High demands on low-voltage electronics have increased the need for zinc oxide (ZnO) varistors with fast response, highly non-linear current-voltage characteristics and energy absorption capabilities at low breakdown voltage. However, trade-off between breakdown voltage and grain size poses a critical bottle-neck in the production of low-voltage varistors. The present study highlights the synthesis mechanism for obtaining praseodymium oxide (Pr(6)O(11)) based ZnO varistor ceramics having breakdown voltages of 2.8 to 13.3 V/mm through employment of direct modified citrate gel coating technique. Precursor powder and its ceramics were examined by means of TG/DTG, FTIR, XRD and FESEM analyses. The electrical properties as a function of Pr(6)O(11) addition were analyzed on the basis of I-V characteristic measurement. The breakdown voltage could be adjusted from 0.01 to 0.06 V per grain boundary by controlling the amount of Pr(6)O(11) from 0.2 to 0.8 mol%, without alteration of the grain size. The non-linearity coefficient, α, varied from 3.0 to 3.5 and the barrier height ranged from 0.56 to 0.64 eV. Breakdown voltage and α lowering with increasing Pr(6)O(11) content were associated to reduction in the barrier height caused by variation in O vacancies at grain boundary.
The rapid and green formation of spherical and dendritic silver nanostructures based on microwave irradiation time was investigated. Silver nanoparticles were successfully fabricated by reduction of Ag(+) in a water medium and using polyvinylpyrrolidone (PVP) as the stabilizing agent and without the use of any other reducing agent, and were compared with those synthesized by conventional heating method. UV-vis absorption spectrometry, transmission electron microscopy (TEM), atomic absorption spectroscopy (AAS) and photon correlation spectroscopy (PCS) measurements, indicated that increasing the irradiation time enhanced the concentration of silver nanoparticles and slightly increased the particle size. There was a lack of large silver nanoparticles at a high concentration, but interestingly, the formation and growth of silver dendrite nanostructures appeared. Compared to conventional heating methods, the silver nanoparticle suspension produced by irradiated microwaves was more stable over a six-month period in aqueous solution without any signs of precipitation.
Thin and transparent films of doped cadmium sulfide (CdS) were obtained on commercial glass substrates by Chemical Bath Deposition (CBD) technique. The films were doped with low concentration of Sn, and annealed in air at 300 °C for 45 min. The morphological characterization of the films with different amounts of dopant was made using SEM and EDAX analysis. Optical properties of the films were evaluated by measuring transmittance using the UV-vis spectrophotometer. A comparison of the results revealed that lower concentration of Sn doping improves transmittance of CdS films and makes them suitable for application as window layer of CdTe/CIGS solar cells.
This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
This study focuses on the fabrication and electrical characterization of a polymer composite based on nano-sized varistor powder. The polymer composite was fabricated by the melt-blending method. The developed nano-composite was characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission scanning electron microscopy (FeSEM), and energy-dispersive X-ray spectroscopy (EDAX). The XRD pattern revealed the crystallinity of the composite. The XRD study also showed the presence of secondary phases due to the substitution of zinc by other cations, such as bismuth and manganese. The TEM picture of the sample revealed the distribution of the spherical, nano-sized, filler particles throughout the matrix, which were in the 10-50 nm range with an average of approximately 11 nm. The presence of a bismuth-rich phase and a ZnO matrix phase in the ZnO-based varistor powder was confirmed by FeSEM images and EDX spectra. From the current-voltage curves, the non-linear coefficient of the varistor polymer composite with 70 wt% of nano filler was 3.57, and its electrical resistivity after the onset point was 861 KΩ. The non-linear coefficient was 1.11 in the sample with 100 wt% polymer content. Thus, it was concluded that the composites established a better electrical non-linearity at higher filler amounts due to the nano-metric structure and closer particle linkages.
In this paper, we report how few layers graphene that can be produced in large quantity with low defect ratio from exfoliation of graphite by using a high intensity probe sonication in water containing liquid hand soap and PVP. It was founded that the graphene powder obtained by this simple exfoliation method after the heat treatment had an excellent exfoliation into a single or layered graphene sheets. The UV-visible spectroscopy, FESEM, TEM, X-ray powder diffraction and Raman spectroscopy was used to analyse the graphene product. The thermal diffusivity of the samples was analysed using a highly accurate thermal-wave cavity photothermal technique. The data obtained showed excellent enhancement in the thermal diffusivity of the graphene dispersion. This well-dispersed graphene was then used to fabricate an electrically conductive polymer-graphene film composite. The results demonstrated that this low cost and environmental friendly technique allowed to the production of high quality layered graphene sheets, improved the thermal and electrical properties. This may find use in the wide range of applications based on graphene.
In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or "smellprint" emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.
Modulated continuous wave (CW) lasers cause photothermal effect that leads to rapid optical absorption and generation of thermal waves around the irradiated nanostructures. In this work, we examined the effect of modulated CW laser irradiation on the particle fragmentation process to enhance the thermal diffusivity of nanofluids. A facile and cost-effective diode laser was applied to reduce the agglomerated size of Al2O3 nanoparticles in deionized water. The thermal wave generation, which was determined by the modulated frequency of the laser beam and the optical and thermal properties of the nanofluid, is also briefly discussed and summarized. The influence of laser irradiation time on nanoparticle sizes and their size distribution was determined by dynamic light scattering and transmission electron microscopy. The thermal diffusivity of the nanofluid was measured using the photopyroelectric method. The data obtained showed that the modulated laser irradiation caused the partial fragmentation of some agglomerated particles in the colloids, with an average diameter close to the original particle size, as indicated by a narrow distribution size. The reduction in the agglomerated size of the particles also resulted in an enhancement of the thermal diffusivity values, from 1.444 × 10-3 to 1.498 × 10-3 cm2/s in 0 to 30 min of irradiation time. This work brings new possibilities and insight into the fragmentation of agglomerated nanomaterials based on the photothermal study.
Integrated pest management (IPM) is widely practiced in commercial oil palm agriculture. This management system is intended to minimize the number of attacks by pest insects such as bagworms on crops, as well as curb economic loss with less dependency on chemical pesticides. One practice in IPM is the use of biological control agents such as predatory insects. In this study, we assessed the response of predatory natural enemies to pest outbreak and water stress, and document the habitat associations of potential pest predators. The abundances of 2 predatory insect species, namely Sycanus dichotomus and Cosmolestes picticeps (Hemiptera: Reduviidae), were compared bagworm outbreak sites and nonoutbreak sites within oil palm plantations. We also examined habitat characteristics that influence the abundances of both predatory species. We found that the abundance of C. picticeps was significantly higher in bagworm outbreak sites than in nonoutbreak sites. There were no significant differences in the abundance of S. dichotomus among outbreak and non-outbreak sites. Both species responded negatively to water stress in oil palm plantations. Concerning the relationship between predatory insect abundance and in situ habitat quality characteristics, our models explained 46.36% of variation for C. picticeps and 23.17% of variation for S. dichotomus. Both species of predatory insects thrived from the planting of multiple beneficial plants in oil palm plantations. The results suggest that C. picticeps can be used as a biological agent to control bagworm populations in oil palm plantations, but S. dichotomus has no or little potential for such ecosystem service.
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
The purpose of this work is to examine the effects of wear severity on the static and dynamic characteristics of journal bearings, and on the vibration response of a rigid rotor supported by journal bearings. Numerical simulations using MATLAB was conducted for three different operating regimes, namely low loaded operating regime ( ϵ 0 = 0.15 ), moderately loaded operating regime ( ϵ 0 = 0.45 ) and highly loaded operating regime ( ϵ 0 = 0.75 ) with wear depth parameter ratio ( δ ) varied from 0 to 0.5 at increments of 0.1. Numerical results showed that the vibration response of the rotor generally increases with the increase of the wear depth for all cases of low, moderately and highly loaded operating regimes of the bearings. For the values of parameters considered in this work, it was shown that the vibration response amplitude of the rotor in worn journal bearings may be six times larger compared to the response amplitude of the rotor in non-worn bearings.
The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software's option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation.
CdTe film was deposited using the electrophoretic deposition technique onto an ITO glass at various bath temperatures. Four batch film compositions were used by mixing 1 to 4 wt% concentration of CdTe powder with 10 mL of a solution of methanol and toluene. X-ray Diffraction analysis showed that the films exhibited polycrystalline nature of zinc-blende structure with the (111) orientation as the most prominent peak. From the Atomic Force Microscopy, the thickness and surface roughness of the CdTe film increased with the increase of CdTe concentration. The optical energy band gap of film decreased with the increase of CdTe concentration, and with the increase of isothermal bath temperature. The film thickness increased with respect to the increase of CdTe concentration and bath temperature, and following, the numerical expression for the film thickness with respect to these two variables has been established.
Colloidal Cu@CuAlO(2)-Al(2)O(3) bimetallic nanoparticles were prepared by a gamma irradiation method in an aqueous system in the presence of polyvinyl pyrrolidone (PVP) and isopropanol respectively as a colloidal stabilizer and scavenger of hydrogen and hydroxyl radicals. The gamma irradiation was carried out in a (60)Co gamma source chamber with different doses up to 120 kGy. The formation of Cu@CuAlO(2)-Al(2)O(3) nanoparticles was observed initially by the change in color of the colloidal samples from colorless to brown. Fourier transform infrared spectroscopy (FTIR) confirmed the presence of bonds between polymer chains and the metal surface at all radiation doses. Results of transmission electron microscopy (TEM), energy dispersive X-ray spectrometry (EDX), and X-ray diffraction (XRD) showed that Cu@CuAlO(2)-Al(2)O(3) nanoparticles are in a core-shell structure. By controlling the absorbed dose and precursor concentration, nanoclusters with different particle sizes were obtained. The average particle diameter increased with increased precursor concentration and decreased with increased dose. This is due to the competition between nucleation, growth, and aggregation processes in the formation of nanoclusters during irradiation.
The interactions of p-cresol photocatalytic degradation components were studied by response surface methodology. The study was designed by central composite design using the irradiation time, pH, the amount of photocatalyst and the p-cresol concentration as variables. The design was performed to obtain photodegradation % as actual responses. The actual responses were fitted with linear, two factor interactions, cubic and quadratic model to select an appropriate model. The selected model was validated by analysis of variance which provided evidences such as high F-value (845.09), very low P-value (
This manuscript reports on the physical properties and optical band gap of five samples of soda lime silicate (SLS) glass combined with zinc oxide (ZnO) that were prepared by a melting and quenching process. To understand the role of ZnO in this glass structure, the density, molar volume and optical band gaps were investigated. The density and absorption spectra in the Ultra-Violet-Visible (UV-Visible) region were recorded at room temperature. The results show that the densities of the glass samples increased as the ZnO weight percentage increased. The molar volume of the glasses shows the same trend as the density: the molar volume increased as the ZnO content increased. The optical band gaps were calculated from the absorption edge, and it was found that the optical band gap decreased from 3.20 to 2.32 eV as the ZnO concentration increased.
Silver nanoparticles were fabricated by ablation of a pure silver plate immersed in castor oil. A Nd:YAG-pulsed Q-switch laser with 1064-nm wavelength and 10-Hz frequency was used to ablate the plate for 10 minutes. The sample was characterized by ultraviolet-visible, atomic absorption, Fourier transform-infrared spectroscopies, and transmission electron microscopy. The results of the fabricated sample showed that the nanoparticles in castor oil were about 5-nm in diameter, well dispersed, and showed stability for a long period of time.