• Energy Research

Shampoo prepares surfactants to remove dirt, surface grease, and skin debris from hair, shaft, and scalp without harming the user. This study aimed to formulate herbal shampoo using Ethiopian plant extracts and evaluate their quality parameters. Herbal shampoos were formulated using seed oil extracts of Lepidium sativum L. and Ricinus communis L., and essential oil extracts of Lippia adeonsis Hochst ex. Walp koseret, along with other ingredients. The formulated herbal shampoos were evaluated for their organoleptic and physicochemical properties such as pH, surface tension, viscosity, dirt dispersion, antimicrobial activities, and stability. When the investigation data were assessed, the formulations were pleasant and attractive, produced sufficient foam, and showed suitable cleansing activities. The pH values were in a range 5.90 ± 0.22–7.45 ± 0.19, and lower surface tension (23.39 ± 0.20–31.89 ± 1.04 dyne/cm) together with acceptable viscosity with good shearing properties were obtained. The formulated products exhibited good antimicrobial activities against Escherichia coli, Staphylococcus aureus, and Aspergillus niger. When the surface morphologies of hair samples were examined using a scanning electron microscope (SEM), a clean and smooth surface was observed for treated samples, comparable to that of the marketed shampoo. This result indicated that the formulated herbal shampoo is good, having acceptable properties at par with commercially available shampoos. However, further investigation, research, and development are required to improve its quality and safety.

There is a clear need to make energy cheap, readily accessible and green, while ensuring its production does not contribute to further climate change. Of all the options available, photovoltaics offer the highest probability of delivering a meaningful and sustainable change in the way society produces its energy. One approach to the development of such photovoltaics involves the use of polymers. These systems offer the advantages of cheap production, flexibility (and hence a range of deployment opportunities) and tunability of light absorption. However, there are issues with polymer-based photovoltaic systems and one significant effort to improve these systems has involved the use of carbon nanotubes (CNTs). This review will focus on those efforts. CNTs have been used in virtually every component of the devices to help charge conduction, improve electrode flexibility and in some cases as active light absorbing materials.

Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb). In the present age, due to the rapid increase in antibiotic resistance worldwide, TB has become a major threat to human life. Regardless of significant efforts have been inclined to improve the healthcare systems for improving diagnosis, treatment, and anticipatory measures controlling TB is challenging. To date, there are no such therapeutic chemical agents available to fight or control the bacterial drug-resistance. The catalase-peroxidase enzyme (katG) which encoded by the katG gene of Mtb is most frequently getting mutated and hence promotes Isoniazid resistance by diminishing the normal activity of katG enzyme. In the current study, an effort has been intended to find novel and therapeutically active antibacterial chemical compounds through pharmacoinformatics methodologies. Initially, the five mutant katG were generated by making mutation of Ser315 by Thr, Ile, Arg, Asn, and Gly followed by structural optimizations. About eight thousand small molecules were collected from the Asinex antibacterial library. All molecules were docked to active site of five mutant katG and wild type katG. To narrow down the chemical space several criteria were imposed including, screening for highest binding affinity towards katG proteins, compounds satisfying various criterion of drug-likeliness properties like Lipinski's rule of five (RO5), Veber's rule, absorption, distribution, metabolism, and excretion (ADME) profile, and synthetic accessibility. Finally, five molecules were found to be important antibacterial katG inhibitors. All the analyzed parameters suggested that selected molecules are promising in nature. Binding interactions analysis revealed that proposed molecules are efficient enough to form a number of strong binding interactions with katG proteins. Dynamic behavior of the proposed molecules with katG protein was evaluated through 100 ns molecular dynamics (MD) simulation study. Parameters calculated from the MD simulation trajectories adjudged that all molecules can form stable complexes with katG. High binding free energy of all proposed molecules definitely suggested strong affection towards the katG. Hence, it can be concluded that proposed molecules might be used as antibacterial chemical component subjected to experimental validation.

Biological synthesis of silver nanoparticles (AgNPs) is a green, simple, cost-effective, time-efficient, and single-step method. This study mainly focused on the synthesis of silver nanoparticles (AgNPs) using essential oil of Laggera tomentosa (LTEO) and investigates their potential applications. Ultraviolet-Visible (UV-Vis) result showed the characteristic Surface Plasmon Resonance (SPR) peak of LTEO-AgNPs at 420 nm. Fourier transform infrared (FT-IR) spectroscopy indicated the functional groups present in LTEO and LTEO-AgNPs. Scanning electron microscope (SEM) image depicted the synthesized AgNPs mainly has spherical shapes with average nanoparticles size 89.59 ± 5.14 nm. Energy dispersive X-ray (EDX) peak at 3.0 keV showed the presence of Ag element in LTEO-AgNPs. The X-ray diffraction (XRD) peaks at 38°, 44° and 67° are assigned to (111), (200), and (220), respectively which displays the crystal nature of LTEO-AgNPs. The average particle size and zeta potential of LTEO-AgNPs were determined as 94.98 nm and −49.6 mV, respectively. LTEO-AgNPs were stable for six months against aggregation at room temperature. LTEO-AgNPs solutions exhibited potential activities for the treatment of some pathogenic bacteria species, agricultural productivity growth, determination of metallic ions, and catalytic reduction. This study is the first work to report nanoparticles synthesis using L. tomentosa extracts and evaluate their potential applications.

Modern-day practices are the major contributors in water quality deterioration, consequently results in clean water scarcity. Herein, co-precipitation procedure was adopted to develop a nanomagnetic copper ferrite/drumstick pod biomass (CuFe2O4/DC) composite, which was characterized, and optimized to sequester malachite green (MG) and lead (Pb(II)) in unary and binary systems from aqueous environment. Mesoporous CuFe2O4/DC surface with 16.96 m2/g BET surface area and acid functionalities predominance was observed. Under the studied experimental conditions, MG adsorption on CuFe2O4/DC in unary system was comparatively higher than that of Pb(II). MG and Pb(II) equilibrium results were fitted to Langmuir isotherm model, their respective maximum monolayer adsorption capacities at 328 K being 952.4 and 921.1 mg/g. On the other hand, binary system (in presence of MG) fastened Pb(II) adsorption kinetics and increased its uptake capacity. Additionally, humic acid (HA) matrix enhanced Pb(II) adsorption kinetics. Recovery studies showed maximal MG and Pb(II) elution with C2H5OH and 0.1 mol/L HCl, respectively. An 82.7% drop in Pb(II) adsorption was found after the first regeneration cycle, while only 17.6% fall in MG adsorption was witnessed after five consecutive regeneration cycles. Hence, it could be concluded that CuFe2O4/DC is a cost-effective and promising adsorbent for an efficient and rapid removal of Pb(II) and MG from both unary and binary systems.

Syngas has been utilized in the production of chemicals and fuels, as well as in the creation of electricity. Feedstock impurities, such as nitrogen, sulfur, chlorine, and ash, in syngas have a negative impact on downstream processes. Fischer–Tropsch synthesis is a process that relies heavily on temperature to increase the production of liquid fuels (FTS). In this study, waste biomass converted into activated carbon and then a carbon-supported iron-based catalyst was prepared. The catalyst at 200 °C and 350 °C was used to investigate the influence of temperature on the subsequent application of syngas to liquid fuels. Potassium (K) was used as a structural promoter in the Fe-C catalyst to boost catalyst activity and structural stability (Fe-C-K). Low temperatures (200 °C) cause 60% and 80% of diesel generation, respectively, without and with potassium promoter. At high temperatures (350 °C), the amount of gasoline produced is 36% without potassium promoter, and 72% with promoter. Iron carbon-supported catalysts with potassium promoter increase gasoline conversion from 36.4% (Fe-C) to 72.5% (Fe-C-K), and diesel conversion from 60.8% (Fe-C) to 80.0% (Fe-C-K). As seen by SEM pictures, iron particles with potassium promoter were found to be equally distributed on the surface of activated carbon.

Recently, the preparation of smart multifunctional hybrid nanoparticles has captured significant interest in versatile areas, including medicine, environment, and food, due to their enhanced physicochemical properties. The present study focuses on the synthesis of Fe-doped NiO nanoparticles by the coprecipitation method using the sources of nickel (II) acetate tetrahydrate and iron (III) nitrate nonahydrate. The prepared Fe-doped NiO nanoparticles are characterized by X-ray diffraction, Fourier transform infrared spectroscopy, UV–visible spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, and X-ray photon spectroscopic analysis. The XRD results clearly confirm the face-centered cubic structure and polycrystalline nature of the synthesized Fe-NiO nanoparticles. The Tauc plot analysis revealed that the bandgap energy of the Fe-doped NiO nanoparticles decreased with the increasing concentration of the Fe dopant from 2% to 8%. The XPS analysis of the samples exhibited the existence of elements, including Fe, Ni, and O, with the absence of any surplus compounds. The FE-SEM and TEM analyses proved the formation of nanostructured Fe-NiO with few spherical and mostly unevenly shaped particles. Further, the photocatalytic efficiency of the prepared Fe-doped NiO nanoparticles were identified by using the cationic dye rhodamine B (Rh-B). The photocatalytic results proved the 8% of Fe doped with NiO nanoparticles achieved 99% of Rh-B degradation within 40 min of visible-light irradiation. Hence, the results of the present study exemplified the Fe-doped NiO nanoparticles have acted as a noticeable photocatalyst to degrade the Rh-B dye.