RESUMEN
The conventional open ponding system employed for palm oil mill agro-effluent (POME) treatment fails to lower the levels of organic pollutants to the mandatory standard discharge limits. In this work, carbon doped black TiO2 (CB-TiO2) and carbon-nitrogen co-doped black TiO2 (CNB-TiO2) were synthesized via glycerol assisted sol-gel techniques and employed for the remediation of treated palm oil mill effluent (TPOME). Both the samples were anatase phase, with a crystallite size of 11.09-22.18 nm, lower bandgap of 2.06-2.63 eV, superior visible light absorption ability, and a high surface area of 239.99-347.26 m2/g. The performance of CNB-TiO2 was higher (51.48%) compared to only (45.72%) CB-TiO2. Thus, the CNB-TiO2 is employed in sonophotocatalytic reactions. Sonophotocatalytic process based on CNB-TiO2, assisted by hydrogen peroxide (H2O2), and operated at an ultrasonication (US) frequency of 30 kHz and 40 W power under visible light irradiation proved to be the most efficient for chemical oxygen demand (COD) removal. More than 90% of COD was removed within 60 min of sonophotocatalytic reaction, producing the effluent with the COD concentration well below the stipulated permissible limit of 50 mg/L. The electrical energy required per order of magnitude was estimated to be only 177.59 kWh/m3, indicating extreme viability of the proposed process for the remediation of TPOME.
RESUMEN
Various conventional approaches have been reported for the synthesis of nanomaterials without optimizing the role of synthesis parameters. The unoptimized studies not only raise the process cost but also complicate the physicochemical characteristics of the nanostructures. The liquid-plasma reduction with optimized synthesis parameters is an environmentally friendly and low-cost technique for the synthesis of a range of nanomaterials. This work is focused on the statistically optimized production of silver nanoparticles (AgNPs) by using a liquid-plasma reduction process sustained with an argon plasma jet. A simplex centroid design (SCD) was made in Minitab statistical package to optimize the combined effect of stabilizers on the structural growth and UV absorbance of AgNPs. Different combinations of glucose, fructose, sucrose and lactose stabilizers were tested at five different levels (-2, -1, 0, 1, 2) in SCD. The effect of individual and mixed stabilizers on AgNPs growth parameters was assumed significant when p-value in SCD is less than 0.05. A surface plasmon resonance band was fixed at 302 nm after SCD optimization of UV results. A bond stretching at 1633 cm-1 in FTIR spectra was assigned to C=O, which slightly shifts towards a larger wavelength in the presence of saccharides in the solution. The presence of FCC structured AgNPs with an average size of 15 nm was confirmed from XRD and EDX spectra under optimized conditions. The antibacterial activity of these nanoparticles was checked against Staphylococcus aureus and Escherichia coli strains by adopting the shake flask method. The antibacterial study revealed the slightly better performance of AgNPs against Staph. aureus strain than Escherichia coli.
RESUMEN
Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the long run, it could be a permanent source of impaired vision, simply known as blindness in the advanced as well as in developing nations. This could be prevented if DR is identified early enough, but it can be challenging as we know the disease frequently shows rare signs until it is too late to deliver an effective cure. In our work, we recommend a framework for severity grading and early DR detection through hybrid deep learning Inception-ResNet architecture with smart data preprocessing. Our proposed method is composed of three steps. Firstly, the retinal images are preprocessed with the help of augmentation and intensity normalization. Secondly, the preprocessed images are given to the hybrid Inception-ResNet architecture to extract the vector image features for the categorization of different stages. Lastly, to identify DR and decide its stage (e.g., mild DR, moderate DR, severe DR, or proliferative DR), a classification step is used. The studies and trials have to reveal suitable outcomes when equated with some other previously deployed approaches. However, there are specific constraints in our study that are also discussed and we suggest methods to enhance further research in this field.
