North Eastern Regional Institute of Science and Technology

About: North Eastern Regional Institute of Science and Technology is a education organization based out in Itanagar, India. It is known for research contribution in the topics: Population & Raman spectroscopy. The organization has 813 authors who have published 1429 publications receiving 16122 citations.

Microwave synthesis of hydroxyapatite bioceramic and tribological studies of its composites with SrCO3 and ZrO2

Abstract: Hydroxyapatite (HAp) powders were prepared successfully using microwave-assisted co-precipitation method. HAp powder was characterized by X-ray diffraction (XRD), fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy for structural confirmation of the prepared material. Further, six composites of HAp with SrCO3 and ZrO2 were synthesized to study the morphological and tribological behaviour. Three composites of HAp with three varying 2, 4, 6 wt% of SrCO3 and similarly other three with ZrO2 were prepared using solid-state route method. Scanning electron microscopy (SEM) analysis confirmed that the presence of SrCO3 and ZrO2 among HAp particles helps in grain growth during the sintering processes. The tribological study reveald that the inclusion of SrCO3 and ZrO2 in pure HAp enhanced the resistance to wear and specific wear rate. The average grain size of HAp–ZrO2 was observed more in comparision to the average grain size of the HAp–SrCO3. The values of the specific wear rate and wear of HAp–SrCO3 and HAp–ZrO2 composite ceramics lies in the range from 4.13,239 × 10−5 to 5.44517 × 10−5 mm3/Nm and 4.68693 × 10−5 to 6.10099 × 10−5 mm3/Nm, respectively.

Electrospun Nanofibrous Filtration Membranes for Heavy Metals and Dye Removal

Abstract: The discharge of untreated domestic sewage and wastewater from industrial and commercial establishments into surface water has caused water scarcity. As a result, 1.2 billion of the population can barely get clean drinking water. Besides, each year millions die drinking unhealthy water. Recently, progress in electrospinning skill has helped in the manufacture of polymer fibers of one-dimensional nanostructures having nanometer diameters that have elicited enormous attention because of high surface area-to-volume ratio, water permeability, high porosity, and superior mechanical properties, which provides a foremost contribution to water treatment. Based on their properties, such as thickness, porosity, and surface roughness, these characteristic nanofibrous materials were used as membrane solvents in an extensive assortment of applications in water purification such as remediation of heavy metal ions and dyes. To increase their functionality and applicability, nanofibers have promoted nanocomposite tactics, for example, carbon nanotubes, metal/metal oxides, keen biological agents, that have been included either throughout electrospinning process or else in the posthanding-out. In addition, to improve the mechanical steadiness and reduce the pressure drop, electrospun nanofibers are supported by nonwoven microfibrous to make a hybrid membrane. These hybrid/nanocomposite nanofibers have performed extremely well in water treatment. This chapter gives a wide-ranging analysis of the extant research and progress in the field of water treatment using electrospun nanofiber filtration membranes to achieve environmental detoxification using the adsorption process.

Estimation of reference evapotranspiration using data driven techniques under limited data conditions

Abstract: Estimation of reference evapotranspiration $$(E{T_0})$$ is a major task in irrigation and water resources development and management. In this context, the FAO56 Penman–Montieth (FAO56PM) equation, one of the most accurate equation is used, which however requires a high number of climatic parameters that are not always available for most meteorological stations. The present study measures the effectiveness of selected machine learning techniques [evolutionary regression (ER), artificial neural network (ANN), multi nonlinear regression (MLNR), and support vector machines (SVMs)] for the estimation of $$E{T_0}$$ under limited data conditions. Different models were developed for estimating $$E{T_0}$$ , including various sets of daily climatic variables, namely maximum and minimum air temperature, extraterrestrial radiation, sunshine hours, wind speed and relative humidity Statistical evaluation of different results obtained from selected techniques showed that the best performance was obtained when six meteorological variables, i.e., ( $${T_{max}},\;{T_{min}},\;{R_a},\;{S_h},\;{W_s},\;R{H_{min}}$$ ) were used as input, with highest $${R^2}=0.9931 \,$$ by ANN4 followed by ER4 with $${R^2}=0.9886$$ . The ER and ANN performed similarly though ANN performed slightly better against FAO56PM model. The ER was recommended over ANN because ER gives a physical algebraic expression, which can be further replicated without modelling tools and computers. Further, its gives the intermittent calculation visibility and understanding, also that ER technique is easy to use and more practical in application. However, ANN models need modelling tools and computers with the very skilled user and inside calculations like a black box. ANN also require simulation process to get any output.

Tillage effects on soil microbial biomass in a rainfed agricultural system of northeast India

Abstract: In an agricultural system, tillage intensity can influence soil biological properties and relevant soil processes including C sequestration. Microbial biomass often responds quickly to changes in soil management and is known to be an indicator of soil quality. A field study was conducted from 2006 to 2007 in the rainfed agricultural system of northeast India with variedly tilled implements (indigenous spade, Country Plough, Bose Plough and Mouldboard Plough) to study the latter's effect on soils’ microbial nutrient concentration during different growth phases of Oryza sativa . No-till plot of the study site recorded comparatively higher values for microbial C (498.17 μg g −1 ) microbial N (71.01 μg g −1 ) and microbial P (44.50 μg g −1 ). Three-way factorial ANOVA (duration, tillage type and soil depth) for microbial nutrients too showed significant variation with each of these factors. Total organic C and available P showed significant correlation with microbial biomass C ( R 2 = 0.6348, n = 120, p R 2 = 0.916, n = 120, p R 2 = 0.5952, n = 120, p