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.     

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.     

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.     

Investigating the wicking behavior of micro/nanofibrous core-sheath PET–PAN yarn modified by dimethyl 5-sodium sulfoisophthalate

The present research aims at imparting an improved wicking ability to the recycled multifilament yarns by wrapping composite nanofibers to attain an efficient material for filtration purposes. Therefore, polyacrylonitrile nanofibers containing dimethyl 5-sodium sulfoisophthalate nanoparticles were wrapped around the recycled poly(ethylene terephthalate) yarn by means of a novel electrospinning technique. Several tests were performed to investigate the parameters affecting wicking rise and moisture regain of the samples. Taguchi method was used in two separate designs (with or without nanoparticles). Some factors such as polymer solution concentration, mass ratio of nanoparticles, take-up rate, and number of filaments were considered as the variable factors while yarn wicking rate and moisture regain were the response factors. It was found that the hydrophilic nature of nanoparticles together with the ultrafine structure of nanofibers provide yarns with enhanced wicking properties. Although solution concentration is the predominant factor in wicking rate of the yarns containing nanoparticles, the most effective factor in wicking rise and moisture regain for other cases is the number of filaments. The mechanism of nanoparticle effect on fluidic jet during electrospinning process is explained by theory of nanofluids stability which has never been validated experimentally in previous research studies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48185.     

Thermal Response of Steel–Steel Composite Metal Foams under Small-Scale Torch-Fire Conditions

Steel–steel composite metal foam (S–S CMF) is a novel metal matrix composite material characterized by its high strength-to-weight ratio and unique mechanical and thermal properties. It is made up of hollow stainless-steel spheres, embedded in stainless steel matrix, with 65–70% air in the structure making it effective as an insulating material. S–S CMF is being explored for use in tank cars carrying hazardous materials (HAZMATs) as a potential partial replacement for conventional carbon steel and thermal insulating material currently being used. In this study, S–S CMF material is numerically and experimentally evaluated for its thermal protection performance. Experimental studies are conducted in scaled-down jet fire condition while numerical studies are conducted using fire dynamics simulator (FDS). Based on experimental and modeling results, as well as uncertainty studies, 13–15 mm thick S–S CMF ranging in density of about 2.5 g cc⁻¹ tested as novel structural/insulating material meets the acceptance criterion for small-scale simulated torch-fire testing. Further success is anticipated in future full-scale evaluation of 122 × 122 cm samples. The outstanding fire resistance and thermal protection of S–S CMF is attributed to the substantial volume of air trapped within the material, which correlates to its total density.     

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.