Search

The rapid detection of uranium in water environment is of great significance for water purification and uranium recovery. In this work, ultrathin nickel hydroxide (Ni(OH)2) nanosheets was in-situ growth on carbon nanotubes (CNTs) to form Ni(OH)2@CNTs electrode for uranyl ion (UO22+) detection. The as-prepared Ni(OH)2@CNTs shows good sensitivity in the electrolyte containing UO22+ with the limit of detection of about 8.603 ppb. Moreover, the Ni(OH)2@CNTs also exhibits significant selectivity for UO22+ in complex wastewater with several interfering cations and anions. This work provides a novel Ni(OH)2@CNTs electrode for UO22+ detection from uranium-containing water. [ABSTRACT FROM AUTHOR]

Vibration signals collected in real industrial environments are usually limited and unlabeled. In this case, fault diagnosis methods based on deep learning tend to perform poorly. Previous work mainly used the unlabeled data of the same diagnostic object to improve the diagnostic accuracy, but it did not make full use of the easily available unlabeled signals from different sources. In this study, a signal momentum contrast for unsupervised representation learning (SMoCo) based on the contrastive learning algorithm—momentum contrast for unsupervised visual representation Learning (MoCo)—is proposed. It can learn how to automatically extract fault features from unlabeled data collected from different diagnostic objects and then transfer this ability to target diagnostic tasks. On the structure, SMoCo increases the stability by adding batch normalization to the multilayer perceptron (MLP) layer of MoCo and increases the flexibility by adding a predictor to the query network. Using the data augmentation method, SMoCo performs feature extraction on vibration signals from both time and frequency domains, which is called signal multimodal learning (SML). It has been proved by experiments that after pre-training with artificially injected fault bearing data, SMoCo can learn a powerful and robust feature extractor, which can greatly improve the accuracy no matter the target diagnostic data with different working conditions, different failure modes, or even different types of equipment from the pre-training dataset. When faced with the target diagnosis task, SMoCo can achieve accuracy far better than other representative methods in only a very short time, and its excellent robustness regarding the amount of data in both the unlabeled pre-training dataset and the target diagnosis dataset as well as the strong noise demonstrates its great potential and superiority in fault diagnosis. [ABSTRACT FROM AUTHOR]

Efficient removal of uranium from wastewater is of great significance to the environment and human health. Herein, a novel electrode composed of carbon felt-anchored phosphorus-functionalized tungsten trioxide nanosheets (P-WO 3 @CF) was designed for electrochemical extraction of UO 2 2+ from uranium-containing wastewater. The induced P in WO 3 @CF not only improves the conductivity to accelerate electron transfer, but also offers surface P O species to capture UO 2 2+ for subsequent electrochemical reduction. Besides, the decoration of WO 3 and P-WO 3 greatly enhanced the hydrophilicity of electrode. As a result, the required voltage was significantly reduced for the electrochemical extraction of UO 2 2+ by using P-WO 3 @CF. The extraction efficiency of UO 2 2+ reached 98.8 % within 240 min at −2 V (vs. Ag/AgCl) by the P-WO 3 @CF electrode. Moreover, the P-WO 3 @CF electrode also exhibited significant antijamming capability for UO 2 2+ extraction in complex aqueous environment with several interfering metal ions. This work provides a new strategy to design novel electrode for efficient extraction of uranium from aqueous solutions. [Display omitted] • A novel P-WO 3 @CF electrode was designed and synthesized for UO 2 2+ removal. • The hydrophilicity and electronic structures of WO 3 were significantly regulated. • U(VI) was efficiently reduced by P-WO 3 @CF via electrochemical reduction. • The P-WO 3 @CF exhibits high selectivity for UO 2 2+ removal form simulated wastewater. [ABSTRACT FROM AUTHOR]

• Propose an enhanced diagnostic method for aero-bearing under data imbalance. • Propose a pre-training network to improve the generalization ability of the diagnostic model. • Reconstruct GAN model to improve its sample generation and recognition ability. • Construct a new generator by integrating IN and DRN for better fitting ability. Rolling bearing is the key supporting component of aero-engines, of which fault diagnosis is very important to ensure its reliable operation and continuous airworthiness. However, the data imbalance problem caused by its complex and harsh environment restricts the intelligent diagnosis. This paper proposes a sample enhanced diagnostic method based on pre-training and auxiliary classifier Wasserstein generative adversarial network with gradient penalty (PT-WGAN-GP). Firstly, a pre-training network is proposed and incorporated into the discriminator and classifier of WGAN-GP for feature adaptive and efficient extraction. Meanwhile, a new generator is constructed by introducing a residual network and the instance batch to improve its data-fitting ability. Finally, the data-enhanced model, PT-WGAN-GP, can stably generate high-quality faulty samples, which balances the testing dataset and completes the optimization training of network structure. Two cases under imbalanced data have verified the effectiveness of the proposed method, as well as its superiority over other widely used methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • CrO x -modified Ni on ZSM-23 has high-performance in n -C 16 hydroisomerization. • Microporous ZSM-23 is suitable to prepare high-performance Ni m Cr n /ZSM-23 catalyst. • Particle size and electronic state of Ni is affected by the interaction of Ni with CrO x. • Ni 2.0 Cr 2.0 /ZSM-23 outperforms a Pt on ZSM-23 for n -C 16 hydroisomerization. • The addition of CrO x intensifies the interaction between the NiO x and ZSM-23 support. CrO x -doped Ni catalysts with different Ni/Cr mass ratios on ZSM-23 (Ni m Cr n /ZSM-23) were synthesized using a simple initial wet impregnation method. The influence of CrO x on Ni in the Ni m Cr n /ZSM-23 catalysts was investigated by combining XPS, IR-CO, H 2 -TPR, TEM, and EDX mapping. The obtained results suggested that an intimate contact was formed between Ni and CrO x in the catalysts. The CrO x -modified NiO x species strongly interacted with ZSM-23, facilitating the formation of small sized Ni nanoparticles. Meanwhile, the electronic density around Ni on the catalyst was increased due to the presence of adjacent CrO x. The reaction rate and TOFs over Ni 2.0 Cr 2.0 /ZSM-23 (1.13 × 10-2 mol•g-1•h−1 and 6.8 × 10-2 s−1) are much higher than over Ni 2.0 /ZSM-23 (0.88 × 10-2 mol•g-1•h−1 and 6.2 × 10-2 s−1) in the n -hexadecane hydroisomerization, illustrating the promotion of CrO x in the Ni-based catalyst. The Ni 2.0 Cr 2.0 /ZSM-23 catalyst exhibited the highest iso -hexadecanes yield of 82.9 %, as compared to Pt/ZSM-23 catalyst (78.9 %). [ABSTRACT FROM AUTHOR]

[Display omitted] • Combining electrospinning and hydrothermal for the synthesis of bismuth-modified SiO 2 nanofiber air filter (Bi@SNF). • The Bi@SNF consists of bismuth nanoparticles modified on the surface of SiO 2 fibers. • The Bi@SNF shows high PM0.3 removal (99.26%), low air resistance (113 Pa), high iodine adsorption capacity (1040 mg/g). • The Bi@SNF shows excellent filtration of aerosols and efficient adsorption of iodine gas in complex environments. Radioactive iodine aerosols released during spent fuel reprocessing pose a serious threat to public health. At present most air filters hardly achieve efficient filtration performance and effective adsorption of radioactive iodine gas at the same time, therefore it is of great significance to develop a multifunctional air filter. Herein, a bismuth-modified silica nanofiber air filter (Bi@SNF) with highly efficient filtration of particulate matter (PM) and adsorption of iodine gas was constructed by combining electrospinning nanofibers and bismuth nanoparticles. The prepared air filter has attractive features such as high specific surface area, hydrophobicity, high efficiency filtration performance toward PM0.3 (99.26 %, 113 Pa), high iodine gas adsorption capacity (1040 mg/g), and excellent stability in humidity, high temperature, acidic, and irradiated environments, which enables the filter to effectively purify iodine gas and particulate matter from iodine aerosols. The successful synthesis of this multifunctional air filter (Bi@SNF) with excellent overall performance provides a new solution for the purification of iodine aerosols in reprocessing plants, with the potential for application in practical working conditions. [ABSTRACT FROM AUTHOR]

Quercetin-germanium (Ge) (IV) complex was synthesized in the laboratory. The structure and physicochemical properties of the complex were characterized utilizing thermal and spectroscopic (UV-vis, IR and 1H NMR) analysis. The antitumor activity of quercetin-Ge (IV) compound was evaluated by the MTT method. The complex was subjected to biological tests in vitro using four tumor cell lines (PC-3, Hela, EC9706 and SPC-A-1). The quercetin-Ge (IV) complex showed significant cytotoxicity against four tumor cell lines. [ABSTRACT FROM AUTHOR]