A B S T R A C TThe purpose of this study was to investigate the effect of graphene oxide nanoparticles modified with 4-aminodiphenylamine (GO-A) on the removal of toluene, ethylbenzene, and p-,o-xylene (TEX). Nano-sorbent was characterized using Fourier transform infrared resonance spectroscopy, carbon, hydrogen and nitrogen elemental analysis, BrunauerEmmett-Teller analysis, and transmission electron microscopy. The effect of different experimental parameters including contact time, pH, and initial concentration of adsorbate and adsorbent were examined. According to the results, an optimum TEX removal efficiency was observed at contact time = 5 min, pH 4, and adsorbent dose = 1 g/L at 20 mg/L initial TEX concentration. Besides, the point of zero charge (pH pzc ) was evaluated to be 4. The adsorption isotherms (Langmuir, Freundlich, and Dubinin-RadushKevich) and kinetics (pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Elovich) were used to indicate the isotherm and kinetic parameters. The adsorption process followed Langmuir isotherm and pseudo-second-order kinetic. Finally, GO-A was regenerated at seven cycles for the TEX removal confirming its good regeneration capacity.
Cardiovascular is arguably the most dominant death cause in the world. Heart functionality can be measured in various ways. Heart sounds are usually inspected in these experiments as they can unveil a variety of heart related diseases. This study tackles the lack of reliable models and high training times on a publicly available dataset. The heart sound set is provided by Physionet consisting of 3153 recordings, from which five seconds were fixed to evaluate to the developed method. In this work, we propose a novel method based on feature reduction combination, using Genetic Algorithm (GA) and Principal Component Analysis (PCA). The authors present eight dominant features in heart sound classification: mean duration of systole interval, the standard deviation of diastole interval, the absolute amplitude ratio of diastole to S2, S1 to systole and S1 to diastole, zero crossings, Centroid to Centroid distance (CCdis) and mean power in the 95–295 Hz range. These reduced features are then optimized respectively with two straightforward classification algorithms weighted k-NN with a lower-dimensional feature space and Linear SVM that uses a linear combination of all features to create a robust model, acquiring up to 98.15% accuracy, holding the best stats in the heart sound classification on a largely used dataset. According to the experiments done in this study, the developed method can be further explored for real world heart sound assessments.
a b s t r a c tThe main goal of this research was to evaluate the adsorption capacity of toluene, ethylbenzene and xylenes (TEX) onto graphene oxide nanoparticles grafted with polystyrene (GO-PS). Graphene oxide was polymerized using ammonium persulfate initiator. The properties of adsorbents were analyzed by Fourier transform infrared resonance spectroscopy, energy-dispersive X-ray spectroscopy, scanning electron microscopy and Brunauer-Emmett-Teller analysis. This paper includes the elements affective adsorption of TEX such as contact time, pH and adsorbent dose. The adsorption capacity was enhanced with the increasing of contact time and adsorbent dose, but changed insignificantly with pH. The findings demonstrated that an optimum TEX removal efficiency was achieved at contact time of 30 min and adsorbent dose of 1 g/L at 20 mg/L initial TEX concentration (solution pH = 7). The different models were applied to predict the mechanisms of adsorption. The isotherm and kinetic models which best displayed the outcome obtained were the Freundlich model and pseudo-second-order kinetic for TEX, respectively. In addition to the main aim of present study, GO-PS was regenerated for nine cycles and the reused adsorbent exhibited the adsorption ability equivalent to the original, approximately.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.