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.     

浮筒式液面调节器设计

该文设计了一种新型的液面调节器：对浮筒式液面调节器的原理和结构进行了设计，并对调节器的技术指标和调节方法做了阐述，经实际使用证明，该调节器对液面调节与控制效果良好。     

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.     

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the ²⁴¹Am full energy peak, ¹³⁷Cs Compton edge, ¹³⁷Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3.     

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.     

pH计检定仪期间核查方法

介绍pH计检定仪的4种期间核查方法:传递比较法、多台对比法、留样测量法、实验室间比对法。传递比较法能直接反应pH计检定仪的变化,适用于已建立pH计检定仪检定装置的单位;多台对比法与留样测量法方便、快捷,可操作性强,适合大多数单位进行pH计检定仪的期间核查;实验室间比对法对设备要求较低,但需要协调的单位较多,周期较长,费用较高,实用性差。     

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.