Kalasalingam Academy of Research and Education (KARE), formerly Arulmigu Kalasalingam College of Engineering and Kalasalingam University, is a private deemed to be university located in Krishnankoil near Rajapalayam in Tamil Nadu, India. The campus is close to the ancient temple town of Srivilliputhur.
Sustainable biofuel production from renewable feedstocks remains a key challenge in the development of low-carbon transportation fuels. This paper outlines an effective approach to biofuel production using marine macroalgal oil produced from Ulva fasciata as a sustainable non-edible feedstock, with focus on environmental suitability and efficiency of the process. The lipids that were obtained after the macroalgal biomass were subjected to proximate and physicochemical analyses to determine the suitability of the feedstock. Zn-doped CaO nano catalyst was developed and systematically characterized to elucidate its structural, morphological and textural properties. Biofuel production was systematically modeled and optimized using response surface methodology (RSM), artificial neural networks (ANN), and a genetic algorithm (GA) to understand process interactions and maximize conversion efficiency. Under optimized conditions, a biofuel yield of 80.78
Calcium molybdate (CaMoO4) green-emitting phosphors were synthesized via the co-precipitation method, subsequently sintered at different temperatures (100–900 °C) in order to study the influence of sintering conditions on their structural, morphological, and luminescent properties. The phase-pure tetragonal scheelite-type structure is identified and confirmed using Powder X-ray diffraction (PXRD) analysis. Green emission at 500 nm is observed, which can be related to charge transfer transitions of the MoO_4^2- tetrahedral groups. The highest intensity of the emission peak is observed for the phosphor prepared by sintering at 800 °C for 2 h. The morphological and XPS analyses confirm the uniform growth of the phosphors and the elemental composition. Thus, it is elucidated that CaMoO4 phosphors prepared and sintered at 800 °C for 2 h have good luminescent properties, which can be used for optoelectronic device applications.
The supercapacitor performance enhancement of vanadium oxide (V2O5) nanostructures through the incorporation of CuO and reduced graphene oxide (rGO), as the redox-active component and conductive additives, respectively, was reported in this work. The CuO incorporated V2O5 (CuO/V2O5) nanocomposites (NCs) and rGO-supported CuO/V2O5 (CuO/V2O5/rGO) NCs were successfully synthesized using an ultrasonication-assisted method. Their structural, morphological, and vibrational properties were systematically investigated using various characterization techniques. For electrochemical evaluation, working electrodes and asymmetric supercapacitors (ASCs) were fabricated using the prepared CuO/V2O5 and CuO/V2O5/rGO NCs via the Doctor Blade technique and analyzed in a 1 M KOH electrolyte. The CuO/V2O5 and CuO/V2O5/rGO electrodes delivered high specific capacitance values of 740 and 1734 F g−1 at 5 mV s−1, and 567 and 1440 F g−1 at 0.5 A g−1, respectively. In terms of device performance, the assembled ASCs based on CuO/V2O5 and CuO/V2O5/rGO NCs exhibited the outstanding energy densities of 3.8 and 9.93 Wh kg−1 and power densities of 500 and 650 W kg−1, respectively. Furthermore, the CuO/V2O5/rGO ASC demonstrated superior cycling stability with 88.31
Groundwater fluoride contamination poses serious public health risks, requiring efficient and affordable treatment materials. This study investigates aluminium-impregnated Sesbania grandiflora activated carbon for defluoridation under realistic conditions, focusing on removal efficiency, parameter optimization, adsorption behaviour, regeneration, and Bureau of Indian Standards compliance. Optimal conditions were 2.36 mm particle size, 8 g dosage, 300 rpm, and 120 min contact time. Characterization using Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction confirmed effectiveness. Fourier transform infrared spectroscopy identified active N–H, =C–H, and metal–oxide groups involved in fluoride binding and adsorption mechanisms. Scanning electron microscopy showed a transition from rough, porous surfaces to smoother, more compact morphology after adsorption. Energy-dispersive X-ray spectroscopy revealed compositional changes, with carbon decreasing from 80.3 to 76.9 wt
Based on behavioural or physical characteristics, humans are recognized by using biometric system. In computer vision and pattern recognition domain, dynamic research is going on in face recognition. Face recognition algorithms are challenged by intra-personal changes in pose, illumination, and expression (PIE). Images are processed and matched with various databases. For face recognition, multi-task learning (MTL) is explored in this work. However, recognition of faces from blur and poor illumination becomes difficult. Recovering face from mixed noise degradation is a challenging and promising theme. This work explores an ensample convolutional neural network (ECNN) for face recognition. Initially a new adaptive morphological bilateral filtering (AMBF) method is proposed. Without introducing undershoot or overshoot, slope of edges is increased for sharpening a blur image. Quality sharpening enhancement is assured by various morphological operations like closing, opening, erosion and dilation with proper size of structure element. In addition to adaptive bilateral filter, mathematical morphology operations are included to enhance the performance. Then a multi-task ECNN is implemented for a main classification task and estimation of pose, blur, illumination, and expression (PBIE) as side tasks. For every side task, loss weights are assigned automatically by developing bat algorithm (BA) based dynamic-weighing method. In multi-task ECNN, balance between various tasks are achieved. Hence, proposed method is effectively demonstrated by the results of experimentation on entire multi-PIE dataset.
