Residual antibiotics in water are often persistent organic pollutants. The purpose of this study was to prepare a cellulose nanocrystals/graphene oxide composite (CNCs-GO) with a three-dimensional structure for the removal of the antibiotic levofloxacin hydrochloride (Levo-HCl) in water by adsorption. The scanning electron microscope, Fourier transform infrared (FT-IR), energy-dispersive spectroscopy, X-ray photoelectron spectroscopy and other characterization methods were used to study the physical structure and chemical properties of the CNCs-GO. The three-dimensional structure of the composite material rendered a high surface area and electrostatic attraction, resulting in increased adsorption capacity of the CNCs-GO for Levo-HCl. Based on the Box–Behnken design, the effects of different factors on the removal of Levo-HCl by the CNCs-GO were explored. The composite material exhibited good antibiotic adsorption capacity, with a removal percentage exceeding 80.1% at an optimal pH of 4, the adsorbent dosage of 1.0 g l −1 , initial pollutant concentration of 10.0 mg l −1 and contact time of 4 h. The adsorption isotherm was well fitted by the Sips model, and kinetics studies demonstrated that the adsorption process conformed to a quasi-second-order kinetics model. Consequently, the as-synthesized CNCs-GO demonstrates good potential for the effective removal of antibiotics such as levofloxacin hydrochloride from aqueous media.

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CNCs and CTM were combined to obtain a nanocomposite used to remove diuron in water. The adsorption of the nanocomposite was analyzed using response surface methodology, isothermal adsorption model and adsorption kinetics.

Abstract For the prediction of river flow sequence, owing to the non-stationariness and randomness of the sequence, the prediction accuracy of extreme river flow is not enough. In this study, the sparse factor of the loss function in a sparse autoencoder was enhanced using the inverse method of simulated annealing (ESA), and the river flow of the Kenswat Station in the Manas River Basin in northern Xinjiang, China, at 9:00, 15:00, and 20:00 daily during June, July, and August in 1998–2000 was considered as the study sequence. When the initial values of the sparse factor β0 are 5, 10, 15, 20, and 25, the experiment is designed with 60, 70, 80, 90, and 100 neurons, respectively, in the hidden layer to explore the relationship between the output characteristics of the hidden layer, and the original river flow sequence after the network is trained with various sparse factors and different numbers of neurons in the hidden layer. Meanwhile, the orthogonal experimental groups ESA1, ESA2, ESA3, ESA4, and ESA5 were designed to predict the daily average river flow in September 2000 and compared with the prediction results of the support vector machine (SVM) and the feedforward neural network (FFNN). The results indicate that after the ESA training, the output of the hidden layer consists a large number of features of the original river flow sequence, and the boundaries of these features can reflect the river flow series with large changes. The upper bound of the features can reflect the characteristics of the river flow during the flood. Meanwhile, the prediction results of the orthogonal experiment groups indicate that when the number of neurons in the hidden layer is 90 and β0 = 15, the ESA has the best prediction effect on the sequence. In particular, the fitting effect on the day of ‘swelling up’ of the river flow is more satisfactory than that of SVM and FFNN. The results are significant, as they provide a guide for exploring the evolution of the river flow under drought and flood as well as for optimally dispatching and managing water resources.

Herein, we report the facile two-step synthesis of an effective carboxylated cellulose nanofiber/montmorillonite nanocomposite (CMNFs–MMT) adsorbent for levofloxacin hydrochloride (Levo-HCl).