Adsorptive Removal of Manganese Ions from Polluted Aqueous Media by Glauconite Clay-Functionalized Chitosan Nanocomposites

The presence of Mn(II) in water exceeding the permitted concentration limits declared by the World Health Organization (WHO) influences individuals, animals, and the ecosystem negatively. Therefore, there is a necessity for an efficient material to eliminate this potentially toxic element from wastewater. We herein focused on the adsorptive removal of Mn(II) ions from polluted aqueous media using natural Egyptian glauconite clay (G) and its nanocomposites with modified chitosan (CS). We applied modified chitosan with glutaraldehyde (GL), ethylenediaminetetraacetic acid (EDTA), sodium dodecyl sulfate (SDS), and cetyltrimethyl ammonium bromide (CTAB). The utilized nanocomposites were referred to as GL-CS/G, EDTA-GL-CS/G, SDS-CS/G, and CTAB-CS/G, respectively. The point of zero charge values of the materials were estimated. The adsorption properties of the G clay and its nanocomposites toward the removal of Mn(II) ions from polluted aqueous media as well as the adsorption mechanism were explored using a batch technique. The glauconite (G) and its nanocomposites: GL-CS/G, CTAB-CS/G, EDTA-GL-CS/G, and SDS-CS/G, exhibited maximum adsorption capacity values of 3.60, 24.0, 26.0, 27.0, and 27.9 mg g^?1, respectively. The adsorption results fitted well the Langmuir isotherm and pseudo-second-order kinetic models. The estimated thermodynamic parameters: ΔH° (from 1.03 to 5.55 kJ/mol) and ΔG° (from ? 14.5 to ? 18.8 kJ/mol), indicated that Mn(II) ion adsorption process was endothermic, spontaneous, and physisorption controlled. Furthermore, the obtained adsorption results are encouraging and revealing a great potentiality for using the modified adsorbents as accessible adsorbents for Mn(II) ion removal from polluted aqueous solutions, depending on their reusability, high stability, and good adsorption capacities.

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Environmentally Friendly Graphene Inks for Touch Screen Sensors

Graphene-based materials have attracted significant attention in many technological fields, but scaling up graphene-based technologies still faces substantial challenges. High-throughput top-down methods generally require hazardous, toxic, and high-boiling-point solvents. Here, an efficient and inexpensive strategy is proposed to produce graphene dispersions by liquid-phase exfoliation (LPE) through a combination of shear-mixing (SM) and tip sonication (TS) techniques, yielding highly concentrated graphene inks compatible with spray coating. The quality of graphene flakes (e.g., lateral size and thickness) and their concentration in the dispersions are compared using different spectroscopic and microscopy techniques. Several approaches (individual SM and TS, and their combination) are tested in three solvents (N-methyl-2-pyrrolidone, dimethylformamide, and cyrene). Interestingly, the combination of SM and TS in cyrene yields high-quality graphene dispersions, overcoming the environmental issues linked to the other two solvents. Starting from the cyrene dispersion, a graphene-based ink is prepared to spray-coat flexible electrodes and assemble a touch screen prototype. The electrodes feature a low sheet resistance (290 Ω □⁻¹) and high optical transmittance (78%), which provide the prototype with a high signal-to-noise ratio (14 dB) and multi-touch functionality (up to four simultaneous touches). These results illustrate a potential pathway toward the integration of LPE-graphene in commercial flexible electronics.     

Fabrication of different conductive matrix supported binary metal oxides for supercapacitors applications

Here, we have fabricated the spinel binary-metal oxide (FeCo₂O₄) via a solvent-free and cost-effective approach. The nanocomposites of the as-fabricated binary-metal spinel oxide have been prepared with three different conductive-matrices, namely r-GO, CNTs, and PANI, via ultra-sonication approach. The spinel phase and surface functionalities of the fabricated FeCo₂O₄ sample have been confirmed via XRD and FT-IR analyses, respectively. The morphological-structure and elemental composition of the fabricated samples have been probed via FESEM and EDX results. The role of added conductive-matrices in the improvement of the electrical conductivities of the fabricated nanocomposites has been investigated via I–V experiments. The electrochemical experiments, conducted in half-cell configuration, showed that FeCo₂O₄/PANI nanocomposite exhibited the highest specific capacitance (658.9 Fg^-1) than that of the remaining two nanocomposites. Furthermore, FeCo₂O₄/PANI nanocomposite exhibited excellent cyclic stability as it lost just 8.3% of its initial specific capacitance even after 3000 cyclic tests. The superior capacitive-activity of the FeCo₂O₄/PANI nanocomposite is accredited to its high conductivity, large surface area, and synergy effects between the pseudocapacitance derived from the PANI and FeCo₂O₄ nanostructure. The electrochemical and electrical measurements suggested that FeCo₂O₄/PANI nanostructure is an emerging contender for energy storage applications.     

Bee Bread Ameliorates Vascular Inflammation and Impaired Vasorelaxation in Obesity-Induced Vascular Damage Rat Model: The Role of eNOS/NO/cGMP-Signaling Pathway

Obesity and hyperlipidemia are major risk factors for developing vascular diseases. Bee bread (BB) has been reported to exhibit some biological actions, including anti-obesity and anti-hyperlipidemic. This study aims to investigate whether bee bread can ameliorate vascular inflammation and impaired vasorelaxation activity through eNOS/NO/cGMP pathway in obese rats. Forty male Sprague-Dawley rats were randomly divided into four groups (n = 10/group), namely: control (normal group), obese rats (OB group), obese rats treated with bee bread (0.5 g/kg/day, OB/BB group) and obese rats treated with orlistat (10 mg/kg/day, OB/OR group). The latter three groups were given a high-fat diet (HFD) for 6 weeks to induced obesity before being administered with their respective treatments for another 6 weeks. After 12 weeks of the total experimental period, rats in the OB group demonstrated significantly higher Lee obesity index, lipid profile (total cholesterol, triglyceride, low-density lipoprotein), aortic proinflammatory markers (tumor necrosis factor-α, nuclear factor-κβ), aortic structural damage and impairment in vasorelaxation response to acetylcholine (ACh). Bee bread significantly ameliorated the obesity-induced vascular damage manifested by improvements in the lipid profile, aortic inflammatory markers, and the impaired vasorelaxation activity by significantly enhancing nitric oxide release, promoting endothelial nitric oxide synthase (eNOS) and cyclic guanosine monophosphate (cGMP) immunoexpression. These findings suggest that the administration of bee bread ameliorates the impaired vasorelaxation response to ACh by improving eNOS/NO/cGMP-signaling pathway in obese rats, suggesting its vascular therapeutic role.     

A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest

Multimedia Tools and Applications - Nowadays, heart diseases are significantly contributing to deaths all over the world. Thus, heart-disease prediction has garnered considerable attention in the...     

Stability enhancement of polypyrrole thin film ammonia sensor by camphor sulfonic acid dopant

Journal of Materials Science: Materials in Electronics - The main weakness of polymer gas sensors is its stability. Here, we report stability enhancement of a 100 nm polypyrrole (PPy) thin...     

A voltammetric sensor for the detection of 2-aminophenol in the presence of bisphenol A using a carbon paste electrode modified with N-doped reduced graphene oxide/CuO nanocomposite and ionic liquid

Journal of Materials Science: Materials in Electronics - In present work, a novel and facile electrode was constructed using the modification of carbon paste electrode (CPE) with N-doped reduced...     

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.     

Meaningful Learning for Deep Facial Emotional Features

Neural Processing Letters - Facial expression is an important aspect to recognize emotions between humans. However, this task remains difficult for machines. Several approaches have been developed...     

Hybrid marine predators algorithm for image segmentation: analysis and validations

Naturally, to analyze an image accurately, all the similar objects within it should be separated to pay attention to the most important object for reaching more details and hence achieving better accuracy. Therefore, multilevel thresholding is an indispensable image processing technique in the field of image segmentation and is employed widely to separate those similar objects. However, with increasing thresholds, the existing image segmentation techniques might suffer from exponentially-grown computational cost and low accuracy due to local optima shortage. Therefore, in this paper, a new image segmentation algorithm based on the improved marine predators algorithm (MPA) is proposed. MPA is improved using a strategy to find a number of the worst solutions within the population then tries to search for other better ones for those solutions by moving them gradually towards the best solutions to avoid accelerating to local optima and randomly within the search space based on a certain probability. In addition, this number of the worst solutions is increased with the iteration. This strategy is known as the linearly increased worst solutions improvement strategy (LIS). Also, we suggested that apply the ranking strategy based on a novel updating scheme, namely ranking-based updating strategy (RUS), on the solutions that could find better solutions in the last number iterations, perIter, in the hope of finding better solutions near it. RUS updates the particles/solutions which could not find better solutions than the best-local one in a number of consecutive iterations, with those that are generated based on a novel updating strategy. LIS is integrated with MPA to produce a new segmentation meta-heuristic algorithm abbreviated as MPALS. Also, MPALS and RUS are combined to tackle ISP in a strong variant abbreviated as HMPA for overcoming the image segmentation problem. The two proposed algorithms are validated on 14 test images and compared with seven state-of-the-arts meta-heuristic algorithms. The experimental results show the effectiveness of HMPA with increasing the threshold levels compared to the seven state-of-the-arts algorithms when segmenting an image, while their performance is roughly the same for the image with a small threshold level.