Piezoelectric properties of PVDF-conjugated polymer nanofibers

This study presents the development and characterization of PVDF-conjugated polymer nanofiber-based systems. Five different conducting polymers (CPs) were synthesized successfully and used to create the nanofiber systems. The CPs used are polyaniline (PANI), polypyrrole (PPY), polyindole (PIN), polyanthranilic acid (PANA), and polycarbazole (PCZ). Nanofiber systems were produced utilizing the Forcespinning® technique. The nanofiber systems were developed by mechanical stretching. No electrical field or post-process poling was used in the nanofiber systems. The morphology, structure, electrochemical and piezoelectric performance was characterized. All of the nanofiber PVDF/CP systems displayed higher piezoelectric performance than the fine fiber PVDF systems. The PVDF/PPY nanofiber system displays the highest piezoelectric performance of 15.56 V. The piezoelectric performance of the PVDF/CP nanofiber systems favors potential for an attractive source of energy where highly flexible membranes could be used in power actuators, sensors and portable, and wireless devices to mention some.     

Design,Synthesis, and Self-Assembly Studies of a Suite of Monodisperse,Facially Amphiphilic,Protein–Dendron Conjugates

The custom design of protein–dendron amphiphilic macromolecules is at the forefront of macromolecular engineering. Macromolecules with this architecture are very interesting because of their ability to self-assemble into various biomimetic nanoscopic structures. However, to date, there are no reports on this concept due to technical challenges associated with the chemical synthesis. Towards that end, herein, a new chemical methodology for the modular synthesis of a suite of monodisperse, facially amphiphilic, protein–dendron bioconjugates is reported. Benzyl ether dendrons of different generations (G1–G4) are coupled to monodisperse cetyl ethylene glycol to form macromolecular amphiphilic activity-based probes (AABPs) with a single protein reactive functionality. Micelle-assisted protein labeling technology is utilized for site-specific conjugation of macromolecular AABPs to globular proteins to make monodisperse, facially amphiphilic, protein–dendron bioconjugates. These biohybrid conjugates have the ability to self-assemble into supramolecular protein nanoassemblies. Self-assembly is primarily mediated by strong hydrophobic interactions of the benzyl ether dendron domain. The size, surface charge, and oligomeric state of protein nanoassemblies could be systematically tuned by choosing an appropriate dendron or protein of interest. This chemical method discloses a new way to custom-make monodisperse, facially amphiphilic, protein–dendron bioconjugates.     

V2O5/RGO/Pt nanocomposite on oxytetracycline degradation and pharmaceutical effluent detoxification

The cover image is based on the Research Article V₂O₅/RGO/Pt nanocomposite on oxytetracycline degradation and pharmaceutical effluent detoxification by Mohan, H et al., DOI: 10.1002/jctb.6238 .

     

Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.     

Intravaginal Controlled Administration of Flurogestone Acetate: (III) Development of Rate-Control Vaginal Devices

A Rate-Control vaginal device was developed which overcomes the low bioavailability and unpredictable Q - t^1/2 type release and absorption rate profiles of flurogestone acetate delivered by the currently marketed Syncro-Mate pessary.

The in vitro release and vaginal absorption profiles from the Rate-Control vaginal device were run simultaneously, a linear Q - t relationship was obtained with a significant improvement in bioavailability. A mathematical model was developed to correlate the in vitro drug release and the vaginal absorption profiles of flurogestone acetate from the vaginal devices.

The design, development and the simultaneous release and absorption profiles of flurogestone acetate from this new vaginal device were outlined and discussed.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

A systematic design method to obtain broadband characteristics for singly-fed electromagnetically coupled patch antennas for circular polarization

We present a systematic, empirical design technique to obtain optimum broadband impedance, axial-ratio (AR) and gain bandwidths for a singly-fed electromagnetically coupled patch antenna for circular polarization. Our investigation has also revealed tradeoffs amongst obtainable AR, impedance bandwidth and AR bandwidth. Using two design examples at different frequency bands and for different senses of circular polarization, we have demonstrated the effectiveness of the proposed knowledge-based tuning method. We have obtained at C-band measured values of impedance bandwidth (VSWR/spl les/2) equal to 43%, 3-dB AR bandwidth of 8%, AR of less than 0.3 dB and a mean gain level of 7 dB. For the Ku-band element, a 40% impedance bandwidth and a 17.3% of 3-dB AR bandwidth have been obtained with a peak gain of 7.2 dBic.