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    Techniques for preparation and dispersion of nano-SiO2 in Cementitious System—A Review
    (Springer, 2019) Snehal, K.; Das, B.B.
    Nowadays, the research in the field of concrete is driving its focus on novel technology by making use of nanosized particles so-called nanotechnology in concrete. The oxide nanoparticles have a great influence on concrete properties, amongst all the nanoparticles, nano-silica is the first nano product that replaced the micro silica or silica fume in concrete, and it is the most predominantly used nanoparticle amid all other nanoparticles in cementitious system. This paper states the overview on various methods involved in production of nano-SiO2 particles for the purpose of utilizing in cementitious system as well as the various techniques for well dispersion of nanosized SiO2 particles in cement matrices. © Springer Nature Singapore Pte Ltd. 2019.
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    Dispersion in an Urban Cross-Street Intersection
    (Springer Science and Business Media Deutschland GmbH, 2025) Ranadive, S.; Singh, L.; Jitendra Pal, S.
    A typical urban street intersection comprises of roadways, pedestrian walkways, and tall buildings on the sides. Due to the large number of vehicles plying on the urban streets, the pedestrians are exposed to vehicular pollution. With the increase in the density of automobiles, there is a bigger threat to pedestrians. The current study investigates pollution dispersion in the case of an eight-story building across a cross-street intersection. The study includes the effect of the aspect ratio (AR = 0.5, AR = 1, and AR = 2), a ratio of building height to road width, on flow patterns and dispersion. The second part of the work includes the effect of wind approach angle (0º, 30º, 45º, 60º) on the dispersion. It is found that the pollutant mass concentration at the nose level of pedestrians is strongly influenced by the aspect ratio wherein the taller building provides less exposure to pedestrians due to the lateral dispersion. The wind approach angle also affects the pollutant concentration at the nose level. The exposure to pollutant is skewed along the length of the road walkways in the case of 30º and 60º. However, the dispersion in more uniform in the case when wind approach angle is 45º. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    Theoretical Analysis of On-Chip Vertical Hybrid Plasmonic Nanograting
    (Springer, 2022) Reddy, S.K.; Sahu, S.K.; Khoja, R.; Kanu, S.; Singh, M.
    A complementary metal oxide semiconductor (CMOS) compatible photonic-plasmonic waveguide with nanoscale dimensions and better optical confinement has been proposed for the infrared (IR)–band applications. The design is based on the multi-layer hybrid plasmonic waveguide (Si–SiO2–Au) structure. The 3D-finite element method (FEM)–based numerical simulations of single slot hybrid plasmonic waveguide (HPWG) confirms 2.5 dB/cm propagation loss and 15 μm−2 confined intensity. Moreover, its application as dual-slot nanograting is studied with higher propagation length and ultra–low–dispersion near the 1550–nm wavelength. The proposed low-dispersion nanoscale grating design is suitable for future lab–on–chip nanophotonic integrated circuits. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Analysis of Concentration of Ambient Particulate Matter in the Surrounding Area of an Opencast Coal Mine using Machine Learning Techniques
    (Shahrood University of Technology, 2024) Podicheti, R.K.; Karra, R.C.
    Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological conditions. There is a need to establish a suitable model for predicting the concentration of particulate matter on a regional basis. Though a number of dispersion models exist for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling complex interactions, anomaly detection, etc. An attempt has been made to assess the dispersion of particulate matter using machine learning techniques by considering the mining and meteorological parameters. Historical data comprising of mine working parameters, meteorological conditions, and particulate matter pertaining to one of the operating opencast coal mines in southern India has been utilized for the study. The data has been analyzed using different machine learning techniques like bagging, random forest, and decision tree. The performance metrics of test data are compared for different models in order to find the best fit model among the three techniques. It is found that for PM10, many of the times bagging technique gave a better accuracy, and for PM2.5, decision tree technique gave a better accuracy. Integration of mine working parameters with meteorological conditions and historical data of particulate matter in developing the model using machine learning techniques has helped in making more accurate predictions. © 2024, Shahrood University of Technology. All rights reserved.