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Everything around us is made up of atoms and molecules. The properties of quantum atoms are sought to understand the behavior of a particular object. But with the advent of research, it was discovered that there is a quantity smaller than the molecular size. The nanoscale measures a fraction of a billionth of a meter. The atom of an object measures 0.1 nm. Since atoms are the building blocks of matter, at the nanoscale one can combine these atoms to create new materials. The proposed model displays the properties of these nano-scale elements in modern medical applications. The nano-scale research of matter is fascinating because it is the basic phase in which atoms are held together. Therefore, by manipulating material at this level, one can create many different types of objects. This proposed model calculates the operation requirements and expects the results. Based on the operational requirements, the proposed model provides the suggestions. This will be helpful for the medical researchers to identify the proper medical treatments based on the microbiological requirements.

Abstract\nNovelty statementThe article details a feasibility study of removing Brilliant Green (BG), a mutagenic dye from an aqueous solution by adsorption using low-cost coriander seed spent as a by-product in the nutraceutical industry. The study includes an analysis of the parameters that affect the adsorption process. The variables that have been identified include pH, dye concentration, process temperature, adsorbent amount, and particle size of the adsorbent. To obtain information on the adsorption process and to design the mechanism of the adsorption system on experimental equilibrium, 10 isotherm models, namely, Langmuir, Freundlich, Jovanovic, Dubinin–Radushkevich, Sips, Redlich–Peterson, Toth, Vieth–Sladek, Brouers–Sotolongo, and Radke–Prausnitz were applied. It was discovered that the experimental adsorption capacity, qe, was roughly 110 mg g−1. The result has a maximum adsorption of 136.17 mg g−1 as predicted by Dubinin–Radushkevich isotherm. Diffusion film models, Dumwald–Wagner and Weber–Morris models, and pseudo-first- and second-order models, were used to determine the adsorption kinetics. It was realized that the adsorption kinetics data fit into a pseudo-second-order model. Thermodynamic analysis with a reduced enthalpy change suggests a physical process. The values of the thermodynamic parameters ΔG0, ΔH0, and ΔS0 demonstrated an endothermic and nearly spontaneous process of adsorption. The small valuation of ΔH0 specifies that the process is physical. FTIR spectroscopy and SEM imaging were used to confirm that the BG dye had been adsorbing on the adsorbent surface. The study concludes that NICSS is an effective adsorbent to extract BG dye from wastewater solutions, offers insights into numerous dye and adsorbent interaction possibilities and indicates that the process can be scaled to fit into the concept of circular economy.An attempt has been made to link the concept of circular economy through design and execution of the experiments in the laboratory scale. The following highlights will justify the newer approach adopted by the authors.The experiments are designed by intention to suit the concept of circular economy.The use of NICSS, a nutraceutical industrial spent, which has no feed, fertilizer, or fuel value suits the sustainability concept.The reuse of “waste” from the remediation process replaces the “end-of-life” concept in circular economy.An attempt has been made to link the concept of circular economy through design and execution of the experiments in the laboratory scale. The following highlights will justify the newer approach adopted by the authors.The experiments are designed by intention to suit the concept of circular economy.The use of NICSS, a nutraceutical industrial spent, which has no feed, fertilizer, or fuel value suits the sustainability concept.The reuse of “waste” from the remediation process replaces the “end-of-life” concept in circular economy. [ABSTRACT FROM AUTHOR]

This research aims to demonstrate that a one-of-a-kind microencapsulated bio-based PCM may be utilized in the passive collection of solar energy in buildings. The research makes use of a PCM with thermal properties within the comfort zone but is also able to track the performance of solar photovoltaic panels. This allows the research to be more accurate. Because of this, the research can produce outcomes that are more accurate. After finishing the step before this one, the next thing that needs to be done is creating novel composite concrete panels. This step follows the completion of the phase before it. The outcomes of the mechanical and thermal tests served as the basis for determining the best composition of the concrete mix that should be used for making this panel. The method concludes with the construction of a test structure using composite walls as the primary building material. [ABSTRACT FROM AUTHOR]

Solar energy measurements are often charted. These charts keep track of the conditions, events, and inspections done on each solar panel. This study describes a paradigm for measuring and monitoring solar energy via the Internet of Things. This service provides monitoring and technical help during corrective inspections within the quality reliability depth services range. These features and monitoring services can provide highly competent, higher-optimization, and excellent services that are safe, fast, and smooth. Compliance with local and international legal regulations, reliability of work, reduction of time and costs spent on corrective inspections, reduction of downtime and production losses, and management of monitoring and testing processes under expert coordination will all benefit from technical support services provided by approved companies for corrective inspections in the company. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are substances that can change their phase at a specific range of temperatures. This is the case in the temperature range and in the event that there is a brief gap between the supply of a particular quantity of energy and the demand for that quantity, these materials come in quite helpful. As a result, heating, ventilation, and air conditioning (HVAC) for the military, as well as waste heat recovery and thermal management. The simulation result of this proposed method is developed using Matlab and Simulink, the user is classified using a fuzzy logic control which helps to improve the energy consumption patterns which are they exhibit. Using this proposed method, we can perform several modes of operations which are toogled between the control, which achieves more amount of greater flexibility, also at same time it helps to reduce the usage of number of switches which are placed between various OM's. This is demonstrated by the fact that the fuzzy logic control is used. Therefore, the developed control logic reveals a promising potential for boosting the energy efficiency. We achieve nearly 85% of accuracy as a result which are very higher than comparing with the several existing methods. [ABSTRACT FROM AUTHOR]

The quantity of photovoltaic systems generated relies on the climate, and to generate significant variations depending on the climatic conditions. Its electricity production processes exhibit unpredictability, instability, and intermittent behavior. The augmented convolutional long-short memory combination network is recommended for PV energy combined forecasting in accordance to overcome the challenge of considerable instability in the high proportion of sustainable energy output. The LSTM and CNN models' training procedure uses the enhanced artificial bee colony (ABC) to adjust their characteristics. To determine the projected model parameters of PV power, the estimated input parameters of every model are stacked and then reassembled. The studies showed that the forecast precision was significantly higher than the reliability rate of the Convolutional LSTM combinational network as well as the forecasting findings from the BP, CNN, and LSTM independent computational methods. This forecast specificity was predicated on the enhanced ABC to optimize the variables of the CLSTM combinational network for forecasting Photo Voltaic power. For comparative study and assessment, the most recent operational data from a Photovoltaic power plant in northern China have been utilized. The suggested technique resolved more quickly and was higher weather-adaptable than the individual neural network and hybrid forecasting models mentioned prior. [ABSTRACT FROM AUTHOR]