Nitrogen fixation is crucial for plants as it is utilized for the biosynthesis of almost all biomolecules. Most of our atmosphere consists of nitrogen, but plants cannot straightforwardly assimilate this from the air, and natural nitrogen fixation is inadequate to meet the extreme necessities of global nutrition. In this study, nitrogen fixation in water was achieved by an AC-driven non-thermal atmospheric pressure nitrogen plasma jet. In addition, Mg, Al, or Zn was immersed in the water, which neutralized the plasma-treated water and increased the rate of nitrogen reduction to ammonia due to the additional hydrogen generated by the reaction between the plasma-generated acid and metal. The effect of the plasma-activated water, with and without metal ions, on germination and growth in corn plants (Zea Mays) was investigated. The germination rate was found to be higher with plasma-treated water and more efficient in the presence of metal ions. Stem lengths and germination rates were significantly increased with respect to those produced by DI water irrigation. The plants responded to the abundance of nitrogen by producing intensely green leaves because of their increased chlorophyll and protein contents. Based on this report, non-thermal plasma reactors could be used to substantially enhance seed germination and seedling growth.

This study aimed to develop magnetic Fe₃O₄/sugarcane bagasse activated carbon composite for the adsorption of arsenic (III) from aqueous solutions. Activated carbon (AC) was prepared from sugarcane bagasse by chemical activation using H₃PO₄ as an activating agent at 400 °C. To enhance adsorption capacity for arsenic, the resultant AC was composited with Fe₃O₄ particles by facile one-pot hydrothermal treatment. This method involves mixing the AC with aqueous solution of iron (II) chloride tetrahydrate, polyvinyl pyrrolidone (PVP), and ethanol. Batch adsorption experiments were conducted for the adsorption of As (III) onto the composite. The effects of pH, adsorbent dosage, and contact time on the arsenic adsorption were studied. The result showed that the composite could remove the arsenic from the water far more effectively than the plain AC. The highest percentage of arsenic removal was found at pH at 8, adsorbent dose of 1.8 g/L, and contact time of 60 min. Langmuir and Freundlich adsorption isotherm was used to analyze the equilibrium experimental data. Langmuir model showed the best fit compared to the Freundlich model with a maximal capacity of 6.69 mg/g. These findings indicated that magnetic Fe₃O₄/sugarcane bagasse AC composite could be potentially applied for adsorptive removal of arsenic (III) from aqueous solutions.

In the recent era, the need for modern smart grid system leads to the selection of optimized analysis and planning for power generation and management. Renewable sources like wind energy play a vital role to support the modern smart grid system. However, it requires a proper commitment for scheduling of generating units, which needs proper load frequency control and unit commitment problem. In this research area, a novel methodology has been suggested, named Harris hawks optimizer (HHO), to solve the frequency constraint issues. The suggested algorithm was tested and examined for several regular benchmark functions like unimodal, multi-modal, and fixed dimension to solve the numerical optimization problem. The comparison was carried out for various existing models and simulation results demonstrate that the projected algorithm illustrates better results towards load frequency control problem of smart grid arrangement as compared with existing optimization models.

There is an increasing concentration in the influences of nonconventional power sources on power system process and management, as the application of these sources upsurges worldwide. Renewable energy technologies are one of the best technologies for generating electrical power with zero fuel cost, a clean environment, and are available almost throughout the year. Some of the widespread renewable energy sources are tidal energy, geothermal energy, wind energy, and solar energy. Among many renewable energy sources, wind and solar energy sources are more popular because they are easy to install and operate. Due to their high flexibility, wind and solar power generation units are easily integrated with conventional power generation systems. Traditional generating units primarily use synchronous generators that enable them to ensure the process during significant transient errors. If massive wind generation is faltered due to error, it may harm the power system’s operation and lead to the load frequency control issue. This work proposes binary moth flame optimizer (MFO) variants to mitigate the frequency constraint issue. Two different binary variants are implemented for improving the performance of MFO for discrete optimization problems. The proposed model was evaluated and compared with existing algorithms in terms of standard testing benchmarks and showed improved results in terms of average and standard deviation.

This paper compared the effects of A. indica plant proteins over chemical methods in the morphology of zinc oxide nanoparticles (ZnO NPs) prepared by a co-precipitation method, and ethanol sensing performance of prepared thin films deposited over a fluorene-doped tin oxide (FTO) bind glass substrate using spray pyrolysis technique. The average crystallite sizes and diameters of the grain-sized cluster ZnO NPs were 25 and (701.79 ± 176.21) nm for an undoped sample and 20 and (489.99 ± 112.96) nm for A. india dye-doped sample. The fourier transform infrared spectroscopy (FTIR) analysis confirmed the formation of the Zn–O bond at 450 cm⁻¹, and also showed the presence of plant proteins due to A. indica dye extracts. ZnO NPs films exhibited good response (up to 51 and 72% for without and with A. indica dye-doped extracts, respectively) toward ethanol vapors with quick response-recovery characteristics at a temperature of 250 °C for undoped and 225 °C for A. indica dye-doped ZnO thin films. The interaction of A. indica dye extracts helps to decrease the operating temperature and increased the response and recovery rates of the sensor, which may be due to an increase in the specific surface area, resulting in adsorption of more oxygen and hence high response results.

Optimization is a broad field for researchers to develop new algorithms for solving various types of problems. There are various popular techniques being worked on for improvement. Grey wolf optimization (GWO) is one such algorithm because it is efficient, simple to use, and easy to implement. However, GWO has several drawbacks as it is stuck in local optima, has a low convergence rate, and has poor exploration. Several attempts have been made recently to overcome these drawbacks. This paper discusses some strategies that can be applied to GWO to overcome its drawbacks. This article proposes a novel algorithm to enhance the convergence rate, which was poor in GWO, and it is also compared with the other optimization algorithms. GWO also has the limitation of becoming stuck in local optima when used in complex functions or in a large search space, so these issues are further addressed. The most remarkable factor is that GWO purely depends on the initialization constraints such as population size and wolf initial positions. This study demonstrates the improved position of the wolf by applying strategies with the same population size. As a result, this novel algorithm has enhanced its exploration capability compared to other algorithms presented, and statistical results are also presented to demonstrate its superiority.