Degradation of Methylene Blue Using Microplasma Discharge – A Relative Study with Photodegradation

Large-scale production and application of synthetic dyes have become a matter of concern as it is a major factor responsible for environmental pollution. Most dyeing effluents are discharged into water bodies and lands without being treated, which ultimately pollutes the groundwater making it unfit for consumption. The present study explains the degradation of one of such synthetic dyes Methylene blue (MB), using non-thermal Microplasma treatment. The aqueous solution of MB was treated with an array of air microplasma discharge at atmospheric pressure. Different concentrations (10 ppm, 20 ppm) of MB solution were treated for various treatment time and chemical parameters like pH, electrical conductivity, total dissolved solids and salinity was measured. The degradation percentage reached 100% in 15 min of treatment for 10 ppm MB solution, and 20 min of treatment for 20 ppm MB solution indicated by the color change from blue to a clear solution. The reactive oxygen species (ROS) and reactive nitrogen species (RNS) formed during the microplasma treatment are responsible for MB degradation. Same volume of MB solution was irradiated by direct sunlight for photodegradation and was found to degrade the solution of 10 ppm by 96% and 20 ppm by 93% in 10 hours of treatment. Experimental results indicated that microplasma treatment was effective for dye degradation, without the need for pretreatment process or chemicals

Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by deep learning

We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network, and Long Short-Term Memory. The Deep Learning models are trained using the training set and are allowed to predict the test set data. After prediction, the time series of the actual and the predicted values are plotted one over the other to visualize the performance of the models. Upon evaluating the Root-Mean-Square Error value between predicted and the actual values of all three models, we find that the Long Short-Term Memory model can serve as the best model to forecast the chaotic time series and to predict the emergence of extreme events in the considered system

Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by deep learning

We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network, and Long Short-Term Memory. The Deep Learning models are trained using the training set and are allowed to predict the test set data. After prediction, the time series of the actual and the predicted values are plotted one over the other to visualize the performance of the models. Upon evaluating the Root-Mean-Square Error value between predicted and the actual values of all three models, we find that the Long Short-Term Memory model can serve as the best model to forecast the chaotic time series and to predict the emergence of extreme events in the considered system

Accelerated degradation of 4-nitrophenol using microplasma discharge: Processes and mechanisms

In this work, an atmospheric microplasma reactor has been constructed to evaluate, the influence of microplasma forming gases (air, oxygen, nitrogen, and argon) on 4-nitrophenol (4NP) degradation and mineralization efficiency as a function of treatment time. 92 % of 4NP in solution was degraded by air microplasma after 7 min, which was higher than nitrogen, oxygen, and argon microplasma treatments. The air microplasma treatment achieved a consistent mineralization percentage of 57 %. With the addition of hydroxyl radical scavenger during the treatment, irrespective of the gas medium, the degradation percentage got reduced; for air, it was 76 % reduced compared to without scavenger. This implies that the hydroxyl radical is primarily responsible for the degradation of 4NP. The amount of hydrogen peroxide produced during the treatment was measured, and its role in 4NP degradation was examined. A density functional theory calculation was carried out to identify reactive sites on the 4NP molecule. 4NP intermediates, including hydroquinone and benzoquinone, were identified using liquid chromatography-mass spectrometry, and a 4NP degradation pathway was proposed based on the detected intermediates. The phytotoxicity of plasma mineralized 4NP aqueous solution was analyzed by mung bean seed germination, and the results reveal that plasma treated 4NP aqueous solution has 11.5 % of higher germination percentage than untreated 4NP

Conversion of Industrial Bio-Waste into Useful Nanomaterials

Chromium-complexed collagen is generated as waste during processing of skin into leather. Here, we report a simple heat treatment process to convert this hazardous industrial waste into core–shell chromium–carbon nanomaterials having a chromium-based nanoparticle core encapsulated by partially graphitized nanocarbon layers that are self-doped with oxygen and nitrogen functionalities. We demonstrate that these core–shell nanomaterials can be potentially utilized in electromagnetic interference (EMI) shielding application or as a catalyst in aza-Michael addition reaction. The results show the ability to convert industrial bio-waste into useful nanomaterials, suggesting new scalable and simple approaches to improve environmental sustainability in industrial processes