Impact of nanotube density and alignment on the elastic modulus near the top and base surfaces of aligned multi-walled carbon nanotube films

The mechanical compliance of vertically aligned carbon nanotube (VACNT) films renders them promising as interface materials that can accommodate thermal expansion mismatch. Here we study the relationship between the detailed morphology and elastic modulus of multi-walled VACNT films with thicknesses ranging from 98 to 1300 μm. A systematic analysis of scanning electron micrographs reveals variations in nanotube alignment and density among samples and within different regions of a given film. Nanoindentation of both top and bottom film surfaces using an atomic force microscope with spherical indenters with radii between 15 and 25 μm provides evidence of the modulus differences. The top surface is shown to have a higher modulus than the base, with out-of-plane modulus values of 1.0–2.8 MPa (top) and 0.2–1.4 MPa (base). The indentation data and microstructural information obtained from electron microscopy are interpreted together using an open cell foam model to account for differences in nanotube alignment and density, which are generally lower at the base and yield predictions that are consistent with the modulus data trends. This work shows that microstructure analysis complements property measurements to improve our understanding of nanostructured materials.     

One-step synthesis of α-p-vinylphenylalkyl-ω-hydroxy poly(ethylene oxide) macromonomers by anionic polymerization initiated from p-vinylphenylalkanols

ω-(p-Vinylphenyl)alkanols, including methanol, ethanol, propanol, pentanol, and hexanol, have been partially alkoxidated with potassium naphthalene to initiate anionic polymerization of ethylene oxide (EO) in order to directly prepare the corresponding α-p-vinylphenylalkyl-ω-hydroxy poly(ethylene oxide) (PEO) macromonomers. p-Vinylphenylmethanol, i.e. p-vinylbenzyl alcohol (VBA) afforded the expected well-defined macromonomer via living polymerization mechanism and the kinetics have been examined as a function of extent of potassium-alkoxidation. Other alcohols such as p-vinylphenylpropanol (VPP), -pentanol (VPPT), and -hexanol (VPH) were also successful to afford the corresponding PEO macromonomers, while p-vinylphenylethanol (VPE) alkoxide polymerized EO to give p-divinylbenzene and poly(ethylene glycol) without p-vinylphenylethoxy end group, which were supposed to form by a very facile intramolecular chain transfer of the activated oligomeric alkoxide chain end to abstract a benzylic proton of the initiating fragment.     

An efficient,chromatic clustering-based background model for embedded vision platforms

People naturally identify rapidly moving foreground and ignore persistent background. Identifying background pixels belonging to stable, chromatically clustered objects is important for efficient scene processing. This paper presents a technique that exploits this facet of human perception to improve performance and efficiency of background modeling on embedded vision platforms. Previous work on the Multimodal Mean (MMean) approach achieves high quality foreground extraction (comparable to Mixture of Gaussians (MoG)) using fast integer computation and a compact memory representation. This paper introduces a more efficient hybrid technique that combines MMean with palette-based background matching based on the chromatic distribution in the scene. This hybrid technique suppresses computationally expensive model update and adaptation, providing a 45% execution time speedup over MMean. It reduces model storage requirements by 58% over a MMean-only implementation. This background analysis enables higher frame rate, lower cost embedded vision systems.     

Real-Time Adaptive Background Modeling for Multicore Embedded Systems

Current trends in microprocessor design integrate several autonomous processing cores onto the same die. These multicore architectures are particularly well-suited for computer vision applications, where it is typical to perform the same set of operations repeatedly over large datasets. These memory- and computation-intensive applications can reap tremendous performance and accuracy benefits from concurrent execution on multi-core processors. However, cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. Furthermore, parallelization and partitioning techniques that allow the application to fully leverage the processing capabilities of each computing core are required for multi-core embedded vision systems. In this paper, we evaluate background modeling techniques on a multicore embedded platform, since this process dominates the execution and storage costs of common video analysis workloads. We introduce a new adaptive backgrounding technique, multimodal mean, which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level background modeling techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences, across a range of processing and parallelization configurations. We show that the multimodal mean algorithm delivers comparable accuracy of the best alternative (Mixture of Gaussians) with a 3.4× improvement in execution time and a 50% reduction in required storage for optimal block processing on each core. In our analysis of several processing and parallelization configurations, we show how this algorithm can be optimized for embedded multicore performance, resulting in a 25% performance improvement over the baseline processing method.     

Conducting Copolymers of 3-Methylthienyl Methacrylate and p-Vinylbenzyloxy Poly(ethyleneoxide) and Their Electrochromic Properties

Summary A random copolymer (CP) containing 3-methylthienyl methacrylate (MTM) and p-vinylbenzyloxy poly(ethyleneoxide) (PEO-VB) units was synthesized. Further graft copolymerization of CP with pyrrole (Py) and thiophene (Th) were achieved in H₂O - sodium dodecyl sulfate (SDS), H₂O - p-toluenesulphonic acid (PTSA) and acetonitrile (AN) - tetrabutylammonium tetrafluoroborate (TBAFB) solvent electrolyte couples via constant potential electrolyses. Characterization was performed by cyclic voltammetry (CV), nuclear magnetic resonance spectroscopy (NMR), and fourier transform infrared spectroscopy (FTIR). The morphologies of the films were examined by scanning electron microscopy (SEM). Conductivities of the samples were measured by using four-probe technique. Moreover, spectroelectrochemical and electrochromic properties of the copolymer obtained from thiophene were investigated by UV-Vis spectrometry and colorimetry.     

Moving away from trading on the margins: Economic empowerment of informal businesses through FinTech

While there have been increasing studies on the impact of financial technology (FinTech), limited research has explored how FinTech supports economic empowerment for informal businesses. Drawing on institutional logics and a case study of mobile money—a FinTech innovation—this study develops a model of mobile money-driven economic empowerment. We argue that this model is important to explain how those at the bottom of the economic pyramid, who are often neglected, use FinTech innovations to create and run informal businesses. Our findings and model explain the dynamics between logics, actors, and mobile money at three levels: regulatory, payments infrastructure, and informal economy. We identify three corresponding effects as outcomes of economic empowerment for informal businesses: greater access to start-up capital, new employment opportunities, and improved financial management. By illustrating these effects, our study contributes to a better understanding of how FinTech innovations offer a possible pathway to economic empowerment for informal businesses.