Browsing by Author "Das, N."

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    Catalytically controlled formation of coumarin-based hydrogelator enables colorimetric ferrous ion detection in sol and hydrogel
    (Nature Research, 2025) Das, N.; Mandal, S.; Mal, S.S.; Bose, S.; Maity, C.
    In-situ generation of a hydrogelator from non-assembling precursors offers an effective strategy for preparing supramolecular hydrogel materials with precise spatiotemporal control. These hydrogels hold broad potential for applications ranging from theranostics to chemical sensing. Herein, we report a method for the in-situ formation of a coumarin-based supramolecular hydrogelator by simply mixing aqueous solutions of two non-assembling precursors under ambient conditions. The formation of the hydrogelator, its subsequent self-assembly into a hydrogel network, and the resulting material properties can all be modulated via acid catalysis. The hydrogelator exhibits excellent selectivity toward Fe(II) ions, providing a distinct colorimetric response with a linear correlation and a notable detection limit. Additionally, the hydrogel material can be easily applied to disposable paper strips, enabling convenient and portable detection of Fe(II) ions. This system demonstrates strong potential for addressing key challenges in Fe(II) ion sensing in both aqueous environments and self-assembled hydrogel states. (Figure presented.) © The Author(s) 2025.
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    Time series forecasting of temperature and turbidity due to global warming in river Ganga at and around Varanasi, India
    (Springer Science and Business Media Deutschland GmbH, 2022) Das, N.; Sagar, A.; Bhattacharjee, R.; Agnihotri, A.K.; Ohri, A.; Gaur, S.
    The fluctuation in the river ecosystem network due to climate change-induced global warming affects aquatic organisms, water quality, and other ecological processes. Assessment of climate change-induced global warming impacts on regional hydrological processes is vital for effective water resource management and planning. The global warming effect on river water quality has been analyzed in this work. The river Ganga stretch near the Varanasi region has been chosen as the study area for this analysis. The air temperature has been predicted using the seasonal autoregressive integrated moving average (SARIMA) and the Prophet model. The Prophet model has shown better accuracy with a root mean square percent error (RMSPE) value of 3.2% compared to the SARIMA model, which has an RMPSE value of 7.54%. The river temperature, turbidity, and nighttime radiance values have been predicted for the years 2022 and 2025 using the long short-term memory (LSTM) algorithm. The anthropogenic effect on the river has been evaluated by using the nighttime radiance imageries. The predicted average river temperature shows an increment of 0.58 °C and 0.63 °C for the city and non-city river stretches, respectively, in 2025 compared to 2022. Similarly, the river turbidity shows an increment of 1.21 nephelometric turbidity units (NTU) and 1.17 NTU for the city and non-city stretch, respectively, in 2025 compared to 2022. For future predicted years, the nighttime radiance values for the region situated near the city river stretch show a significant rise compared to the region that lies nearby the non-city river stretch. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.