Enhancing arsenic sequestration on ameliorated waste molasses nanoadsorbents using response surface methodology and mach
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RESEARCH ARTICLE
Enhancing arsenic sequestration on ameliorated waste molasses nanoadsorbents using response surface methodology and machine-learning frameworks Julie Baruah 1,2 & Chayanika Chaliha 1 & Bikash Kar Nath 1 & Eeshan Kalita 1 Received: 8 July 2020 / Accepted: 13 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The development of a novel nanobiosorbent derived from waste molasses for the adsorptive removal of arsenic (As) has been attempted in this study. Waste molasses were chemically ameliorated through a solvothermal route for the incorporation of iron oxide, thereby producing iron oxide incorporated carbonaceous nanomaterial (IOCN). Synthesis of IOCN was confirmed through transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and atomic emission spectroscopy (AES) analysis. The surface area and porous behavior of IOCN were elucidated by BrunauerEmmett-Teller (BET) assessments. The experimental conditions for adsorption were first modeled using response surface methodology (RSM) based on the central composite design (CCD), considering the parameters: adsorbate dosage, adsorbent dosage, pH, and contact time. RSM optimizations were improved upon using a three-layer feed-forward multilayer perceptron (MLP) based Artificial Neural Network (ANN) model. Optimization through ANN model resulted in the increase of the maximal As adsorption efficiency to ~ 96% for IOCN. The IOCN isotherm plots show the best fit for the Sips isotherm, and the reaction kinetics follows the pseudo-second-order model, indicating the chemisorption mechanism for As adsorption. Evidence for direct coordination of As to the surface of adsorbents was further confirmed by FTIR spectroscopic studies before and after As adsorption. The high adsorption efficiencies and the low-cost facile synthesis of the IOCN nanosorbent from agro-industrial waste indicate their potential for commercial applications. Keywords Waste molasses . Carbonaceous nanomaterial . Arsenic . Adsorption . Response surface methodology . Artificial neural network
Introduction Arsenic, a metalloid that occurs naturally, is the 12th most abundant element in the Earth’s crust and is a component of over 245 minerals (Mandal and Suzuki 2002). This
Responsible Editor: Tito Roberto Cadaval Jr Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11356-02011259-0. * Eeshan Kalita [email protected] 1
Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam 784028, India
2
Department of Chemical Sciences, Tezpur University, Tezpur, Assam 784028, India
predominant trace element is recognized as a major toxic pollutant to the environment that causes concern due to its natural prevalence, toxicity, and cancerous potential (Singh et al. 2007). Chronic arsenic exposure results in skin, lung, or bladder cancer (Ng et al. 2003). In addition, keratosis, hyperpigmentation, non-pitting edema of the fee
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