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We report systematic studies on crystal structure and magneto-dielectric properties of (1-x) BiFeO3-x DyMnO3 (0.0⩽x⩽0.2) nanoceramics synthesized by auto-combustion method. Rietveld refinement of X-ray diffraction data indicates a structural transition from R3c to R3c + Pn21a at x=0.1. Field emission scanning electron micrographs display a decrease in grain size with increase in x. The presence of dielectric anomalies around antiferromagnetic transition temperature implies the magnetoelectric coupling. Dielectric measurements showed decrease in magnetic ordering temperature with increasing x in agreement with differential scanning calorimetry results. A significant increase in magnetization has been found with increasing DyMnO3 substitution. Magnetoimpedance spectroscopy reveals a significant change (∼18%) in dielectric permittivity at H=2T for x=0.2. [ABSTRACT FROM AUTHOR]

Highly sensitive large-scale tin oxide (SnO2) nanostructures were grown on a glass substrate by thermal evaporation of a mixture of anhydrous tin (II) chloride (SnCl2) and zinc chloride (ZnCl2) powders at 550°C in air. We demonstrate a single cell vapor deposition system to precisely control nanostructural morphology of SnO2 by changing the weight ratio of SnCl2 and ZnCl2 and growth temperature. The morphology and structural property of as-grown nanostructures were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The SEM images revealed that the SnO2 nanostructures with different densities, sizes, and shapes can be achieved by adjusting the weight ratio of SnCl2 and ZnCl2. A thin film gas sensor based on SnO2 nanostructures with diameter ∼20 nm and length ∼100 nm showed ∼85% sensitivity and 53 seconds of response time, whereas the nanorods with diameter ∼100 nm and length ∼ 1μm showed ∼50% sensitivity with 198 seconds response time. The nanostructured material with small size and shape showed better sensitivity on sensing at room temperature compared to previously reported SnO2 based sensors. [ABSTRACT FROM AUTHOR]

An Elman network is used for the prediction of material removal rate (MRR) in electrical discharge machining (EDM). An Elman network is a dynamic recurrent neural network that can be used to model non-linear dynamic systems. Training of the models is performed with data from series of EDM experiments on AISI D2 tool steel from finishing, semi-finish to roughing operations. The machining parameters such as discharge current, pulse duration, duty cycle, and voltage were used as model input variables during the development of predictive models. The developed model is validated with a new set of experimental data that was not used for the training step. The mean percentage error of the model is found to be less than 6 per cent, which shows that the proposed model can satisfactorily predict the MRR in EDM. [ABSTRACT FROM AUTHOR]