Nano-composite rGO-Ag-Cu-Ni mediated photocatalytic degradation of anthracene and benzene.

Polycyclic aromatic hydrocarbons (PAHs) are omnipresent, persistent, and carcinogenic pollutants continuously released in the atmosphere due to the rapid increase in population and industrialization worldwide. Hence, there is an ultimate rise in concern about eliminating the toxic PAHs and their related aromatic hydrocarbons from the air, water, and soil environment by employing efficient removal technologies using nanoparticles as a catalyst. Here, the degradation of selective PAHs viz., anthracene and benzene using laboratory synthesized rGO-Ag-Cu-Ni nanocomposite (catalyst) was studied. Characterization studies revealed the nanocomposites exhibited surface plasma resonance at 350 - 450 nm, confirming the presence of Ag, Cu, and Ni metal ions embedded on the reduced graphene substrate. It was found that the nanocomposites synthesized were spherical, amorphous in nature, and aggregated together with measurements ranging from 423 to 477 nm. An SEM-EDX analysis of the nanocomposite demonstrated that it contained 25.13% O, 14.24% Ni, 27.79% Cu, and 32.84% Ag, which confirms the synthesis of the nanocomposite. Crystalline, sharp nanocomposites of average size 17-41 nm with an average diameter of 118.5 nm (X-ray diffraction and DLS) were observed. FTIR spectra showed that the nanocomposites had the functional groups alkanes, alkenes, alkynes, carboxylic acids, and halogen derivatives. Batch adsorption studies revealed that the maximum degradation achieved at optimum nano-composite concentration of 10 µg/mL, pH value of 5, PAHs concentration of 2 µg/mL and effective irradiation source being UV radiations in the case of both benzene and anthracene pollutants. The degradation of benzene and anthracene followed Freundlich & Langmuir isotherm with the highest R2 value of 0.9894 & 0.9885, respectively. Adsorption kinetic studies under optimum conditions revealed that the adsorption of both benzene and anthracene followed Pseudo-second order kinetics. Antimicrobial studies revealed that the synthesized nano-composite exhibited potential antimicrobial activity against Gram positive bacterium (Bacillus subtilis, Staphylococcus aureus), Gram negative bacterium (Klebsiella pneumonia, Escherichia coli) and fungal strain (Aspergillus niger) respectively. Thus, the synthesized rGO-Ag-Cu-Ni nano-composite acts as an effective antimicrobial agent as well as a PAHs degrading agent, helping to overcome antibiotics resistance and to mitigate the overgrowing PAHs pollution in the environment.

Fabrication and characterization of manganese dioxide (MnO2) nanoparticles and its degradation potential of benzene and pyrene.

MnO2 nanoparticles have a wide range of applications, including catalytic abilities due to their oxygen reduction potential. Industrial processes and the burning of organic materials released PAHs into the biosphere which have adverse effects on living organisms when continually exposed. In this study, MnO2 nanoparticles were synthesized chemically using sodium thiosulphate as reducing agent. MnO2 nanoparticles were characterized using UV-visible adsorption spectroscopy and Fourier Transform Infrared Spectroscopy (FTIR). A X-Ray Diffraction Spectrophotometer (XRD), a Scanning Electron Microscopy - Energy Dispersive X-Ray Analyzer (SEM-EDAX), and Dynamic Light Scattering (DLS) were used to identify the crystalline nature and particle size of the fabricated MnO2 nanoparticles. Batch adsorption studies were conducted to identify the optimal conditions for better benzene and pyrene adsorption from aqueous solution using MnO2 nanoparticles. They are also effective in degrading benzene and pyrene by batch adsorption as determined by their adsorption isotherms and kinetics.

Unleashing the potential of nanoparticles on seed treatment and enhancement for sustainable farming.

The foremost challenge in farming is the storage of seeds after harvest and maintaining seed quality during storage. In agriculture, studies showed positive impacts of nanotechnology on plant development, seed storage, endurance under various types of stress, detection of seed damages, and seed quality. Seed's response varies with different types of nanoparticles depending on its physical and biochemical properties and plant species. Herein, we aim to cover the impact of nanoparticles on seed coating, dormancy, germination, seedling, nutrition, plant growth, stress conditions protection, and storage. Although the seed treatment by nanopriming has been shown to improve seed germination, seedling development, stress tolerance, and seedling growth, their full potential was not realized at the field level. Sustainable nano-agrochemicals and technology could provide good seed quality with less environmental toxicity. The present review critically discusses eco-friendly strategies that can be employed for the nanomaterial seed treatment and seed enhancement process to increase seedling vigor under different conditions. Also, an integrated approach involving four innovative concepts, namely green co-priming, nano-recycling of agricultural wastes, nano-pairing, and customized nanocontainer storage, has been proposed to acclimatize nanotechnology in farming.

Effects of different biofuels and their mixtures with diesel fuel on diesel engine performance and exhausts.

In this study, a compression ignition engine that ran on recycled used cooking oil (RUCO), Jatropha curcas (JC), Pongamia Pinnata (PP), and petroleum diesel fuel (PDF) was investigated for its energy, performance, and exhaust emissions. The 20 % by volume RUCO, JC, and PP biofuel mix with PDF is taken. According to the American Society for Testing and Material (ASTM) standard, the blend qualities are evaluated. Viscosity, density, flash point, and heating value have all been tested for the 20 % blend. The outcome indicated that for a 20 % mix, the viscosity, density and flash point were all greater than in the PDF but heat value lower. Because studies have demonstrated that diesel engines can operate on 20 % replacement without any modifications, this study focused on 20 % blend. The engine was tested with loads (Ls) ranging from 0 % to 100 % of its entire capacity while the compression ratios (CRs) was varied. The experimental result demonstrated that the thermal efficiency, as measured by the PDF, was much greater than that of the DRUCO20, DJC20, and DPP20 blends. After the addition of RUCO, JC, and PP to PDF, the temperature of the exhaust gases reduced, and the engine used more gasoline as a result. It was discovered that an engine emissions of hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) were lower than those of PDF. Even though it produced a greater amount of carbon dioxide (CO2) emissions, the DRUCO20 was superior to both the DCJ20 and the DPP20.

High efficiency lipid production, biochar yield and chlorophyll a content of Chlorella sp. microalgae exposed on sea water and TiO2 nanoparticles.

This study explores the challenges facing microalgae biofuel production, specifically low lipid content and difficulties with algal cell harvesting. The purpose of the research is to investigate the effect of seawater content and nanoparticle concentration on freshwater microalgae growth and biofuel production. The principal results of the study show that increasing the proportion of seawater and nanoparticles enhances the lipid content and cell diameter of microalgae, while excessive concentrations of nanoparticles and low seawater content lead to reduced microalgae growth. Furthermore, an optimal cell diameter was identified at a nanoparticle concentration of 150 mg/L. The study also reveals that increasing seawater content can decrease zeta potential and increase chlorophyll a content due to the concentration of dissolved organic matter. Increasing the seawater content from 0% to 25% decreased zeta potential by 1% owing to the instability and aggregation of the cells. Chlorophyll a for the 0% seawater was 0.55 which is increased to 1.32 only due to the increase in the seawater content. This significant increase is due to the concentration of dissolved organic matter in seawater. Additionally, the presence of seawater positively affects microalgae metabolic activity and biochar yield. The findings of this study offer valuable insights into the potential for optimizing microalgae biofuel production. The use of seawater and nanoparticles has shown promise in enhancing microalgae growth and biofuel yield, and the results of this study underscore the scientific value of exploring the role of seawater and nanoparticles in microalgae biofuel production. Further research in this area has the potential to significantly contribute to the development of sustainable energy solutions.

Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 by in silico approaches: Molecular dynamics docking studies.

Traditional medicines against COVID-19 have taken important outbreaks evidenced by multiple cases, controlled clinical research, and randomized clinical trials. Furthermore, the design and chemical synthesis of protease inhibitors, one of the latest therapeutic approaches for virus infection, is to search for enzyme inhibitors in herbal compounds to achieve a minimal amount of side-effect medications. Hence, the present study aimed to screen some naturally derived biomolecules with anti-microbial properties (anti-HIV, antimalarial, and anti-SARS) against COVID-19 by targeting coronavirus main protease via molecular docking and simulations. Docking was performed using SwissDock and Autodock4, while molecular dynamics simulations were performed by the GROMACS-2019 version. The results showed that Oleuropein, Ganoderic acid A, and conocurvone exhibit inhibitory actions against the new COVID-19 proteases. These molecules may disrupt the infection process since they were demonstrated to bind at the coronavirus major protease's active site, affording them potential leads for further research against COVID-19.

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Retraction notice to "Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 by in silico approaches: Molecular dynamics docking studies" [Engineering Analysis with Boundary Elements volume 150 (2023) Pages 583-598].

[This retracts the article DOI: 10.1016/j.enganabound.2023.02.043.].