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.     

The efficiency of private and public schools in urban and rural areas: moving beyond the development goals

Data from the Young Lives study are used to evaluate the efficiency of education systems in four low and middle income countries: Ethiopia, India, Peru and Vietnam. A meta‐frontier variant of data envelopment analysis is used to assess the relative performance of each country's system, and, within each country, to evaluate the impact of public and private schooling, and of urban and rural location. Comparisons are drawn between the four countries; the results indicate that in no country does the educational system perform uniformly badly or well. Conditioning on the inputs available, rural areas are often indicative of higher levels of efficiency, thus suggesting a number of implications for policy.     

Unified modeling technique for high-viscosity,melt-phase,condensation polymer reactors

Mathematical models for chemical reaction and mass transfer occurring in the manufacture of high-viscosity condensation polymers are considered. A preliminary study indicates that several diverse models can be represented using a single formula based upon an effectiveness factor. The effectiveness factor is shown to depend upon a ratio of time scales of mixing and reaction. The formula giving the effectiveness factor in terms of the time scales ratio is shown to depend upon mixing assumptions only. Starting with a mixing-cell model, a new modeling framework is developed and shown to include the previous models as special cases. The framework is free of inherent mixing assumptions and can be applied to a wide variety of situations once the mixing characteristics are specified. © 1993 John Wiley & Sons, Inc.     

Microwave-Assisted Synthesis of Fine Particle Oxides Employing Wet Redox Mixtures

Interaction of electromagnetic radiation with a physical mixture of metal nitrates and amides/hydrazides is observed to initiate high-temperature reactions, useful for realizing several high-temperature ceramic materials. A judicious choice of such redox mixtures undergoes exothermic reactions when they couple with microwave radiation. The coupling of electromagnetic radiation with metal salts and amides/hydrazides depends on the dielectric properties of the individual components in the reaction mixture. The approach has been used to prepare γ-Fe₂O₃, Fe₃O₄, MgCr₂O₄, α-CaCr₂O₄, and La_0.7Ba_0.3MnO₃.     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...     

Artificial neural network based system identification and model predictive control of a flotation column

The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded over a period of time. The data so generated is used to develop a dynamic feed forward neural network model. The model uses two past values and one present value of the tailings valve opening as well as interface level as inputs and predicts the future interface level. This model is used for the design of a model predictive controller to control the interface level. The controller was tested both for liquid–gas system as well as liquid–gas–solid system and was found to perform very satisfactorily. The performance of the controller was compared with that of a conventional PI controller for a two-phase system and was found to be better.     

Essential oil nanoemulsions: antibacterial activity in contaminated fruit juices

Many human acid tolerant bacterial and fungal pathogens can be transmitted through the consumption of the contaminated fruit juices. We aim to formulate essential oil nanoemulsions (basil, black seed, turmeric, clove & cinnamon), determine their ability to clear contamination by food borne bacterial pathogens from fruit juices. The antibacterial activity of the optimised formulations was tested in the fruit juices against bacterial pathogens causing gastrointestinal tract infections. The minimum bactericidal concentration (MBC) of clove emulsions ranged from 15.6 to 25 μL mL⁻¹. Cinnamon oil emulsion had an MBC ranging between 15 and 31 μL mL⁻¹. At MBC, cinnamon oil emulsions caused a 6log₁₀ decrease in viable counts by 8 h and maintained the sterility of fruit juices for 7 days at ambient temperature. Thus, clove and cinnamon microemulsions can be used as juice additives to control food borne bacterial pathogens and maintain the bacterial sterility of fruit juices.     

White graphene based composite proton exchange membrane: Improved durability and proton conductivity

Boron nitride, which is also known as “white graphene” may be an attractive filler for composite proton exchange membrane. Application of polymer electrolyte membranes in fuel cell as an electrolyte is gaining attention due to the requirement of clean energy. However, despite its attractive features it requires more consideration for complete commercialization. Herein we demonstrate the preparation of novel functionalized WHITE GRAPHENE (hexagonal boron nitride) and sulfonated poly ether sulfone (SPES) based polymer electrolyte membranes (PEM). Composite membranes have been characterized through thermal, mechanical, structural analysis. Membranes have been subjected to measure methanol permeability and proton conductivity at different temperatures for its use in DMFC. Composite membranes exhibit good physicochemical properties as well as high methanol crossover resistance. 0.5 wt % of FBN (SP-FBN-05) membrane is found to be adequate to get the better performance in DMFC.     

Effect of heating and acidic pH on characteristics of wheat gluten suspension

The effects of different pH treatments with and without heating on the characteristics of wheat gluten suspension was investigated. At pH 1, maximum changes in colour were observed with a concurrent 65% decrease in protein free-thiol content compared to the control gluten. The SDS-Extractability of protein (SDS-EP) chromatogram eluted at lower retention time and the presence of bands at the top lane even during reducing conditions in SDS-PAGE gel suggested complex formation involving bonds other than disulphides. An increase in the free-amino group content and the presence of an additional peak at a higher retention time in the SDS-EP chromatogram was suggestive of hydrolysis. At pH 2 and 3, similar decreases in SDS-EPs and free-thiol content indicated formation of complexes. When heated, the free-thiol content of the dispersions increased compared to the non-heated dispersions indicating disruption of disulphide bonds with changes in gluten structure and size distribution.     

Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.