Adsorption of Co(II) from aqueous solution using municipal sludge biochar modified by HNO3.

Here adsorption studies were proposed on a carboxylated sludge biochar (CSB) material modified by HNO3 to assess its capacity in the removal of cobalt from aqueous solution. The as-prepared sludge biochar material was characterized by Brunauer-Emmett-Teller (BET) analysis, Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), energy-dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). The isotherm process could be well described by the Langmuir isotherm model. The adsorption kinetics indicated that cobalt adsorption followed a pseudo-second-order kinetics model. The mechanism between Co(II) and biochar involved electrostatic interaction, ion exchange, surface complexation and physical function. The adsorption capacity on CSB was as high as 72.27 mg·g-1, surpassing original sludge biochar (SB) as CSB had abundant oxygen-containing functional groups and many hydroxyls, plus the BET surface areas increased when SB was modified by HNO3, which stimulated adsorption effect. Therefore, this work shows that CSB could be used as an efficient adsorbent to remove Co(II) in wastewater.

Efficient elimination of Cr(VI) from aqueous solutions using sodium dodecyl sulfate intercalated molybdenum disulfide.

In recent years, the heavy metal ions have been immoderately released into the ecological system and result in potential hazardous to public health. Herein, the sodium dodecyl sulfate intercalated molybdenum disulfide (SDS-MoS2) was synthesized for the adsorption of Cr(VI). The SDS molecule was flat and vertically intercalated into the interlayer of MoS2, which was further evidenced by density functional theory calculations. The capture of Cr(VI) on the sphere-like SDS-MoS2 relied on solution pH. The retention of Cr(VI) on SDS-MoS2 attained 63.92 mg/g, and the removal process was endothermic, spontaneous and increased with temperature increasing. The main removal mechanism of Cr(VI) onto SDS-MoS2 was Cr(VI) fixing on the surface of the composites by chemisorption involving possible Cr-S coordination bonding. More importantly, Cr(VI) passed into the increased interlamination and reacted at the interlamination of SDS-MoS2, which was further proved at molecular level. The results can provide critical information for the application of SDS-MoS2 in Cr(VI) elimination or other kinds of pollutants removal in natural aquatic environment.

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN.

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.

Novel back propagation optimization by Cuckoo Search algorithm.

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

RNON: image inpainting via repair network and optimization network.

In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information. However, the existing premier convolutional neural networks methods usually causes the problems of excessive color difference and image texture loss and distortion phenomenon. The paper has proposed an effective image inpainting method using generative adversarial networks, which is composed of two mutually independent generative confrontation networks. Among them, the image repair network module aims to solve the problem of repairing the irregular missing areas of the image, and its generator is based on a partial convolutional network. The image optimization network module aims to solve the problem of local chromatic aberration in the repaired images, and its generator has based on deep residual networks. Through the synergy of the two network modules, the visual effect and image quality of the images has improved. The experimental results can show that the proposed method (RNON) performs better from comparisons of qualitative and quantitative evaluations with state-of-the-arts in image inpainting quality field.

Comprehensive Evaluation of Raw Eating Quality in 81 Sweet Potato (Ipomoea batatas (L.) Lam) Varieties.

The raw eating quality of sweet potato is complex. As consumers start paying more attention to the raw eating quality of tuberous roots in sweet potato, the evaluation of the raw eating quality of sweet potato is becoming an important issue. Therefore, we measured 16 quality indicators in 81 varieties of sweet potato. It was found that these 16 quality traits had different coefficients of variation (C.V.). Among them, the C.V. of fructose, glucose, and adhesiveness were the largest: 87.95%, 87.43% and 55.09%, respectively. The cluster analysis method was used to define six categories of the different tuberous roots of sweet potato. Group I, III, and IV had a stronger hardness and higher starch and cellulose content. Groups II, V, and VI were softer, with a high moisture and soluble sugar content. The principal component analysis method was used to comprehensively evaluate 16 quality indicators of 81 sweet potato varieties. It was found that Futian1, Taishu14, and Nanshu022 are good varieties in terms of raw eating quality. These varieties have low hardness, high adhesiveness in texture, high soluble sugar content, and low starch and cellulose. Future research should focus on improving the raw eating quality of sweet potato by reducing hardness, starch, and cellulose, while increasing adhesiveness, soluble sugar, and moisture content.

Simulated adsorption of iodine by an amino-metal-organic framework modified with covalent bonds.

Radioactive iodine in nuclear waste is increasingly harmful to the human body and the environment because of its strong radioactivity, high fluidity, easy solubility in water, and long half-life. It is very important to find clean and economical materials to recover and fix radioactive iodine. In this paper, the amino-metal-organic framework was covalently modified to obtain composite materials to improve the recycling of iodine in the environment. These adsorbents are used to adsorb iodine in water, showing outstanding adsorption performance. The adsorption data are in good agreement with the Langmuir isothermal adsorption model and pseudo-second-order kinetic model, indicating that the adsorption process is mainly monolayer adsorption and chemical adsorption. The two materials showed selective adsorption capacity for iodine in the solution containing multiple competing ions. The adsorption capacity of the covalently modified composite increased from 949.52 to 2157.44 mg/g. Compared with the amino-metal-organic framework, the modified composite contains more electron-rich groups as active sites, and forms charge transfer compounds with iodine to realize chemical adsorption. Through the simulated adsorption of ultra-high-pressure micro-jet, the material has certain working ability under high pressure, which provides a theoretical basis for the future recovery and utilization of iodine under high pressure.

Zinc-based triazole metal complexes for efficient iodine adsorption in water.

Radioactive iodine is extremely harmful to the environment, and it is of great significance to develop materials that efficiently remove iodine. We prepared two triazole metal complexes with simple method, denoted as Zn(tr)(OAc) and Zn(ttr)(OAc), which were used to adsorb iodine from aqueous solution. The properties and adsorption mechanism of the two materials were studied by different techniques including XRD, SEM, N2 porosimetry at 77 K, FTIR, TGA, elemental analysis (EDS), and X-ray photoelectron spectroscopy (XPS). The results showed that both materials had good water and thermal stability. Pseudo-second-order kinetic model was better at describing the iodine adsorption kinetics onto the adsorbents. It was proved that chemical adsorption dominated, iodine mainly enriched on the materials in the form of I3-1. Zn(ttr)(OAc) had a higher adsorption capacity than Zn(tr)(OAc) due to the electron-donating group -NH2. The maximum adsorption capacity of the two materials for iodine reached 714.501 mg·g-1 and 846.108 mg·g-1 at 25 °C.

Interlayer Quasi-Bonding Interactions in 2D Layered Materials: A Classification According to the Occupancy of Involved Energy Bands.

Recent studies have revealed that the interlayer interaction in two-dimensional (2D) layered materials is not simply of van der Waals character but could coexist with quasi-bonding character. Herein, we classify the interlayer quasi-bonding interactions into two main categories (I: homo-occupancy interaction; II: hetero-occupancy interaction) according to the occupancy of the involved energy bands near the Fermi level. We then investigate the quasi-bonding-interaction-induced band structure evolution of several representative 2D materials based on density functional theory calculations. Further calculations confirm that this classification is applicable to generic 2D layered materials and provide a unified understanding of the total strength of interlayer interaction, which is a synergetic effect of the van der Waals attraction and the quasi-bonding interaction. The latter is stabilizing in main category II and destabilizing in main category I. Thus, the total interlayer interaction strength is relatively stronger in category II and weaker in category I.

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders.

Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders. DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs). And then, subclusters are merged into individuals by comparing the density of edge samples. By iteration of the merger, the suitable dc and cluster centers are ensured. Finally, we compared state-of-the-art methods with our proposal in public datasets. Experiments show that our algorithm automatically determines cutoff distance and centers accurately.

Removal of boron from water by GO/ZIF-67 hybrid material adsorption.

With the development of the boron industry, boron pollution is getting more and more serious, and excessive boron will harm human health. In this paper, graphene oxide was used as a template to prepare ZIF-67, and GO/ZIF-67 was successfully prepared. GO/ZIF-67 was used for the first time to remove boron from water. SEM, XRD, and other characterization methods were used to confirm the structure. The adsorption kinetics, adsorption isotherm, adsorption thermodynamics, and adsorption mechanism of boron by GO/ZIF-67 were studied in this paper. The adsorption capacity of GO/ZIF-67 for boron is up to 66.65 mg·g-1 at 25 °C, and adsorption process reaches equilibrium in 400 min. Adsorption kinetics indicates that the adsorption process conforms to the pseudo-first-order kinetic model, and adsorption thermodynamics indicates that the adsorption process is a spontaneous endothermic process controlled by entropy change. The adsorption capacity of boron by GO/ZIF-67 does not decrease significantly after four cycles. The adsorption of boron by GO/ZIF-67 has both chemical and physical adsorption. From Zeta potential and adsorption kinetics, it can be seen that there is physical adsorption during the adsorption process and boron mainly has positive charge on the surface of GO/ZIF-67 and graphene oxide hydroxyl bonding. Based on the adsorption thermodynamics and XPS, it is known that there is chemisorption during the adsorption process, and mainly the combination of boron and cobalt sites.

4-(4-Diethyl-amino-2-hydroxy-benzyl-idene-ammonio)-3-methyl-benzene-sulfonate dihydrate.

In the crystal of the title compound, C(18)H(22)N(2)O(4)S·2H(2)O, mol-ecules are linked into a one-dimensional chain structure by C-H⋯O, N-H⋯O, O-H⋯O and O-H⋯N hydrogen bonds.

Hexaaqua-cobalt(II) bis-(4-amino-3-methyl-benzene-sulfonate).

In the title mol-ecular salt, [Co(H(2)O)(6)](C(7)H(8)NO(3)S)(2), the Co(2+) cation lies on an inversion centre. In the crystal, the components are linked by N-H⋯O and O-H⋯O hydrogen bonds, thereby generating sheets parallel to (001).

Hexaaqua-magnesium bis-(4-amino-3-methyl-benzene-sulfonate).

In the title mol-ecular salt, [Mg(H(2)O)(6)](C(7)H(8)NO(3)S)(2), the Mg(2+) cation lies on an inversion centre. In the crystal, the components are linked by N-H⋯O and O-H⋯O hydrogen bonds, thereby generating sheets parallel to (001).

Bis(thio-semicarbazide)nickel(II) bis-[2-(thio-semicarbazonometh-yl)benzene-sulfonate] dihydrate.

In the title compound, [Ni(CH(5)N(3)S)(2)](C(8)H(8)N(3)O(3)S(2))(2)·2H(2)O, the Ni(II) atom lies on a inversion centre and is four-coordinated by two N and two S atoms of two thio-semicarbazide ligands in an almost square-planar coordination. In the crystal structure, the molecules are linked into a three-dimensional network via C-H⋯O, C-H⋯N, N-H⋯O, N-H⋯S and O-H⋯O hydrogen bonds.

Rationally designed core-shell and yolk-shell magnetic titanate nanosheets for efficient U(VI) adsorption performance.

The hierarchical core-shell and yolk-shell magnetic titanate nanosheets (Fe3O4@TNS) were successfully synthesized by employing magnetic nanoparticles (NPs) as interior core and intercrossed titanate nanostructures (NSs) as exterior shell. The as-prepared magnetic Fe3O4@TNS nanosheets had high specific areas (114.9 m2 g-1 for core-shell Fe3O4@TNS and 130.1 m2 g-1 for yolk-shell Fe3O4@TNS). Taking advantage of the unique multilayer structure, the nanosheets were suitable for eliminating U(VI) from polluted water environment. The sorption was strongly affected by pH values and weakly influenced by ionic strength, suggesting that the sorption of U(VI) on Fe3O4@TNS was mainly dominated by ion exchange and outer-sphere surface complexion. The maximum sorption capacities (Qmax) calculated from the Langmuir model were 68.59, 121.36 and 264.55 mg g-1 for core-shell Fe3O4@TNS and 82.85, 173.01 and 283.29 mg g-1 for yolk-shell Fe3O4@TNS, at 298 K, 313 K and 328 K, respectively. Thermodynamic parameters (ΔH0, ΔS0 and ΔG0) demonstrated that the sorption process was endothermic and spontaneous. Based on X-ray photoelectron spectroscopy (XPS) analyses, the sorption mechanism was confirmed to be cation-exchange between interlayered Na+ and UO22+. The yolk-shell Fe3O4@TNS had more extraordinary sorption efficiency than core-shell Fe3O4@TNS since the yolk-shell structure provided internal void space inside the titanate shell to accommodate more exchangeable active sites. The flexible recollection and high efficient sorption capacity made core-shell and yolk-shell Fe3O4@TNS nanosheets promising materials to eliminate U(VI) or other actinides in wastewater cleanup applications.

The role of graphene oxide and graphene oxide-based nanomaterials in the removal of pharmaceuticals from aqueous media: a review.

In this review paper, the ill effects of pharmaceuticals (PhAs) on the environment and their adsorption on graphene oxide (GO) and graphene oxide-based (GO-based) nanomaterials have been summarised and discussed. The adsorption of prominent PhAs discussed herein includes beta-blockers (atenolol and propranolol), antibiotics (tetracycline, ciprofloxacin and sulfamethoxazole), pharmaceutically active compounds (carbamazepine) and analgesics such as diclofenac. The adsorption of PhAs strictly depends upon the experimental conditions such as pH, adsorbent and adsorbate concentrations, temperature, ionic strength, etc. To understand the adsorption mechanism and feasibility of the adsorption process, the adsorption isotherms, thermodynamics and kinetic studies were also considered. Except for some cases, GO and its derivatives show excellent adsorption capacities for PhAs, which is crucial for their applications in the environmental pollution cleanup.

The study of randomized visual saliency detection algorithm.

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

The research and application of visual saliency and adaptive support vector machine in target tracking field.

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.