Design,Facile Synthesis and Characterization of Porphyrin-Zirconium-Ferrite@SiO2 Core-Shell and Catalytic Application in Cyclohexane Oxidation

Silicon - In this study, a new magnetic ZrFe2O4@SiO2-TCPP nanocatalyst with high efficiency was used for the oxidation of cyclohexane to cyclohexanone (Ke) and cyclohexanol (Al). The mesoporous...     

Using plasmonic cloaking method on infinite cylindrical structures and its applications

Journal of Computational Electronics - In recent years, cloaking using materials with negative electric permittivity or magnetic permeability has been studied and researched. It has been...     

Feasibility Study of Microwave-Assisted Biodiesel Production from Vegetable Oil Refinery Waste

Vegetable oil refinery waste containing acid oil is used as an inexpensive feedstock for producing biodiesel by microwave-assisted esterification (MAE) method. Effects of some main variables such as free fatty acid:methanol molar ratio (1:1, 1:5, and 1:10), reaction time (5, 30, and 60 min), and catalyst concentration (1%, 2%, and 3%) on physicochemical properties of produced biodiesel are investigated. Optimum reaction conditions of MAE are free fatty acid:methanol molar ratio of 1:10, reaction time of 60 min, and a catalyst concentration of 3%, while having 95.79% conversion yield. By increasing the conversion yield of the biodiesel, density and color brightness increase, while viscosity and refractive index decrease. There are no significant differences between physicochemical and heating properties of biodiesel produced by MAE and magnetic stirrer esterification (MSE) methods. Meanwhile, energy consumption of MAE method is almost four times lower than that of MSE. MAE as a promising alternative to the conventional esterification method can be considered as an energy-efficient method for producing biodiesel from inexpensive vegetable oil refinery waste. Practical applications : Acid oil is an inexpensive by-product of alkali refining in vegetable oil plants that would pollute the environment if not rendered safely. In this study, MAE is used to convert acid oil to biodiesel as a practical process for bringing alkali refining waste into production cycle. Acid oil can provide a reduction in the cost of biodiesel production. In addition, application of energy-efficient MAE method can facilitate the economical production of biodiesel.     

Electronic transport behavior of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (a bicyclic aziridine) as optical molecular switch: a first-principles approach

Journal of Computational Electronics - In this study, the electronic transport properties of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (as a bicyclic aziridine) have been...     

Magnetic solid phase extraction of copper from aquatic samples by Fe3O4/SiO2 nanoparticles followed by atomic absorption spectrometric determination

A magnetic solid phase extraction was developed for preconcentration of Cu(II) as a 4-bromo-2-(2,4-dichloro-phenylimino)-phenol complex on Fe₃O₄/SiO₂ nanoparticles prior to its determination by atomic absorption spectroscopy. The effect of different parameters influencing the formation of the Cu complex and the extraction efficiency of the Cu complex were studied. Under optimal conditions, the calibration graph showed linearity in the range of 0.946–700 µg L^?1 (R² = 0.9995) of Cu(II). The limit of detection and relative standard deviation of the method (n = 6) were 0.283 µg L^?1 and 3.0%, respectively. The method was successfully applied to determine copper (II) in water samples.     

Field-effect transistors assembled from functionalized carbon nanotubes

We have fabricated field-effect transistors from carbon nanotubes using a novel selective placement scheme. We use carbon nanotubes that are covalently bound to molecules containing a hydroxamic acid functionality. The functionalized nanotubes bind strongly to basic metal oxide surfaces, but not to silicon dioxide. Upon annealing, the functionalization is removed, restoring the electronic properties of the nanotubes. The devices thus fabricated show excellent electrical characteristics.     

BeWell: Sensing Sleep,Physical Activities and Social Interactions to Promote Wellbeing

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Flexible Prime‐Field Genus 2 Hyperelliptic Curve Cryptography Processor with Low Power Consumption and Uniform Power Draw

This paper presents an energy‐efficient (low power) prime‐field hyperelliptic curve cryptography (HECC) processor with uniform power draw. The HECC processor performs divisor scalar multiplication on the Jacobian of genus 2 hyperelliptic curves defined over prime fields for arbitrary field and curve parameters. It supports the most frequent case of divisor doubling and addition. The optimized implementation, which is synthesized in a 0.13 μm standard CMOS technology, performs an 81‐bit divisor multiplication in 503 ms consuming only 6.55 μJ of energy (average power consumption is 12.76 μW). In addition, we present a technique to make the power consumption of the HECC processor more uniform and lower the peaks of its power consumption.