Highly Stable Gully-Network Co3O4 Nanowire Arrays as Battery-Type Electrode for Outstanding Supercapacitor Performance

3D transition metal oxides, especially constructed from the interconnected nanowires directly grown on conductive current collectors, are considered to be the most promising electrode material candidates for advanced supercapacitors because 3D network could simultaneously enhance the mechanical and electrochemical performance. The work about design, fabrication, and characterization of 3D gully-network Co3O4 nanowire arrays directly grown on Ni foam using a facile hydrothermal procedure followed by calcination treatment will be introduced. When evaluated as a binder-free battery-type electrode for supercapacitor, a high specific capacity of 582.8 C g−1 at a current density of 1 A g−1, a desirable rate capability with capacity retention about 84.8% at 20 A g−1, and an outstanding cycle performance of 93.1% capacity retention after 25,000 cycles can be achieved. More remarkably, an energy density of 33.8 W h kg−1 at a power density of 224 W kg−1 and wonderful cycling stability with 74% capacity retention after 10,000 cycles can be delivered based on the hybrid-supercapacitor with the as-prepared Co3O4 nanowire arrays as a positive electrode and active carbon as negative electrode. All the unexceptionable supercapacitive behaviors illustrates that our unique 3D gully-network structure Co3O4 nanowire arrays hold a great promise for constructing high-performance energy storage devices

A Surface Texture Prediction Model Based on RIOHTrack Asphalt Pavement Testing Data

The surface texture of asphalt pavement is of enormous importance to skid resistance. To investigate the degradation tendency of surface texture related to the skid resistance, four years of sensor measured texture depth (SMTD) panel data measured from 19 pavement structures including 4 types of surface asphalt layer are used to develop a surface texture prediction model. The panel data-based prediction model takes into account the dependence on time scale, diversified road sections, traffic factors, environmental factors, and the Bailey method-based aggregate gradation parameters. The regression and prediction capability of the surface texture model is evaluated from both short and long-term perspective. The results indicated that the random-effects model is the most suitable form to characterize the degradation of surface texture. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), and the Bailey method-based aggregate gradation parameters (the coarse aggregate ratio (CA), the fine aggregate coarse ratio (FAC), and the fine aggregate fine ratio (FAF)) have significant influences on the sensor measured texture depth. The time scale of the input sensor measured texture depth (SMTD) data has influence on the accuracy of the surface texture prediction model; therefore, long-term data series can ensure the robustness of long-term prediction. The results of this study can benefit the material design, construction, maintenance of asphalt pavement, and its evaluation pavement for longer service life

Research on State Evaluation of Petrochemical Plants Based on Improved TOPSIS Method and Combined Weight

Due to the involvement of hazardous materials and the potential serious accidents that may occur in petrochemical plants, it is of great significance to develop real-time state evaluation methods offering high performance. Data-driven methods have received widespread attention following the development of advanced condition-monitoring systems. However, scarce training samples evaluated under multiple operating conditions are available because of the high stability and reliability requirements of petrochemical plants. In this paper, a real-time state evaluation method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which circumvents dependence on data samples. First, the positive and negative ideal solutions of TOPSIS are determined using expert experience and the process index control limits of process cards. Then, fixed-value and fixed-interval indices are proposed to address the interval-optimal parameters. Subsequently, a new combined weight is established using the entropy method and the subjective weight coefficient. Finally, the above steps are integrated into an improved TOPSIS for the state evaluation of petrochemical plants. Experiments conducted on a fluid catalytic cracking (FCC) unit show that the proposed method can quantify the real-time operating status of a petrochemical plant. Furthermore, compared with the equal weight method, the evaluation result of combined weights is more aligned with the actual operating status

Extraction Optimization, Purification, Antioxidant Activity, and Preliminary Structural Characterization of Crude Polysaccharide from an Arctic Chlorella sp.

The arctic strain of Chlorella sp. (Chlorella-Arc) exists in the coldest and driest arctic ecosystems, and it is a new resource of active polysaccharides. The extraction of crude polysaccharide from Chlorella-Arc was optimized using the response surface methodology. A crude polysaccharide yield of approximately 9.62 ± 0.11% dry weight was obtained under these optimized conditions. Three fractions (P-I, P-II, and P-III) were present after purification by 2-diethylaminoethanol Sepharose Fast Flow and Sephadex G-100 chromatography. The P-IIa fraction demonstrated significant antioxidant activities. Moreover, P-IIa was an α- and β-type heteropolysaccharide with a pyran group and contained variable amounts of rhamnose, arabinose, glucose, and galactose based on fourier-transform infrared spectroscopy, high-performance liquid chromatography, and 1H and 13C nuclear magnetic resonance imaging. Production of high amounts of polysaccharide may allow further exploration of the microalgae Chlorella-Arc as a natural antioxidant

Quantitative Analysis of the Driving Factors of Water Quality Variations in the Minjiang River in Southwestern China

The Minjiang River is an important first-level tributary of the Yangtze River. Understanding the driving factors of water quality variations in the Minjiang River is crucial for future policy planning of watershed ecology protection of the Yangtze River. The water quality of the Minjiang River is impacted by both meteorological factors and anthropogenic factors. By using wavelet analysis, machine learning, and Shapley analysis approaches, the impacts of meteorological factors and anthropogenic factors on the permanganate index (CODMn) and ammonia nitrogen (NH3-N) concentrations at the outlet of the Minjiang River Basin were quantified. The observed CODMn and NH3-N concentration data in the Minjiang River from 2016 to 2020 were decomposed into long-term trend signals and periodic signals. The long-term trends in water qualities showed that anthropogenic factors were the major driving factors, accounting for 98.38% of the impact on CODMn concentrations and 98.18% of the impact on NH3-N concentrations. The periodic fluctuations in water qualities in the Minjiang River Basin were mainly controlled by meteorological factors, with an impact of 68.89% on CODMn concentrations and 63.94% on NH3-N concentrations. Compared to anthropogenic factors, meteorological factors have a greater impact on water quality in the Minjiang River Basin during both the high-temperature and rainy seasons from July to September and during the winter from December to February. The separate quantification of impacts of driving factors on the varying water quality signals contributed to the originality in this work, providing more intuitive insights for the assessment of the influences of policies and the climate change on the water quality