Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Power Input Prediction in Agitated Gas‐Liquid Contactors with Non‐coalescent Batch

Volumetric mass transfer coefficients, k_La, just as power input are considered as essential parameters for mechanically agitated gas‐liquid contactors in relation to their optimization and design. The knowledge of power input is crucial for the prediction of other mass transfer characteristics. A power input correlation is created for the industrial design of the process with a non‐coalescent batch that would be appropriate for a broad range of operational conditions. The recommended resulting correlation is able to predict the power input for impellers in industrial‐scale design for a significant scope of operational conditions.     

碳酸盐岩岩溶储层多井评价方法及地质应用

多井评价是建立在单井精细解释对比分析基础上的预测储层及含油气性平面分布规律的技术方法,而常规测井资料无法区分碳酸盐岩岩溶储层,因而利用多井评价结果确定岩溶发育程度在平面上的分布规律就显得十分重要。为此,在岩心标定成像测井的基础上,对四川盆地高石梯—磨溪地区15口井的成像测井岩溶发育特征进行分析,建立了中二叠统茅口组岩溶发育各分带的标准成像图版,利用交会图及直方图分析各分带的常规测井响应特征,在此基础上形成了电成像测井刻度常规测井识别岩溶发育带的新方法。研究结果表明:①高石梯—磨溪地区茅口组岩溶带自上而下可划分为风化壳残积带、垂直渗流岩溶带、水平潜流岩溶带以及受岩溶作用较弱的基岩;②风化壳残积带在成像测井图像显示为"暗—亮—暗"条带状模式,垂直渗流岩溶带为垂直线状与暗色斑状组合模式,水平潜流岩溶带为水平线状—层状与斑状组合模式,基岩整体显示为亮色块状模式偶见线状或斑状特征;③有效储层主要发育在垂直渗流带和水平潜流带的顶部;④该区茅口组岩溶发育主要受裂缝发育控制,而裂缝发育又与断层关系密切。结论认为,该新方法对碳酸盐岩岩溶储层的多井评价具有普遍适用性,为四川盆地中二叠统风险探井的部署提供了技术支撑。     

冷链条件下气调包装酱卤鸭肉制品货架期预测模型的建立

目的 以气调包装酱卤鸭肉制品为研究对象,在冷链温度范围内建立一套准确、高效的货架期预测模型。方法 利用选择性培养基测定不同温度下产品各微生物数量,确定4~25℃条件下产品优势腐败菌。对乳酸菌数量与感官评定值进行了回归分析确定最小腐败量Ns。分别采用修正的Gompertz方程和平方根方程建立一、二级模型,并通过预测值与实测值对比验证模型的可靠性。结果 确定了4~25℃条件下产品优势腐败菌为乳酸菌,最小腐败量Ns=6.14(lg(cfu /g))。一、二级模型拟合度均良好,三种温度下模型预测值与实际值间的差异均在30％左右,波动幅度在10%以内。结论 实现了对4~25℃内任何时间点产品剩余货架期的预测,为冷链条件下气调包装酱卤鸭肉制品品质的变化提供了理论指导。     

Fabrication of BSCF-based mixed ionic-electronic conducting membrane by electrophoretic deposition for oxygen separation application

Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3-δ (BSCF), which exhibits a high mixed oxide ionic-electronic conduction, was used for the fabrication of an oxygen separation membrane. An asymmetric structure, which was a thin and dense BSCF membrane layer supported on a porous BSCF substrate, was fabricated by the electrophoretic deposition method (EPD). Porous BSCF supports were prepared by the uniaxial pressing method using a powder mixture with BSCF and starch as the pore-forming agent (0–50 wt.%). The sintering behaviors of the porous support and the thin layer were separately characterized by dilatometry to determine the co-fired temperature at which cracking did not occur. A crack-free and thin dense membrane layer, which had about a 15 μm thickness and >95% relative density, was obtained after optimizing the processes of EPD and sintering. The dense/porous interface was well-bonded and the oxygen permeation flux was 2.5 ml (STP) min⁻¹ cm^-2 at 850 °C.     

Prompting structure stability of O3–NaNi0.5Mn0.5O2 via effective surface regulation based on atomic layer deposition

Layered O3 type oxides exhibit promising prospects as high-performance cathodes for sodium-ion batteries (SIBs) due to their low cost and high theoretical capacities. Nevertheless, the intrinsic surface composition and bulk structure degradation upon cycling presents a huge obstacle to stable sodium-ion storage/transportation. Besides, the effective surface decoration on layered O3 oxides is still challenging through conventional wet chemical route owing to their extraordinarily high surface sensitivities. Herein, a typical O3 type layered oxide of NaNi_0.5Mn_0.5O₂ (NNMO) was selected and successfully encapsulated by precisely controlled Al₂O₃ layers via atomic layer deposition (ALD) technology. With the optimally controlled Al₂O₃ thickness of 3 nm, the surface regulated NNMO delivers a highly reversible capacity of 73.6 mA h g^-1, with a significantly improved capacity retention of 68.0% after 300 cycles at 0.5 C, and a superior rate capability of 65.8 mA h g^-1 at 10 C. Further air sensitivity tests demonstrate that the protective layer could effectively mitigate the generation of sodium-based impurities on NNMO, and reduce the surface sensitivities. Both chemical and electrochemical aging tests confirm the contribution of Al₂O₃ coating layer in alleviating ion dissolution as well as stabilizing the structure and morphology of NNMO. Based on regulating the surface of O3 type layered oxides utilizing ALD technique, this work supplies an effective and facile strategy to overcome the challenges from fast structure degradation and electrochemical property decay, which not only highlights the significance and effectiveness of surface engineering in secondary batteries, but also sheds light on accurate interface construction and regulation for active electrode materials, particularly for those sensitive to ambient atmosphere.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Charge injection in metal/organic/metal structures with ZnO:Al/organic interface modified by Zn1−xMgxO:Al layer

Aluminum-doped zinc oxide (ZnO:Al, AZO) electrodes were covered with very thin (∼6 nm) Zn_1−xMg_xO:Al (AMZO) layers grown by atomic layer deposition. They were tested as hole blocking/electron injecting contacts to organic semiconductors. Depending on the ALD growth conditions, the magnesium content at the film surface varied from x = 0 to x = 0.6. Magnesium was present only at the ZnO:Al surface and subsurface regions and did not diffuse into deeper parts of the layer. The work function of the AZO/AMZO (x = 0.3) film was 3.4 eV (based on the ultraviolet photoelectron spectroscopy). To investigate carrier injection properties of such contacts, single layer organic structures with either pentacene or 2,4-bis[4-(N,N-diisobutylamino)-2,6-dihydroxyphenyl] squaraine layers were prepared. Deposition of the AMZO layers with x = 0.3 resulted in a decrease of the reverse currents by 1–2 orders of magnitude and an improvement of the diode rectification. The AMZO layer improved hole blocking/electron injecting properties of the AZO electrodes. The analysis of the current-voltage characteristics by a differential approach revealed a richer injection and recombination mechanisms in the structures containing the additional AMZO layer. Among those mechanisms, monomolecular, bimolecular and superhigh injection were identified.     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.