Publications by authors named "Sathishkumar V E"

5 Publications

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An efficient and magnetically recoverable g-CN/ZnS/CoFeO nanocomposite for sustainable photodegradation of organic dye under UV-visible light illumination.

Environ Res 2021 Jun 17;201:111429. Epub 2021 Jun 17.

Advanced Materials Research Chair, Chemistry Department, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia.

Effective improvement of an easily recoverable photocatalyst is equally vital to its photocatalytic performance from a practical application view. The magnetically recoverable process is one of the easiest ways, provided the photocatalyst is magnetically strong enough to respond to an external magnetic field. Herein, we prepared graphitic carbon nitride nanosheet (g-CN), and ZnS quantum dots (QDs) supported ferromagnetic CoFeO nanoparticles (NPs) as the gCN/ZnS/CoFeO nanohybrid photocatalyst by a wet-impregnation method. The loading of CoFeO NPs in the g-CN/ZnS nanohybrid resulted in extended visible light absorption. The ferromagnetic g-CN/ZnS/CoFeO nanohybrid exhibited better visible-light-active photocatalytic performance (97.11%) against methylene blue (MB) dye, and it was easily separable from the aqueous solution by an external bar magnet. The g-CN/ZnS/CoFeO nanohybrid displayed excellent photostability and reusability after five consecutive cycles. The favourable band alignment and availability of a large number of active sites affected the better charge separation and enhanced photocatalytic response. The role of active species involved in the degradation of MB dye during photocatalyst by g-CN/ZnS/CoFeO nanohybrid was also investigated. Overall, this study provides a facile method for design eco-friendly and promising g-CN/ZnS/CoFeO nanohybrid photocatalyst as applicable in the eco-friendly dye degradation process.
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http://dx.doi.org/10.1016/j.envres.2021.111429DOI Listing
June 2021

Adsorptive removal of noxious atrazine using graphene oxide nanosheets: Insights to process optimization, equilibrium, kinetics, and density functional theory calculations.

Environ Res 2021 09 6;200:111428. Epub 2021 Jun 6.

Department of Chemical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates.

Atrazine is a toxic herbicide whose alarming rate of contamination in the drinking water and wastewater poses a severe threat to the environment and human health. Here in this study, the graphene oxide (GO) nanosheets were prepared using Hummers' method with minor modification and studied as a potential adsorbent for atrazine removal from simulated wastewater. The spectroscopy and microscopic analysis confirmed the successful formation of GO with a multilayer structure resembling the crumpled sheets with random stacking. The Response Surface Methodology (RSM) employing Box Behnken design (BBD) was successfully developed to predict the optimal conditions for maximal atrazine removal as adsorbent dosage 121.45 mg/L; initial feed concentration 27.03 mg/L; temperature 27.69 °C, pH 5.37, and time 180 min. The atrazine adsorption onto GO was found to be higher in acidic pH and lower temperature. Density functional theory (DFT) calculation of adsorbent-adsorbate complex in the implicit solvent medium suggests adsorption affinity energy of -24.4 kcal/mol for atrazine. A careful observation of the molecules configuration and binding energy showed that the π-π interactions and hydrogen bonds played a significant role in the adsorption phenomena. Langmuir isotherm suited well to the adsorption process with a maximum adsorption capacity of 138.19 mg/g, at 318 K. The fitness of kinetic models for atrazine adsorption onto GO nanosheets were in following order Ho < Sobkowsk-Czerwi < Avrami model based on their correlation coefficient (R) values. Reusability analysis showed that GO nanosheets could be effectively recycled using 0.01 N NaOH up to six cycles of atrazine removal. Thus, this study provided a theoretical and experimental basis for the potential application of GO nanosheets as a novel adsorbent for the removal of hazardous atrazine.
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http://dx.doi.org/10.1016/j.envres.2021.111428DOI Listing
September 2021

Rice leaf diseases prediction using deep neural networks with transfer learning.

Environ Res 2021 07 11;198:111275. Epub 2021 May 11.

Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India. Electronic address:

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%.
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http://dx.doi.org/10.1016/j.envres.2021.111275DOI Listing
July 2021

Prediction of batch sorption of barium and strontium from saline water.

Environ Res 2021 06 1;197:111107. Epub 2021 Apr 1.

School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, 52828, South Korea. Electronic address:

Celestite and barite formation results in contamination of barium and strontium ions hinder oilfield water purification. Conversion of bio-waste sorbent products deals with a viable, sustainable and clean remediation approach for removing contaminants. Biochar sorbent produced from rice straw was used to remove barium and strontium ions of saline water from petroleum industries. The removal efficiency depends on biochar amount, pH, contact time, temperature, and Ba/Sr concentration ratio. The interactions and effects of these parameters with removal efficiency are multifaceted and nonlinear. We used an artificial neural network (ANN) model to explore the correlation between process variables and sorption responses. The ANN model is more accurate than that of existing kinetic and isotherm equations in assessing barium and strontium removal with adj. R values of 0.994 and 0.991, respectively. We developed a standalone user interface to estimate the barium and strontium removal as a function of sorption process parameters. Sensitivity analysis and quantitative estimation were carried out to study individual process variables' impact on removal efficiency.
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http://dx.doi.org/10.1016/j.envres.2021.111107DOI Listing
June 2021

Machine learning techniques based on security management in smart cities using robots.

Work 2021 ;68(3):891-902

Department of Computer Science & Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India.

Background: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities' security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services.

Objectives: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network.

Results: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis.

Conclusion: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.
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http://dx.doi.org/10.3233/WOR-203423DOI Listing
June 2021
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