Resolving Concentrated Particle Size Mixtures Using Dynamic Light Scattering

Dynamic light scattering (DLS) is a technique used for measuring the size of molecules and particles undergoing Brownian motion by observing time‐dependent fluctuations in the intensity of scattered light. The measurement of samples using conventional DLS instrumentation is limited to low concentrations due to the onset of a phenomenon called multiple scattering. The problems of multiple scattering have been addressed in a light scattering instrument incorporating non‐ invasive backscatter optics (NIBS). This novel optic arrangement maximizes the detection of scattered light while maintaining signal quality and allows for measurements of turbid samples. This paper discusses the ability of backscatter detection to accurately determine particle sizes at 1 %w/v sample concentrations and demonstrates the correct resolution of different size populations using a series of latex standard mixtures with known volume ratios. The concentration of 1 %w/v is much higher than can be measured on conventional dynamic light scattering instruments.     

Continuous Size Classification of Nanoparticles Utilizing Brownian Motion in Microchannel Size Exclusion Chromatography

This study presents a new method for classifying the sizes of colloidal nanoparticles of below 100 nm in diameter in liquid dispersion using a microchannel size exclusion chromatography (SEC) chip. This chip can classify polydisperse colloidal nanoparticles containing a mix of two monodisperse nanoparticles into several monodisperse particle populations. The particles classified by the SEC chip are then sequentially analyzed by a photon correlation spectroscopy (PCS) method in combination with a flow cell. Two different pillar patterns of such SEC chips were used in experiments to investigate the effects of these patterns on the nanoparticle classification performance. The results obtained were compared with those from a numerical simulation. Standard polystyrene latex particles with diameters of 20 nm and 100 nm were used in this study. The usefulness of this methodology was verified since the simulation and measurement results were in good agreement with each other.     

In‐line Particle Size Measurement for Control of Jet Milling

Jet milling is suitable for the production of very fine powders with a well‐defined particle size distribution (PSD). The major disadvantage of the process is the poor energy efficiency. In practice jet mills have to be operated below their maximum capacity in order to avoid the production of off‐specification material, due to operation disturbances. Therefore, it is important to automate operation with a real‐time PSD instrument for quality control. In this paper, a study for in‐line particle size measurement using laser diffraction is presented to improve operation control. This measurement system uses a novel CMOS pixel array, which has a robust and flexible feature for the application. It also includes an in‐line cell system, where direct measurement is implemented in the product particle stream coming from the mill and an air purge for the cell window allows continued operation. Experiments were conducted on a 10‐inch spiral jet mill pilot plant with alpha‐Al₂O₃ as test material. The milling process is investigated by varying the operational conditions. The measurement results are useful to build a grinding model for control purpose.     

Estimation of Particle Size Distributions Using Pipet-Withdrawal Centrifuges

The mathematical treatments used in the evaluation of particle size distribution from pipet-withdrawal centrifuge data are reviewed. An error in the published procedure for multiple-sample withdrawal systems is pointed out and a corrected formulation is presented. An alternative treatment, based on a piece-wise linearization of the size distribution is described. Computer simulations of the analytical and data evaluation procedures indicate that the errors in the original formulation are not generally serious and that the corrected and alternative procedures lead to very similar estimates. Except for some cases involving very narrow size distributions, the alternative approach appears to give slightly closer estimates of the size distribution. Comparisons based on actual experimental data are also described.     

Shape Estimation of Anisometric Particles Using Size Measurement Techniques

This study was conducted to establish a simple method for evaluating the morphology of fine anisometric particles using size measurement techniques. The size distributions of mica particles and carbon fibers classified into narrower size ranges were measured by gravitational sedimentation and laser diffraction techniques. The ratio of mean diameters determined for flaky particles strongly depended on the aspect ratio, i.e. flatness. The relationship between particle shape and diameter is discussed theoretically. The experimental results were similar to those predicted. The flatness of fine particles can be evaluated by the ratio of the median diameter determined by laser diffraction to that determined by sedimentation.     

Particle Size Characterization of an Extra Fine Milled Product

The present research aims to characterize the particle size distribution of sub micron particles suspended in a liquid. The particles milled are an organic poorly water soluble crystalline product. To characterize the size of these particles, different techniques have been tested: imaging techniques (SEM, CryoTEM), static light scattering techniques, dynamic light scattering techniques, centrifugation and flow field flow fractionation. The results indicate that the studied milled particles have a primary particle size close to 180nm and there is strong evidence of larger particles which are very likely aggregates. This is clearly seen from the Cryo TEM results. All the above mentioned techniques should in principle be able to measure samples of dispersion containing particles of ca 180 nm but several are disturbed by the presence of large aggregates. It is difficult to estimate the amount of aggregate present, but most of the time one is interested in what the primary particle size distribution is. It is clear that no single piece of equipment is capable of exactly determining the particle size distribution of our samples, but the static light scattering with low shear on mixing does give a good representation of what is seen with the image analysis by cryo TEM.     

Particle Size of Pneumatically Conveyed Powders Measured Using Impact Duration

CSIRO Minerals has developed a technique for measuring particle size in pneumatically conveyed powders [1] by measurement of the acoustic waves produced by particle impacts upon a specially designed transducer. Previous work has focused on using the peak acoustic wave amplitude to determine particle size. This produces a spectrum that is hard to determine the particle size from, as the peak amplitude is a non‐linear function of particle diameter, and is strongly affected by angle of incidence and velocity of the impacting particle. In this paper impact duration measurements are used to overcome these difficulties while retaining the advantages of being able to measure in high solids loadings of up to at least 0.5 kg/m³ of powder. In laboratory tests the impact size monitor's (ISM) results have been correlated with optical diffraction measurements of the mean (by number) powder size with a correlation coefficient of 0.985 and a relative error of 5.5 %. The ISM operated successfully in the laboratory at a loading of 0.5 kg/m³ of powder and measured particles down to 50 microns in size.     

On-line Measurement of Solids Concentration or the Mean Particle Size in a Saturated Slurry Containing Background Particles Using a Turbidity Method

An on-line double-sensor turbidimeter for estimating the solids concentration or the mean particle size in a saturated slurry system in the presence of foreign insoluble particles is proposed. The correlation representing the operation of the double-sensor turbidimeter for on-line implementation is also developed. The technique relies on measuring the transmittance of an infrared light beam through the suspension, once in the presence of soluble particles and a second time when the soluble particles have completely dissolved. The device can be easily implemented on a crystallization system for monitoring and control applications. The maximum errors for solids concentration and mean particle size measurements were 13% and 7%, respectively.     

非独立模式算法下粒径分布反演及分类的研究

在光全散射法颗粒粒径测量中,提出一种非独立模式算法下粒径分布反演及分类的方法。对被测颗粒系分别按照不同的粒径分布函数同时进行反演,并依据反演误差大小判断被测颗粒系符合哪种分布函数。仿真实验结果表明,在非独立模式下,完全可以利用已知的不同分布函数的反演误差作为分类依据,从而更准确地确定被测颗粒系的粒径分布。采用的遗传反演算法能够在3个可见光波长下得到较准确的粒径分布,反演结果稳定可靠,最大限度地减少了多个波长的使用,从而对光源有更大的选择余地。对透射消光测量结果加入5%随机噪声时,单峰分布颗粒系的反演误差小于5%,多峰分布颗粒系的反演误差小于10%。整个算法运行时间小于2 s。该方法具有原理简单,计算速度快等优点,能够满足颗粒粒径在线测量的要求。     

Improving Document-Level Sentiment Classification Using Importance of Sentences

Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.     

应用低相干动态光散射法测量悬浮液中的颗粒粒径分布

利用低相干动态光散射法测量不同浓度悬浮液中的颗粒粒径分布。由低相干光源和迈克尔逊干涉仪,结合传统的动态光散射技术组成低相干动态光散射装置,通过检测单次背散射光的能谱分布,利用CONTIN算法得到不同浓度悬浮液中颗粒的粒径分布。结果表明,对于1%～10%体积浓度范围内的单分散悬浮液羊品测量得到的颗粒粒径精确度在4%以内。     

基于光阻法的小光束截面粒径信息提取方法

为了实现同时兼顾大、小颗粒测量的液体颗粒计数,在分析细小光束照射下球形颗粒脉冲信号波形特征的基础上,提出了一种颗粒粒径信息提取方法,该方法通过对脉冲信号积分将颗粒脉冲信号的幅度与持续时间结合,可更加准确地提取颗粒的等效粒径信息.对理想脉冲信号进行数值模拟,并采用乳胶微粒标准物质进行实验测试,结果表明,该方法能够有效地提取大颗粒和小颗粒的粒径信息,从而拓宽了仪器检测范围.同时,分析了流速和噪声对该方法提取粒径准确性的影响,结果表明当流速波动小于10%时,测量误差小于5.41%;当噪声小于1%时,测量误差小于0.50%.     

Classification Performance of a Low Pressure Differential Mobility Analyzer for Nanometer-sized Particles

Size distribution measurements and classification tests by a low pressure differential mobility analyzer (LPDMA) for nanometer-sized silver particles and cesium iodide particles under low pressure conditions (123–300 Torr) were performed using a transmission electron microscope (TEM) and the tandem DMA technique. When the ratio of the sheath gas flow rate and the aerosol gas flow rate was set at 5 : 1, the targeted sizes calculated from the classification voltage applied to the LPDMA at 160 Torr are found to be in good correlation with the count mean Feret diameter obtained from the TEM observation of the particles collected after a classification ranging from 6 to 25 nm, although the targeted sizes set by the LPDMA were approximately 15% greater than the count mean Feret diameter measured by the TEM after classification. The geometric standard deviations of DMA-classified particles measured by TEM and those obtained from the tandem DMA method ranged from 1.08 to 1.17 and from 1.05 to 1.13, respectively. They were slightly greater than the ideal geometric standard deviations (1.05) of the particles classified with the LPDMA, which was calculated by neglecting the broadening effects due to Brownian diffusion. We experimentally demonstrated the validity of our LPDMA system for size measurements and classification of the nanometer-sized particles under low pressure conditions.     

多波段激光雷达颗粒物质量浓度探测方法

为了获得大气颗粒物的质量浓度廓线,提出一种基于多波段激光雷达回波信号的大气气溶胶消光系数与颗粒物质量消光效率相结合的新型算法。该方法利用覆盖紫外到近红外波段的激光雷达作为遥感探测工具,获取气溶胶的消光与后向散射系数,反演得到气溶胶粒子谱分布;同时,根据米散射理论算出气溶胶消光效率,结合粒子谱分布,提出颗粒物质量消光效率模型,从而建立基于消光系数与质量消光效率相结合的反演颗粒物质量浓度的新型数学模型与算法。采用该算法对两组不同天气条件多波段激光雷达实测数据进行反演,并与地表采用的颗粒物浓度对比,证明该方法的可行性,为实现颗粒物质量浓度空间分布的探测提供科学依据和方法论。     

Analysis of Particle Size Distribution by Particle Tracking

Particle tracking is performed using a combination of dark field or fluorescence video microscopy with automatic image analysis. The optical detection together with the image analysis software allows for the time resolved localization of individual particles with diameters between 100 and 1000 nm. Observation of their Brownian motion over a set of time intervals leads to the determination of their mean square displacements under the given room temperature and viscosity. Hereby, the radii of a set of particles visible within a given optical frame are derived simultaneously. Rapid data analysis leads to reliable particle size histograms. The applicability of this method is demonstrated on polystyrene latices and PMMA nanospheres with radii between 51 nm and 202 nm.     

Scalably Using Node Attributes and Graph Structure for Node Classification

The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and other known node attributes. Common node classification approaches are based on the assumption that adjacent nodes have similar attributes and, therefore, that a node’s label can be predicted from the labels of its neighbors. While such an assumption is often valid (e.g., for political affiliation in social networks), it may not hold in some cases. In fact, nodes that share the same label may be adjacent but differ in their attributes, or may not be adjacent but have similar attributes. In this work, we present JANE (Jointly using Attributes and Node Embeddings), a novel and principled approach to node classification that flexibly adapts to a range of settings wherein unknown labels may be predicted from known labels of adjacent nodes in the network, other node attributes, or both. Our experiments on synthetic data highlight the limitations of benchmark algorithms and the versatility of JANE. Further, our experiments on seven real datasets of sizes ranging from 2.5K to 1.5M nodes and edge homophily ranging from 0.86 to 0.29 show that JANE scales well to large networks while also demonstrating an up to 20% improvement in accuracy compared to strong baseline algorithms.     

Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.     

纳秒脉冲等离子体合成射流激励器的流场特性分析

采用一台高重频、快上升沿纳秒脉冲电源作为激励源,对典型的双电极合成射流激励器进行放电,通过粒子图像测速法（particle image velocimetry,PIV）测量放电实验中激励器稳定流场特性以及发展速度.分析实验结果发现,随着重复频率的提高,合成射流的平均发展速度也随之增大,1 kHz时的平均速度最高达到28.28 m/s,并在单脉冲能量远低于微秒脉冲的情况下,实现了更快的稳态流场控制,表明高重复频率下,更多次数的脉冲放电可提高激励总能量,有效地弥补纳秒脉冲单脉冲输出能量不足的缺点.而且频率越高,流场发展速度越快,说明高重频工作模式会对输出总能量有补偿作用.