Green methods of synthesizing nanoparticles are safer than chemical and physical methods, as well as being eco-friendly and cost-efficient. In this study, we use copper oxide nanoparticles (CuO NPs) fabricated with Sesbania grandiflora (Sg) (Hummingbird tree) leaves to test the effectiveness of green synthesizing methods. The attained Sg-CuO NPs physical and optical nature is characterized by UV-Vis spectroscopy Differential Reflectance Spectroscopy (UV-Vis DRS), Fourier Transform Infra-Red spectroscopy (FTIR), X-ray Diffraction spectroscopy (XRD), Scanning Electron Microscope (SEM), and Energy Dispersive X-ray Analysis (EDAX). UV-Vis spectrum for Sg-CuO NPs revealed a peak at 410 nm. SEM images showed the aggregation of needle-shaped particles, at a size of 33 nm. The amylase and glucosidase enzymes were inhibited by the Sg-CuO NPs up to 76.7% and 72.1%, respectively, indicating a possible antihyperglycemic effect. Fabricated Sg-CuO NPs disclosed the excellent inhibition of DPPH-free radicle formation (89.7%) and repressed protein degradation (81.3%). The results showed that Sg-CuO NPs display good anti-bacterial activity against the gram-negative (Escherichia coli and Pseudomonas aeruginosa) and gram-positive (Staphylococcus aureus). Cytotoxicity of the Sg-CuO NPs was determined using anIC50 of 37 μg/mL. Sg-CuO NPs have shown promising anti-diabetic, anti-oxidant, protein degradation-inhibiting, and anti-microbial properties. Our findings have shown that synthesized Sg-CuO NPs have biological activities that may be utilized to treat bacterial infections linked to hyperglycemia.
IOT is one of the standard data transfer technique used in day today applications like health monitoring, industrial data collection and in home security system. Security in data transmission is one of the major concerned research area in IOT. The existing methodologies is secure data transmission is not provided full fledge privacy for data collection and transmission. Hence, this paper proposed a new methodology to compute and secure the valuable data. The methodology to optimize the data collection is achieved in two different steps. The noise is added to original data in the first step to secure the original data. In second step different nodes in the network/clustered average data will be computed. Later the research methods is implementing to minimize the data loss. To show the performance of the optimal data collection and secure transmission we simulate different constraints of the network parameters and compared with existing methods. The developed algorithm proved that it is one of the better data collection technique.
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