a large number of inequality constraints. To efficiently solve the CQPs, we propose a constraint-reduced variant of a Mehrotra-predictor-corrector algorithm, with a novel constraint selection rule. We prove that, under appropriate assumptions, the proposed optimization algorithm converges globally to the solution at a locally q-quadratic rate. We test the algorithm on randomly generated problems, and the numerical results indicate that the combination of the proposed algorithm and the constraint selection rule outperforms other compared constraint-reduced algorithms, especially for problems with many more inequality constraints than variables.