Single-Particle Analysis in An Indoor Working Environment in Valencia, Spain

Actually is recognized the importance of indoor air environment and associated health risks. In order to evaluate indoor air quality and to characterize the particles in terms of size, composition and shape were done measurements of the suspended particulate matter in a mechanical workshop of the Polytechnic University of Valencia (Spain). These measurements were performed using scanning electron microscope (SEM) with energy dispersive X-ray microanalysis (EDX) and image digital analysis. To differentiation of individual particles in the fine- ultrafine fraction, in some case, was used the atomic force microscopy (AFM). Multivariate statistics, such as hierarchical cluster analysis and factor analysis were appliqued and allowed to establish groups of elements and in this way to facilitate the identification of the natural and anthropogenic sources. It is confirmed that indoor air is influenced by outdoor surroundings and the anthropogenic sources due to the daily activity.

An image segmentation method of pulverized coal for particle size analysis

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.

A NEW ALGORITHM OF RELAXATION METHOD FOR PARTICLE ANALYSIS FROM FORWARD SCATTERED LIGHT

A new algorithm of the relaxation method is developed for the inversion of forward scattered light to obtain the size distribution of spherical particles. Numerical tests are performed for a laser particle analyzer using the Mie theory and the diffraction approximation. The algorithm efficiency, in the presence of experimental noises, is studied. The results show that the technique is fast in convergence, stable against random noise and insensitive to the distribution of particles and the initial trial distribution.

An investigation on dissolution kinetics of single sodium carbonate particle with image analysis method

Dissolution kinetics of sodium carbonate is investigated with the image analysis method at the approach of single particle.The dissolution experiments are carried out in an aqueous solution under a series of controlled temperature and p H.The selected sodium carbonate particles are all spherical with the same mass and diameter.The dissolution process is quantified with the measurement of particle diameter from dissolution images.The concentration of dissolved sodium carbonate in solvent is calculated with the measured diameter of particle.Both surface reaction model and mass transport model are implemented to determine the dissolution mechanism and quantify the dissolution rate constant at each experimental condition.According to the fitting results with both two models,it is clarified that the dissolution process at the increasing temperature is controlled by the mass transport of dissolved sodium carbonate travelling from particle surface into solvent.The dissolution process at the increasing pH is controlled by the chemical reaction on particle surface.Furthermore,the dissolution rate constant for each single spherical sodium carbonate particle is quantified and the results show that the dissolution rate constant of single spherical sodium carbonate increases significantly with the rising of temperature,but decreases with the increasing of pH conversely.

Analysis of Low-Frequency Vibrational Modes and Particle Rearrangements in Marginally Jammed Amorphous Solid under Quasi-Static Shear

We present the numerical simulation results of a model granular assembly formed by spherical particles with tIertzian interaction subjected to a simple shear in the athermal quasi-static limit. The stress-strain curve is shown to separate into smooth, elastic branches followed by a subsequent plastic event. Mode analysis shows that the lowest-frequency vibrational mode is more localized, and eigenvalues and participation ratios of low- frequency modes exhibit similar power-law behavior as the system approaches plastic instability, indicating that the nature of plastic events in the granular system is also a saddle node bifurcation. The analysis of projection and spatial structure shows that over 75% contributions to the non-affine displacement field at a plastic instability come from the lowest-frequency mode, and the lowest-frequency mode is strongly spatially correlated with local plastic rearrangements, inferring that the lowest-frequency mode could be used as a predictor for future plastic rearrangements in the disordered system jammed marginally.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

CO and Particle Pollution of Indoor Air in Beijing and Its Elemental Analysis

Three representative types of houses in Beijing were selected and, in each type, smoking and nonsmoking households were compared. IP, RP. and CO concentrations in the living room and kitchen were monitored during each season. and the level of COHb in the heads of the households were measured. The study showed that indoor air pollution was rather severe, especially during winter. when paniculate concentrations markedly exceeded the standard and CO concentration was as high as 47 ppm. Indoor air pollution was closely related to the type of house, particularly to the mode of heating. In houses. of the same type, pollution improved greatly after central heating facilities were installed. Analysis of 30 elements revealed that pollution was typically caused by coal burning. aggravated by dusty wind, but high indoor Pb levels were probably due to the use of LPG for cooking. In our study the effect of cigarette smoking was sometimes masked by the severe indoor pollution. (C)1990 Academic Press, Inc.

Analysis of Correlation between Concentration of Atmospheric Particulates and Humidity Based on the Aerodynamic Particle Sizer

The TSI-3321 APS was used to measure concentration of atmospheric particulates in Ranjiaba region of Yubei District in Chongqing City during March 21- 29,2014,and the temporal variations in the hourly average mass and number concentration and median particle diameter of PM10 and PM2.5 as well as their correlation with relative humidity were analyzed. The results showed that the three indicators of PM10 and PM2.5 except for the mass concentration correlated with relative humidity,of which the correlation between the mass median particle diameter and relative humidity was the best. The correlation coefficient R^2 between the mass median particle diameter of PM10（ PM2.5） and relative humidity was up to 0. 943（ 0. 832）. Therefore,relative humidity and pressure are key impact factors of indicators of particles.

Design of the stripping unit and the electromagnetic analysis unit for the E//B NPA on HL-2A/2M tokamak

An E//B neutral particle analyzer is under development for fast ion diagnosis on HL-2A/2 M tokamak.The stripping unit is composed of a stripping room(equipped with two differential tubes and a gas supply bellows),a vacuum chamber and a vacuum pumping system.The stripping efficiency of the stripping room is calculated in the form of global efficiency R×f_(+1),where R is the non-scattered-away rate and f_(+1)is the fraction of charge state+1.The magnetic field of the E//B analyzer is produced with a permanent magnet.The yoke and the poles of the magnet are made of mild steel and the magnet plates are made of Nd Fe B.The magnetic poles are specially designed to focus the ion trajectories and to increase the use rate of the magnet.The shape of the magnet and the electric plates are carefully designed so that the ions are dispersed into two lines of H^(+) and D^(+) on the detector plane.For each line,the energy increases from 10 to 200 ke V from one side to another.

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

An analytical model for pyrolysis of a single biomass particle

Decreasing in emissions of greenhouse gases to confront the global warming needs to replace fossil fuels as the main doer of the world climate changes by renewable and clean fuels produced from biomass like wood waste which is neutral on the amount of CO2. An analytical and engineering model for pyrolysis process of a single biomass particle has been presented. Using a two-stage semi global kinetic model which includes both primary and secondary reactions, the effects of parameters like shape and size of particle as well as porosity on the particle temperature profile and product yields have been investigated. Comparison of the obtained results with experimental data shows that our results are in a reasonable agreement with previous researchers' works. Finally, a sensitivity analysis is done to determine the importance of each parameter on pyrolysis of a single biomass particle which is affected by many constant parameters.

<i>In-Silico</i>Validation and Development of Chlorogenic Acid (CGA) Loaded Polymeric Nanoparticle for Targeting Neurodegenerative Disorders

Background: Recent decades witnessed a significant growth in terms of phytocompounds based therapeutics, extensively explored for almost all types of existing disorders. They have also been widely investigated in Neurodegenerative disorders (NDDs) and Chlorogenic acid (CGA), a polyphenolic compound having potential anti-inflammatory and anti-oxidative properties, emerged as a promising compound in ameliorating NDDs. Owing to its poor stability, bioavailability and release kinetics, CGA needed a suitable nanocarrier based pharmaceutical design for targeting NDDs. Objective: The current study is aimed at the in-silico validation of CGA as an effective therapeutic agent targeting various NDDs followed by the fabrication of polymeric nanoparticles-based carrier system to overcome its pharmacological limitations and improve its stability. Methods: A successful in-silico validation using molecular docking techniques along with synthesis of CGA loaded polymeric nanoparticles (CGA-NPs) by ionic gelation method was performed. The statistical optimisation of the developed CGA-NPs was done by Box Behnken method and then the optimized formulation of CGA-NPs was characterised using particle size analysis (PSA), Transmission electron microscopy (TEM), Fourier Transform Infrared spectroscopy (FTIR) along with in-vitro release kinetics analysis. Results & Conclusion: The results attained exhibited average particle size of 101.9 ± 1.5 nm, Polydispersibility (PDI) score of 0.065 and a ZP of −17.4 mV. On a similar note, TEM results showed a size range of CGA-NPs between 90 - 110 nm with a spherical shape of NPs. Also, the data from in-vitro release kinetics showed a sustained release of CGA from the NPs following the first-order kinetics suggesting the appropriate designing of nanoformulation.

Source apportionment of PM_(2.5)using PMF combined online bulk and single-particle measurements:Contribution of fireworks and biomass burning

Fireworks(FW)could significantly worsen air quality in short term during celebrations.Due to similar tracers with biomass burning(BB),the fast and precise qualification of FW and BB is still challenging.In this study,online bulk and single-particle measurements were combined to investigate the contributions of FW and BB to the overall mass concentrations of PM_(2.5)and specific chemical species by positive matrix factorization(PMF)during the Chinese New Year in Hong Kong in February 2013.With combined information,fresh/aged FW(abundant ^(140)K_(2)NO_(3)^(+)and ^(213)K_(3)SO_(4)^(+)formed from ^(113)K_(2)Cl^(+)discharged by fresh FW)can be extracted from the fresh/aged BB sources,in addition to the Second Aerosol,Vehicles+Road Dust,and Sea Salt factors.The contributions of FW and BB were investigated during three high particle matter episodes influenced by the pollution transported from the Pearl River Delta region.The fresh BB/FW contributed 39.2%and 19.6%to PM_(2.5)during the Lunar Chinese New Year case.However,the contributions of aged FW/BB enhanced in the last two episodes due to the aging process,evidenced by high contributions from secondary aerosols.Generally,the fresh BB/FW showed more significant contributions to nitrate(35.1%and15.0%,respectively)compared with sulfate(25.1%and 5.9%,respectively)and OC(14.8%and11.1%,respectively)on average.In comparison,the aged FW contributed more to sulfate(13.4%).Overall,combining online bulk and single-particle measurement data can combine both instruments’advantages and provide a new perspective for applying source apportionment of aerosols using PMF.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Ferrograph-based wear debris analysis(WDA)provides significant information for wear fault analysis of mechanical equipment.After decades of offline application,this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring.However,online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications.To address this issue,an integrated optimization method is developed that focuses on two aspects:the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains.For enhancing the imaging quality of wear particles,the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils.Furthermore,a light source simulation model is established based on the light intensity distribution theory,and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor.On this basis,a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest(ROI)generation layer and the ROI align layer for the irregular particle morphology.With these measures,a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles.For verification,the developed sensor is tested to collect particle images from different degraded oils,and the images are further handled with the Mask-RCNN-based model for particle feature extraction.Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils,and the illumination uniformity reaches 90%in its imaging field.Most importantly,the statistical accuracy of wear particles has been improved from 67.2%to 94.1%.

Probing the effects of lithium doping on structures, properties, and stabilities of magnesium cluster anions

Bimetallic clusters have aroused tremendous interest because the property changes like structure,size,and composition have occurred.Herein,a structural search of the global minimum for anionic LiMg_(n)^(-)(n=2-11) clusters is performed using an efficient crystal structure analysis by particle swarm optimization(CALYPSO) structural searching program with subsequent density functional theory(DFT) calculations.A great variety of low energetic isomers are converged,and the most stable ones are confirmed by comparing their total energy of each size.It is found that the LiMg_(n)^(-)clusters are structurally consistent with corresponding Mg clusters anions except for LiMg_(5)^(-)and LiMg_(7)^(-).In all the doped clusters,the Li atom prefers to occupy the convex position.Simulated photoelectron spectra(PES),Infrared(IR),and Raman spectra of LiMg_(n)^(-)could be used as an essential evidence for identifying cluster structures experimentally in the future.Stability study reveals that a tower-like structure of LiMg_(9)^(-)has prominent stability and can be identified as a magic number cluster.The reason might be that there are both closed-shell 1S^(2)1P^(6)1D^(10)2S^(2) electronic configurations and stronger Li-Mg bonds caused by sp hybridization in the LiMg_(9)^(-)cluster.

Element and mineral characterization of dust emission from the saline land at Songnen Plain, Northeastern China

Recent observations of Asian dust storms show an eastern expansion of the source area to degraded lands, where dust emissions have been little studied. The dust concentrations over the saline land of the western Songnen Plain （SSL）, Northeastern China, are circumstantially higher than those from the northwestern Chinese deserts. These concentrations are sensitive to the surface soil conditions and wind velocity on the ground. The dust samples collected during dust storm events on the SSL contain abundant Na, Mg, A1, K, Ca, Fe and Ti, as well as toxic elements such as Cu, V, Zn and Ba. Individual particle analysis reveals that fine saline particles （〈 10 μm in diameter） on the saline land, consisting largely of carbonate, halite and sulfate together with lithogenic minerals such as SiO2 and aluminosilicate, are eventually uplifted during the interval from spring to autuum. The predominantly fine saline particles uplifted from the SSL are likely transported eastward by the winter monsoon circulation and westerlies. Recent degradation of saline lands in Northeastern China would not only increase the frequency of dust storm events in the downwind area, but also might change the chemical composition of the Asian dust emissions.

FUNCTIONS OF POLYAMINES IN ROSIN SIZING UNDER NEUTRAL PAPERMAKING CONDITIONS

Functions of the polyamines in neutral rosin sizing were investigated using X-ray photoelectron spectroscopy (XPS) and laser diffraction particle analysis. The polyamine with a higher charge density and a smaller unite size could retain more rosin and cover larger fiber surfaces. The XPS spectra demonstrated that polyallylamine (PAAm) and polyvinylamine (PVAm) could react with a rosin size to form a -OC-N-CO- structure, but polydimethylamino ethyl methacrylate (PDMAEMA) could not. The formation of this structure may be a key step for effective sizing.

A novel method for simultaneous analysis of particle size and mineralogy for Chang’E-5 lunar soil with minimum sample consumption

The successful return of lunar soil samples from the northern Oceanus Procellarum by the Chang’E 5(CE-5) mission has provided unprecedented ground-truth information for the previously unexplored region of the Moon. In particular, the particle size and mineral constituents of the CE-5 soil samples are of critical importance to interpret remote sensing data. With a Raman-based particle analysis system, we show that the particle size properties and mineral constituents of the CE-5 soil can be simultaneously determined with a small sample size(ca. 30 μg). The CE-5 sample scooped from the lunar surface has an overall small size between 0.4 μm and 73.9 μm(mean=3.5 μm), and mainly consists of pyroxene(39.4%), plagioclase(37.5%), olivine(9.8%), Fe-Ti oxides(1.9%), glass(8.3%) and other minor or trace phases. The results are consistent with previous analyses with larger sample sizes. In addition to minimum sample consumption, this method requires very little sample preparation, and can rapidly build a large database with each particle precisely traceable. Therefore, this novel technique is particularly suitable for the analysis of future returned soil samples from extraterrestrial bodies.

Dimensional Description of On-line Wear Debris Images for Wear Characterization

As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle’s chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area （IPCA）, show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity.

A Development of A Computer aided Ferrographic System

In this paper the authors suggest a computer aided system for processing data and images of ferrographic analysis by the authors. The system consists of seven modules and five databases. There is a typical wear particle library in the system. Its applications state that the analytical speed increases with this system and more information can be obtained by using this system.