Nickel-Based Bifunctional Cocatalyst to Enhance CdS Photocatalytic Hydrogen Production.

The design of nanocomposites as a light-capturing system applied in photocatalytic water splitting is an emerging area of research. In our study, a simple in situ photodeposition method was proposed for the synthesis of CdS nanoflowers modified by nickel-based bifunctional, i.e., Ni/Ni(OH)2, cocatalysts. The introduction of cocatalysts has demonstrated a notable enhancement in the photocatalytic hydrogen evolution efficiency of CdS. The quantity of cocatalysts supported on CdS played an important role in governing the light absorption capability and photocatalytic efficacy. Ni-CdS-10 showed the best photocatalytic activity of 30.51 mmol g-1 h-1, which was 1.8 times and 2.6 times higher activity than Pt-CdS-1 wt % and pure CdS, respectively. Mechanism studies with UV-vis DRS, photoluminescence, and Mott-Schottky plots revealed the intrinsic electric field created at the p-n Ni(OH)2/CdS junctions, which can effectively implement the transport and separation of photoinduced carriers. From linear sweep voltammetry, electrochemical impedance spectroscopy, and DFT calculation, both Ni(OH)2 and Ni can effectively decrease the Gibbs free energies of hydrogen adsorption and reduce the overpotential of hydrogen evolution. As a result, the efficiency of generating H2 through photocatalysis experienced significant improvement, and the participation of bifunctional cocatalysts further reduced the photocorrosion of CdS, enhanced stability, improved low price, and efficient photocatalyst production.

Scene Uyghur Recognition Based on Visual Prediction Enhancement.

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.

Insights into the occurrence, elimination efficiency and ecological risk of antibiotics in rural domestic wastewater treatment facilities along the Yangtze River Basin, China.

China is embarking on the treatment of rural domestic wastewater, but little information on the characteristics of antibiotics in the rural domestic wastewater is available. As one of the most important new-emerging pollutants, antibiotic has been explicitly proposed to be controlled and treated since the fifth plenary session of the 19th Central Committee of the Communist Party of China. Thus, the occurrence, elimination efficiency and ecological risk of antibiotics, as well as conventional wastewater quality parameters were investigated in influents and effluents from 41 rural domestic wastewater treatment facilities (RD-WWTFs) along the Yangtze River Basin. Results showed that elimination efficiencies of routinely monitored conventional pollutants in 33 RD-WWTFs (accounting for 80.5%) were effective based on conventional wastewater quality parameters. Of 39 target antibiotics selected, 26 ones were detected in the influents and effluents with a detection frequency (DF) between 2.4% and 100%. No significant decrease of antibiotics was observed in the effluents compared with that in the influents except chlortetracyclin (CTE) and tetracycline (TCs). The composition characteristics of antibiotics showed regional differences. Among the investigated antibiotics, ofloxacin (OFX), doxycycline (DOX), roxithromycin (ROX) and lincomycin (LIN) were the predominant, totally accounted for a median percentage of more than 60% of ΣAB concentrations in both influents and effluents. Ecological risk values of ΣABs showed that 11 effluents presented high risks (26.8%), 18 ones had moderate risks (43.9%), and the rest 12 ones showed low risks (29.3%). Moreover, oxytetracycline (OXY), norfloxacin (NOR), LIN and ROX was the dominant contributors to the ecological risk values. Overall, the elimination effect of antibiotics was limited in RD-WWTFs along the Yangtze River Basin, which was likely to pose potential adverse impacts on aquatic ecosystems.

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE: One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS: Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS: A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

Doping evolution of the magnetic excitations in the monolayer CuO2.

In this paper, we study theoretically the doping evolution behaviors of the magnetic excitations (MEs) in the monolayer CuO2 grown on Bi2Sr2CaCu2O8+δ substrate. For the undoped system, the MEs exhibit the low energy commensurate behavior around (π, π). They turn to be incommensurate when the system is slightly hole-doped. In the intermediate doping regime, the low energy MEs diminish gradually. They turn to be dominated by the high energy modes. With further doping, an exotic structure transition of the MEs occurs in the heavily hole-doped regime which is directly related to the Lifshitz transition. Distinct MEs are separated by the transition point around which the low energy MEs exhibit the ring-like structure around (0, 0). Before the transition, the MEs are dominated by the broad particle-hole continuum at very high energies. In contrast, across the transition point, two new low energy modes develop around (0, 0) and (π, π) attributing to the intrapocket and interpocket particle-hole scatterings, respectively.

Single-layer ZnMN2 (M = Si, Ge, Sn) zinc nitrides as promising photocatalysts.

Searching for two-dimensional semiconductor materials that are suitable for visible-light photocatalytic water splitting provides a sustainable solution to deal with the future energy crisis and environmental problems. Herein, based on first-principles calculations, single-layer ZnMN2 (M = Si, Ge, Sn) zinc nitrides are proposed as efficient photocatalysts for water splitting. Stability analyses show that the single-layer ZnMN2 zinc nitrides exhibit energetic and dynamical stability. The electronic properties reveal that all of the single-layer ZnMN2 zinc nitrides are semiconductors. Interestingly, single-layer ZnSnN2 is a direct band gap semiconductor with a desirable band gap (1.74 eV), and the optical adsorption spectrum confirms its optical absorption in the visible light region. The hydrogen evolution reaction (HER) calculations show that the catalytic activity for single-layer ZnMN2 (M = Ge, Sn) is better than that of single-layer ZnSiN2. Furthermore, the band gaps and band edge positions for the single-layer ZnMN2 zinc nitrides can be effectively tuned by biaxial strain. Especially, single-layer ZnGeN2 can be effectively tuned to match better with the redox potentials of water and enhance the light absorption in the visible light region at a tensile strain of 5%, which is confirmed by the corresponding optical absorption spectrum. Our results provide guidance for experimental synthesis efforts and future searches for single-layer materials suitable for photocatalytic water splitting.

Well-defined linear Au n (n = 2-4) chains encapsulated in SWCNTs: a DFT study.

One-dimensional (1D) gold nanostructures have been extensively studied due to their potential applications in nanoelectronic devices. Using first-principles calculations, composites consisting of a well-defined linear Au n (n = 2-4) chain encapsulated in a (9,0) single-walled carbon nanotube (SWCNT) were studied. The translational energy barrier of a single Au atom in a (9,0) SWCNT was found to be 0.03 eV. This low barrier guaranteed the formation of Au n @ (9,0) SWCNT (n = 1-4) composites. Bond lengths, differential charge densities, and electronic band structures of the composites were studied. The average Au-Au bond lengths in the composites were found to be almost the same as those in the corresponding free-standing linear Au n . The average bond length increased as the number of Au atoms increased. Charge transfer in all of these composites was slight, although a few valence electrons were transferred from the (9,0) SWCNT and the Au chains to intercalations. The conductivities of the encapsulated linear Au n (n = 2-4) chains were enhanced to some extent by encapsulating them in the SWCNT.

Density functional theory calculations insight to the effect of anion on the nonlinear optical properties of LiInX2 (X = S, Se).

The linear and nonlinear optical susceptibilities for LiInS2 and LiInSe2 are studied by density functional theory using total energy pseudo potential plane wave method. The structure, band structure, density of states, and frequency-dependent complex dielectric function ε(ω) are calculated. The direct band gap of LiInSe2 is smaller than that of LiInS2. The birefringence Δn (0) is negative for LiInS2 and LiInSe2. The imaginary and real parts of the second-order susceptibility are evaluated. It is found that χ 333 ((2)) (ω) is the dominant component which shows the largest total Re χ ijk ((2)) (0) values. The contribution from the intra-band and inter-band to the second harmonic generation is increased when S is replaced by Se. It is found that a compound with smaller energy band gap has larger values of χ 333(0) which would lead to a possible route to further enhancement of Im χ 333(ω) by band gap engineering.

Density functional theory study of small X-doped Mg(n) (X = Fe, Co, Ni, n = 1-9) bimetallic clusters: equilibrium structures, stabilities, electronic and magnetic properties.

The geometries, stabilities, and electronic and magnetic properties of Mg(n) X (X = Fe, Co, Ni, n = 1-9) clusters were investigated systematically within the framework of the gradient-corrected density functional theory. The results show that the Mg(n)Fe, Mg(n)Co, and Mg(n)Ni clusters have similar geometric structures and that the X atom in Mg(n)X clusters prefers to be endohedrally doped. The average atomic binding energies, fragmentation energies, second-order differences in energy, and HOMO-LUMO gaps show that Mg4X (X = Fe, Co, Ni) clusters possess relatively high stability. Natural population analysis was performed and the results showed that the 3s and 4s electrons always transfer to the 3d and 4p orbitals in the bonding atoms, and that electrons also transfer from the Mg atoms to the doped atoms (Fe, Co, Ni). In addition, the spin magnetic moments were analyzed and compared. Several clusters, such as Mg1,2,3,4,5,6,8,9Fe, Mg1,2,4,5,6,8,9Co, and Mg1,2,5,6,7,9Ni, present high magnetic moments (4 µ(B), 3 µ(B), and 2 µ(B), respectively).