Simple preparation and characterization of nickel ferrite nanocrystals by a thermal treatment method

Nickel ferrite nanocrystals were prepared from an aqueous solution containing metal nitrates and poly (vinyl pyrrolidone) (PVP) as a capping agent. To stabilize the particles, they were thermally treated at various temperatures from 623 to 823 K at which calcination occurred, thereby stabilizing the particles, controlling the growth of the nanoparticles, preventing their agglomeration, and creating a uniform distribution of particle sizes. The characterization studies were conducted by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and transmission electron microscopy (TEM). The crystallization was completed between 723 and 823 K, as revealed by the absence of organic absorption bands in the FT-IR spectra Magnetization measurements were obtained at room temperature by using a vibrating sample magnetometer (VSM), which showed that the calcined samples exhibited ferromagnetic behaviors. Finally, we used TEM images and FT-IR spectra to investigate the same process in the absence of PVP and with various of concentrations of PVP for comparison with the results acquired from using the optimum concentration that was used in this work.     

Facial fabrication of self-healing natural rubber foam based on zinc thiolate ionic networks

In this work, self-healing natural rubber (SHNR) foam incorporating an intrinsic zinc thiolate ionic network was successfully prepared. The materials exhibited the ability to autonomously repair damage at room temperature without the need for external triggers. The investigation focused on the effect of sodium bicarbonate, employed as a blowing agent, on the self-healing performance, as well as the physical and mechanical properties of the foam. Various concentrations of sodium bicarbonate (0, 1, 4, 8, and 10 phr) were employed. The conventional two-roller mill was used for mixing and compounding, while compression molding was utilized for the vulcanization process. With increasing sodium bicarbonate concentration, the density, tensile strength, elongation at break, and compression set of the self-healing NR foam were found to decreased. Conversely, the porosity, shrinkage, compressive strength, and water uptake of the SHNR foam increased as the concentration of sodium bicarbonate increased. Scanning electron microscopy analysis revealed that the optimal concentration of sodium bicarbonate (8 phr) resulted in smaller, finer, and more uniform porous structures. The self-healing rubber foam incorporating 8 phr sodium bicarbonate exhibited improved properties in terms of tensile modulus, elongation at break, and tear strength, with healing efficiencies of 91.27%, 69.39%, and 83.99%, respectively.     

Artificial neural network‐based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield

This article presents an artificial neural network (ANN)‐based approach for predicting photovoltaic (PV) module temperature using meteorological variables. The proposed approach utilizes actual hourly records of various meteorological parameters, such as ambient temperature T_a, solar irradiation G, relative humidity RH, and wind speed Ws as input variables. The hourly meteorological data were collected over 9 months in the year 2009 from a 92‐kWp installed PV system in Selangor, Malaysia. The data were divided into two sets: training data, which are a set of 1849 (April–October) hourly data, and 578 (November–December) hourly records of working as test data. Four ANN models have been developed by using different combination of meteorological parameters as inputs, and, for each model, the output is the PV module temperature T_m. It was found that the model using all parameters, including RH and Ws as inputs, gave the most accurate results with correlation coefficient (r) 95.9%, and 0.41, 0.1, and 4.5% for MBE, RMSE, and MPE, respectively. To show the superiority and applicability of the developed ANN model, results from the proposed ANN model have been compared with the conventional model adopted by Malaysia Energy Center and another mathematical model based on regression. With the model's simplicity, the proposed approach can be used as an effective tool for predicting the PV module temperature, for any type of PV systems, in remote or rural locations with no direct measurement equipments. The developed model also will be very useful in studying PV system performance and estimating its energy output. Copyright © 2013 John Wiley & Sons, Ltd.     

Green synthesis of α-Fe2O3 nanoparticles for photocatalytic application

In this study, the preparation of α-Fe₂O₃ nanoparticles using curcuma and tea leaves extract are reported. The curcuma and tea leaves are acted as a reductant and stabilizer. The crystal structure and particle size of the as-synthesized materials were measured through X-ray diffraction. X-ray diffraction patterns revealed that the as-prepared samples were α-Fe₂O₃ nanoparticles with well-crystallized rhombohedral structure and the crystallite sizes of the α-Fe₂O₃ nanoparticles are 4 and 5 nm. Scanning electron microscopy images showed that the prepared samples have spherical shape. The purity and properties of the as-synthesized α-Fe₂O₃ nanoparticles were measured by Raman spectroscopy. The chemical compositions of the as-prepared α-Fe₂O₃ nanoparticles have been analyzed by Fourier transform infrared spectroscopy. The absorption edge of the α-Fe₂O₃ nanoparticles are 561 and 551 nm. The photocatalytic activity of the α-Fe₂O₃ nanoparticles was measured by degradation of methylene orange and the α-Fe₂O₃ nanoparticles showed the excellent photocatalytic performance.     

Influence of filament-to-substrate distance on the spectroscopic,structural and optical properties of silicon carbide thin films deposited by HWCVD technique

Silicon carbide (SiC) thin films were deposited using hot wire chemical vapor deposition (HWCVD) technique from pure silane and methane gas mixture. The effect of filament distance to the substrate on the structural and optical properties of the films was investigated. Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), Raman scattering spectroscopy and UV–Vis–NIR spectroscopy were carried out to characterize SiC films. XRD patterns of the films indicated that the film deposited under highest filament-to-substrate distance were amorphous in structure, while the decrease in distance led to formation and subsequent enhancement of crystallinity. The Si–C bond density in the film structure obtained from FTIR data, showed significant increment with transition from amorphous to nano-crystalline structure. However, it remained almost unchanged with further improvement in crystalline volume fraction. From Raman data it was observed that the presence of amorphous silicon phase and sp ² bonded carbon clusters increased with the decrease in distance. This reflected in deterioration of structural order and narrowing the optical band gap of SiC films. It was found that filament-to-substrate distance is a key parameter in HWCVD system which influences on the reactions kinetics as well as structural and optical properties of the deposited films.     

Foliar epidermal anatomy of some selected wild edible fruits of Pakistan using light microscopy and scanning electron microscopy

The present study is focused on the detailed foliar epidermal anatomy of some selected wild edible fruits (WEFs) from Pakistan using light microscopy (LM) and scanning electron microscopy (SEM). The studied species are Ficus racemosa L., Solanum nigrum L., Capparis spinosa L., Physalis divaricata D.Don, Rosa moschata Herrm. and Ribes orientale Desf. collected from various localities of Pakistan. The objective of the present study is to investigate qualitative and quantitative anatomical characters for the identification and differentiation of collected wild edible fruits. The characters studied are shape and size of epidermal cells, anticlinal wall pattern, trichome type and shape, average number of stomata, length and width of stomata and pore. The detailed microscopic investigation and variations in the characters recorded have a key role in the determination and authentication of wild edible fruits. This study possesses great potential for plant taxonomists to further evaluate the species at molecular and genetic levels.     

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer‐aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi‐type features extraction, fusion, and robust features selection. Five key steps are performed—database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16‐based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K‐Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets—Kvasir, CVC‐ClinicDB, Private, and ETIS‐LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%.     

The effect of some alloying elements on the corrosion resistance of lead-antimony alloys—I. Cadmium

The role played by Cd in lowering the corrosion rate of PbSb alloys has been investigated. It has been shown that 0·1%Cd reduces the steady state corrosion rate of PbSb by ca. 50%. This effect is discussed with respect to results obtained from galvanostatic experiments and from microscopic examination and microprobe analysis. Cd is effective through modification of the nucleation rate in the pores of the originally formed PbSO₄ film.     

Accumulation of arsenic in different fresh water fish species – potential contribution to high arsenic intakes

The aim of the study was to evaluate total arsenic (As) in five tissues (gills, mouthpiece, intestine, liver and muscles) of 10 fish species caught from As contaminated Manchar Lake (26°3′N: 67°6′E) Sindh Pakistan during 2006–2007. The total As concentration was determined by hydride generation atomic absorption spectrometry (HG-AAS), prior to microwave assisted acid digestion. The certified reference material DORM-2 (dogfish muscle) was used to check the quality control of the technique. The good agreement with the certified value at 95% confidence limit confirmed the validity of As determination method. The limit of detection (LOD) and limit of quantitation (LOQ) of As were 0.034 and 0.11 μg/g, respectively.     

Optimization of equilibrium headspace analysis of volatile flavor compounds of malaysian soursop (Annona muricata): Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS)

Headspace solid-phase microextraction (HS-SPME) coupled to comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) was applied for equilibrium headspace analysis of Malaysian soursop (Annona muricata) volatile flavor compounds. A two-level fractional factorial design (2^5-1) was used to determine the effect of SPME variables, namely, SPME fibers, adsorption temperature, extraction time, amount of salt, sample amount and sample concentration on the extraction efficiency of volatile flavor compounds. A total of 37 volatile compounds were identified, comprising 21 esters, 6 alcohols, 3 terpenes, 2 acids, 2 ketones, 2 aldehydes and an aromatic with different hydrophobicities (log P) ranging between −0.14 and 4.83. Extraction using 10 g of diluted (5% w/w) blended soursop pulp with CAR/PDMS fiber at 25 °C for 30 min and 30% (w/w) of NaCl under stirring mode resulted in the highest extraction efficiency of volatile flavor compounds. The principal component analysis score discriminated the influence of SPME variables on the equilibrium headspace concentration of target volatile compounds.