Dynamic release process of pollutants during suspended sediment transport in aquatic system

Pollutants release is highly consistent with suspended sediment concentration （SSC） in water column, especially during re-suspension and transport events. The present research focuses on pollutant dynamic release from re-suspended sediment, especially the vertical distribution relationship between them. The sediment erosion experiments on a series of uniform flow are conducted in a circulate flume. Reactive tracer （phosphorus） is used as the contaminant in fine-grained sediments to identify the release characteristic length and time. Experimental results show that the flow condition near-bed depends on the sediment surface roughness. The region with high turbulent intensities corresponds to a high concentration sediment layer. In addition, the SSC decreases with the distance, water depth, and particle grain size. The sediment in a smaller grain size takes much more time to reach equilibrium concentration. Total phosphorus （TP） concentration changes along the water depth as SSC in the initial re-suspension stage, appearing in two obvious concentration regimes： the upper low-concentration layer and the high-concentration near-bottom layer. This layered phenomenon remains for about 3 hours until SSC distri- bution tends to be uniform. Longitudinal desorption plays an important role in long-way transport to reduce the amount of suspended sediment in water column.     

Fabrication of robust superhydrophobic coatings using PTFE‐MWCNT nanocomposite: Supercritical fluid processing

Fabrication of polymer‐carbon composite nanostructure with good dispersion of each other is critical for the desired application due to the nanostructure flaws, agglomeration, and poor absorption between the 2 materials. Fabrication of superhydrophobic surface coating composites of polytetrafluoroethylene (PTFE) with multiwalled carbon nanotubes (MWCNTs) through supercritical fluid processing was explored in this study. Homogeneity of the composite was characterized by X‐ray diffraction and Raman spectroscopy studies, which reveal that the PTFE and MWCNT are uniform in the composite. Microstructural surface evaluation of field‐emission scanning electron microscope and high‐resolution transmission electron microscope studies display that the coating composite possesses roughness structures and fibrillation of the superhydrophobic surface coating. Superhydrophobic character was evaluated on fiber‐reinforced plastic (FRP) sheets, which showed that the prepared coating composite surface showed self‐cleaning properties with a high water contact angle of 162.7°. The surface wettability was studied by increasing different temperatures (30°C to 300°C) in PTFE‐MWCNT composite, which reveals that the FRP sheets were thermally stable up to 200°C and afterward; they transformed from superhydrophobic to hydrophilic state at 250°C. The superhydrophobic surfaces are thermally stable in extreme environmental conditions, and this technique may be used and extendable for large‐scale applications.     

Cyclic welding behavior of covalent adaptable network polymers

Surface welding effect of covalent adaptable network (CAN) polymers enables self‐healing, reprocessing and recycling of thermosets, but little is known about their welding behaviors during repeated welding‐peeling cycles. In this article, we study the cyclic welding effect of an epoxy based thermal‐sensitive CAN. Surface roughness is generated by rubbing the sample on sandpapers with different grid sizes. The welding‐peeling cycles are repeated on the same pair of samples for five times, with roughness amplitude and interfacial fracture energy measured in each cycle. It is shown that the roughness gradually decreases during the repeated welding cycles, especially when a long welding time or high welding pressure is applied. Even though lower roughness amplitude promotes the contact area, the interfacial fracture energy reduces due to the increased BER activation energy after long‐time heating. A multiscale constitutive model is adopted, where we incorporate an explicit expression of interfacial contact area as a function of root‐mean‐square roughness parameter. The model is able to capture the evolving interfacial fracture energy during repeated welding cycles by using the measured roughness parameter, network modulus and BER activation energy. The study provides theoretical basis for the design and applications of CANs involving cyclic welding‐peeling operations. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018 , 56, 402–413.     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Effect of Rough Nanoparticle Orientation on DLVO Energy Interaction

This study investigated the effect of the orientation of rough nanoparticles on Derjaguin and Landau, Verwey and Overbeek (DLVO) energy interaction. Rippled sphere model was used to survey van der Waals attraction energy and repulsive double-layer interaction energy between nanoparticles. The effect of particle size, asperity size, number of asperities, and concentration was studied for different orientations. Spherical coordinates were used to evaluate the effect of changes in orientation under controlled conditions. Surface element integral method was used to calculate DLVO energy interactions between rough particles at specific orientations. The DLVO energy results show that a change in orientation significantly affected the stability of the nanoparticles. The stability of dispersion varied as the contact surface between nanoparticles changed.     

基于偏振特性对石英玻璃和绿漆涂层的反演

物质的偏振特性与其复折射率、表面粗糙度以及观测几何条件有关,为了应用偏振探测技术实现对目标的定量反演,本文首先对两种典型目标(绿漆涂层和石英玻璃)的偏振特性进行了实验测定,并对偏振度与探测天顶角的关系进行了分析。利用实验数据并基于描述目标偏振特性的PG模型首次在考虑了粗糙度的影响下,对目标的折射率、消光系数进行了定量反演,最后将反演结果与参考结果进行比较。结果表明,石英玻璃的折射率相对误差为4.944 9%,绿漆涂层的折射率与消光系数的相对误差分别为11%和21.558 9%。该方法在考虑表面粗糙度的条件下能够更精确地测定物质的复折射率,同时也为偏振技术应用于目标定量反演提供了依据。     

Characterization of the channel walls roughness in photonic crystal fibers

We present electron microscope (FEI NanoSEM) and atomic force microscopy measurements of surface roughness in nanochannels in photonic crystal fibers (PCF). A method was invented to cleave the PCF along the axis without damaging the surface structure in the nanochannels allowing us to characterize the morphology of the nanochannels in the PCF. A multi-wall carbon nanotube mounted onto commercial AFM probes and super sharp silicon non-contact mode AFM probes were used to characterize the wall roughness in the nanochannels. The roughness is shown to have a Gaussian distribution, and has an amplitude smaller than 0.5 nm. The height–height correlation function is an exponential correlation function with an autocorrelation length of 13 nm, and 27 nm corresponding with scan sizes of 200×100 nm², and 1600×200 nm², respectively.     

Surface Roughness Characterization of Poly(methylmethacrylate) Films with Immobilized Eu(III) β-Diketonates by Fractal Analysis

The structural complexity of the 3-D surface of poly(methylmethacrylate) films with immobilized europium β-diketonates was studied by atomic force microscopy and fractal analysis. Fractal analysis of surface roughness revealed that the 3-D surface has fractal geometry at the nanometer scale. Poly(methylmethacrylate) (PMMA) as immobilization matrix is dense and uniform, and a tendency for formation of chain structures was observed. Fractal analysis can quantify key elements of 3-D surface roughness such as the fractal dimensions D _f determined by the morphological envelopes method of the Eu(DBM)₃ and Eu(DBM)₃ · dpp nanostructures, which are not taken into account by traditional surface statistical parameters.     

Physicochemical parameter influences and their optimization on the biosynthesis of MnO2 nanoparticles using Vernonia amygdalina leaf extract

The manganese dioxide nanoparticles (MnO₂ NPs) were synthesized using Vernonia amygdalina leaf extract which was used as a reducing, capping, and stabilizing agents due to the presence of bioactive phytochemical compounds. Twenty five runs were designed to investigate the effect of V. amygdalina leaf extract ratio (A), initial potassium permanganate (KMnO₄) concentration (B), pH (C), and reaction time (D) on the biosynthesized MnO₂ NPs using 4-factor, 4-level D-Optimal Response Surface Quadratic Design Model approach. The relationship between physicochemical variables and absorption responses were established using transform second degree polynomial quadratic model. The effects of each absorption responses were analyzed by ANOVA principle using quadratic equations. A very low p-values (<0.0001), non-significant Lack of Fit F-values, and reasonable regression coefficient values (coefficient R² = 0.9790, adjusted R² = 0.9496, and predicted R² = 0.8452) suggested that there is an effective correlation between experimental results and predicted values. Numerical and graphical optimized results demonstrated that the optimized conditions for the predicted absorbance at 320 nm (1.095) were suggested at 43.72%, 1.81 mM, 6.02, and 103.42 min for V. amygdalina leaf extract ratio, initial KMnO₄ concentration, pH, and reaction time, respectively. Under these optimal conditions, the average absorbance from four experimental run was recorded to be 0.9678. This result was very closest to the predicted values. The average size elucidated by X-ray diffraction (XRD) analysis was found in the range between 20 nm and 22 nm. The stretching/or and vibrational, surface topography, thermal, and surface roughness as well as its porosity distributions were investigated by UV–Vis spectroscopy, Fourier transforms infrared (FTIR), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), and Gwyddion software analysis.     

扰动模糊集的粗糙度

首先,将扰动模糊集和粗糙集理论相结合,提出了粗糙扰动模糊集的概念并研究了其基本性质.接着,通过引进扰动模糊集水平上、下边界区域的概念,克服了粗糙集理论中普遍存在的两个集合的上近似的交不等于两个集合的交的上近似(两个集合的下近似的并不等于两个集合的并的下近似)的缺陷.最后,定义了依参数的扰动模糊集的粗糙度的定义,讨论了其基本性质.