Global deterioration of water, soil, and atmosphere by the release of toxic chemicals from the ongoing anthropogenic activities is becoming a serious problem throughout the world. This poses numerous issues relevant to ecosystem and human health that intensify the application challenges of conventional treatment technologies. Therefore, this review sheds the light on the recent progresses in nanotechnology and its vital role to encompass the imperative demand to monitor and treat the emerging hazardous wastes with lower cost, less energy, as well as higher efficiency. Essentially, the key aspects of this account are to briefly outline the advantages of nanotechnology over conventional treatment technologies and to relevantly highlight the treatment applications of some nanomaterials (e.g., carbon-based nanoparticles, antibacterial nanoparticles, and metal oxide nanoparticles) in the following environments: (1) air (treatment of greenhouse gases, volatile organic compounds, and bioaerosols via adsorption, photocatalytic degradation, thermal decomposition, and air filtration processes), (2) soil (application of nanomaterials as amendment agents for phytoremediation processes and utilization of stabilizers to enhance their performance), and (3) water (removal of organic pollutants, heavy metals, pathogens through adsorption, membrane processes, photocatalysis, and disinfection processes).
Future demand of rice is projected to increase with the increase of global population. However, the presence of bacteria, insects, and fungi has resulted in various changes in the physical and chemical characteristics of rice grain. To make it worse, the overuse of post-harvest chemicals (fungicide and pesticide) has caused possible risks to human health through either occupational or non-occupational exposure. For the last few years, cold plasma has been developed as an alternative non-thermal emerging technology for rice grains treatment due to its ability to inactivate or decontaminate pathogens without causing thermal damage and free of any harmful residues. Therefore, this review describes the operational mechanism of cold plasma treatment technology on rice grains, existing reactor system designs, and parameters influenced by the treatment technology (reactor design parameters and treatment process parameters). Possible advanced investigation on future reactor design modification as well as standard operating range of influenced parameters were suggested for improved efficiency and effectiveness of cold plasma treatment.
In this work, principal component analysis (PCA) was utilized to analyze laser-induced breakdown spectroscopy (LIBS) signals of the extracted chicken fat, lamb fat, beef fat, and lard froze using two different freezing methods. The frozen samples were ablated using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with a wavelength of 1064 nm, 170 mJ pulse energy, and 6 ns pulse duration to produce plasma on target surfaces. The samples were ablated using 30-60 shots of the laser beam at different spots. Stronger LIBS signals from the extracted chicken fat and lamb fat were obtained with liquid nitrogen (LN2) method. However, LIBS signals obtained from the freezer freezing method were found to be stronger for extracted beef fat and lard. The PCA was then used to visualize the LIBS spectra of extracted animal fats into a score plot. Data points of each extracted animal fat were divided into three groups representing LIBS spectra collected at the early, middle, and end part of the ablation process. The score plot revealed that the data points of the three groups of frozen extracted animal fats using the LN2 method were more closely clustered than those frozen in the freezer. Good discrimination with 97% of the variance was achieved between the extracted chicken fat, lamb fat, beef fat, and lard using the LN2 method in the three-dimensional score plot. LIBS signals of the extracted animal fats produced from the LN2 method were found to be more stable than those from the freezer method.
In this study, carbon species were grown on the surface of Ni-impregnated powder activated carbon to form a novel hybrid carbon nanomaterial by chemical vapor deposition. The carbon nanomaterial was obtained by the precipitation of the methane elemental carbon atoms on the surface of the Ni catalyst. The physiochemical properties of the hybrid material were characterized to illustrate the successful growth of carbon species on the carbon substrate. The response surface methodology was used for the evaluation of adsorption parameters effect such as pH, adsorbent dose and contact time on the percentage removal of MB dye from aqueous solution. The optimum conditions were found to be pH = 11, adsorbent dose = 15 mg and contact time of 120 min. The material we prepared showed excellent removal efficiency of 96% for initial MB concentration of 50 mg/L. The adsorption of MB was described accurately by the pseudo-second-order model with R2 of 0.998 and qe of 163.93 (mg/g). The adsorption system showed the best agreement with Langmuir model with R2 of 0.989 and maximum adsorption capacity (Qm) of 250 mg/g.
In this study two deep eutectic solvents (DESs) were prepared using ethylene glycol (EG) and two different ammonium-based salts. The potential of these DESs as novel agents for CNTs functionalization was examined by performing a comprehensive characterization study to identify the changes developing after the functionalization process. The impact of DESs was obvious by increasing the surface area of CNTs to reach 197.8 (m2/g), and by adding new functional groups to CNTs surface without causing any damage to the unique structure of CNTs. Moreover, CNTs functionalized with DESs were applied as new adsorbents for the removal of methyl orange (MO) from water. The adsorption conditions were optimized using RSM-CCD experimental design. The kinetics and the equilibrium adsorption data were analyzed using different kinetic and isotherm models. According to the regression results, adsorption kinetics data were well described by pseudo-second order model, whereas adsorption isotherm data were best represented by Langmuir isotherm model. The highest recorded maximum adsorption capacity (qmax) value was found to be 310.2 mg/g.
In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.
In the last few decays, the fiber-optic was employed in the field of sensing because of its benefits in contrast to other types of sensors such as small size, easy to fabricate, high response, and flexibility. In this study, unclad single mode fiber-optic sensor is proposed to operate at 650 nm wavelength. COMSOL Multiphysics 5.1 finite element method (FEM) is used to design the sensor and tested it theoretically. The middle portion of the fiber cladding is removed and replaced by gold nanoparticles (Au NPs) of 50 nm thickness. Analytic layer of 3 μm thickness was immersed in different liquids in range of refractive index (RI) from 1.000281 to 1.39. These liquids are NaCl Deionized (DI) water solution, sucrose-Deionized (DI) water solution, and glycerol solution Deionized (DI) water. It was found that the highest obtained sensitivity and resolution are for glycerol-DI water solution with value of 3157.98 (nm/RIU) and 3.16 × 10-5 (RIU), respectively. Furthermore, it is easy to fabricate and of low cost. In experiments, pulsed laser ablation (PLA) was used to prepare Au NPs. X-ray diffraction (XRD) shown that the peak of the intensity grew as the ablated energy increased as well as the structure crystallization. Transmission electron microscopy (TEM) revealed an average diameter of 30 nm at the three ablated energies, while X-ray spectroscopy (EDX) spectrum has indicated the presence of Au NPs in the prepared solution. The photoluminescence (PL) and ultraviolet-visible UV-Vis transmission were used to study the optical properties of the prepared Au NPs. An optical spectrum analyzer was used to obtain the sensor's output results. It has shown that best intensity was obtained for sucrose which confined with theoretical results.