Distribution of Platinum and Palladium between Dissolved,Nanoparticulate, and Microparticulate Fractions of Road Dust

Ageing processes of vehicle catalytic converters inevitably lead to the release of Pt and Pd into the environment, road dust being the main sink. Though Pt and Pd are contained in catalytic converters in nanoparticulate metallic form, under environmental conditions, they can be transformed into toxic dissolved species. In the present work, the distribution of Pt and Pd between dissolved, nanoparticulate, and microparticulate fractions of Moscow road dust is assessed. The total concentrations of Pt and Pd in dust vary in the ranges 9–142 ng (mean 35) and 155–456 (mean 235) ng g⁻¹, respectively. The nanoparticulate and dissolved species of Pt and Pd in dust were studied using single particle inductively coupled plasma mass spectrometry. The median sizes of nanoparticulate Pt and Pd were 7 and 13 nm, respectively. The nanoparticulate fraction of Pt and Pd in Moscow dust is only about 1.6–1.8%. The average contents of dissolved fraction of Pt and Pd are 10.4% and 4.1%, respectively. The major fractions of Pt and Pd (88–94%) in road dust are associated with microparticles. Although the microparticulate fractions of Pt and Pd are relatively stable, they may become dissolved under changing environmental conditions and, hence, transformed into toxic species.     

Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches

In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m² on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R² values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

坝前淤泥土与新填坝体接触面抗剪强度试验研究及工程应用

坝前淤泥面与新填筑坝体接触面是"坝前淤泥面加坝"技术的薄弱环节.为了探究不同工况下淤泥坝基、新填筑坝体,以及淤泥土-新填土接触面的剪切强度特性,以宁夏南部山区西吉县南川水库坝前淤泥面加坝工程为研究对象,通过常规应变式直剪仪、改进常规应变式直剪仪分别进行剪切试验,分析了淤泥土含水率和压实度变化时,淤泥土、新填土、淤泥土-...     

形状记忆聚合物表面弹性薄膜的屈曲研究1)

形状记忆聚合物具有形状变化后在特定条件下可恢复的特点,因此作为一种柔性基底材料在柔性电子中得到广泛应用。对于形状记忆聚合物基底和弹性薄膜组成的双层结构,当 基底收缩时,其表面的弹性薄膜可以形成屈曲波形。针对基底收缩过程中波形的变化, 本文实验测得形状记忆聚合物材料在不同温度下的 属性,结合一维应变恢复函数,利用柔性基底表面薄膜屈曲波形参数(波幅、波长等)表达式,求解得到了在基底收缩的过程中,弹性薄膜屈曲波形的变化规律,和实验结果吻合很好。     

Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory’s viability and performance were implemented using IBM’s Qiskit library.     

孔梯度陶瓷纤维复合膜管的制备及特性

陶瓷过滤管具有孔隙率高、耐腐蚀、耐高温、机械强度高、便于清洗、使用寿命长等优点,是高温烟尘处理用的高效过滤元件.本文研制了一种具有梯度孔结构堇青石陶瓷纤维复合膜过滤元件,该过滤元件是由多孔支撑体、过渡层和分离膜层组成.其中支撑体、过渡层和分离层的气孔率分别为35～40;、50～60;和60～70;.文中主要分析了孔梯度陶瓷纤维复合膜管的材料结构和抗热震性能,同时对复合膜管进行含尘气体过滤的冷态模拟试验.对于烟气中粒径大于或等于0.1μm的颗粒,复合膜管的截留率达到99.8;以上.     

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique,Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.     

湍流大气中的准直激光束III.分形结构与相位不连续点饶瑞中

利用数值方法研究了不同起伏条件下准直激光束在湍流大气中的传播,分析了光斑的分形维数以及相位不连续点数目的统计特征。结果表明：随着起伏条件的增大,光斑的分形维数以及相位不连续点数目增大;光斑的分形维数与锐度（描述光斑质量的参量）有一定的关系,但相位不连续点数目与光斑质量不存在确定的关系。在本文的计算条件下,分形维数一直随起伏条件的增大而增大,没有出现类似于闪烁饱和的现象。在一定的起伏条件下,相位不连续点数目具有一定的统计分布,而不是一个确定的值,并且具有相当的发散性。     

Dust modelling using a combined CFD and discrete element formulation

The production and dispersal of airborne dust is an important issue in both environmental and industrial contexts. Dust pollution is a major environmental concern, and long exposure in occupational settings has been linked with numerous respiratory health issues. Industrial dust pollution can also present a significant explosion hazard, even in facilities with dust extraction systems. Computational models for dust generation and dispersal have, however, generally been formulated for specific geophysical applications and restricted to static, two‐dimensional, approaches. Here, we present a method for simulating dust production from a dynamic granular bed by using a three‐dimensional coupled discrete element method and Navier–Stokes computational model. Dust production is based on an energy formulation in which micro‐scale dust particles are assumed to overcome cohesion to macro‐scale grains. This model is used over the entire range of energies present within the system, from macro‐scale collisions to aerodynamic entrainment and bombardment of micro‐scale particles. The dust concentration is modelled as a scalar density field, which is advected and diffused through turbulence in the gas flow field. The model is tested against empirical predictions for two test cases, a slug of granular material dropped from a set height and air flow over a granular stockpile. Both give good agreement to the empirical relations, showing that the model can accurately predict the production and subsequent dispersal of dust from a dynamic granular bed. Copyright © 2012 John Wiley & Sons, Ltd.