Hevea Latex of Large Particle Size

1940 ◽  
Vol 13 (2) ◽  
pp. 415-421
Author(s):  
J. McGavack

Abstract The purpose of this paper is to report, primarily, the particle size distribution of modified Hevea latices. In addition, the purpose is to show that the amount of nitrogen absorbed by the rubber particles in a latex thoroughly washed by aqueous ammonia depends on the surface exposed. Until the careful and complete work of Lucas (Ind. Eng. Chem. 30, 146 (1938)), there were no reliable particle size distribution data on a normal latex by which the distribution curves of modified latices could be compared. Now, as a result of his excellent technique for measuring all of the particles, even the very small ones, we are able to discuss clearly surface and volume changes when these smaller sized particles are removed. This work was so conducted as to eliminate particle sizes which could not be photographed with microscopical equipment in visible light. It was eventually carried out in such a way as to remove all water-soluble materials not adsorbed on or dissolved in the latex particles which were to be subsequently analyzed for nitrogen.

Author(s):  
H. Lin ◽  
X. Zhang ◽  
Y. Yang ◽  
X. Wu ◽  
D. Guo

From geologic perspective, understanding the types, abundance, and size distributions of minerals allows us to address what geologic processes have been active on the lunar and planetary surface. The imaging spectrometer which was carried by the Yutu Rover of Chinese Chang’E-3 mission collected the reflectance at four different sites at the height of ~ 1 m, providing a new insight to understand the lunar surface. The mineral composition and Particle Size Distribution (PSD) of these four sites were derived in this study using a Radiative Transfer Model (RTM) and Sparse Unmixing (SU) algorithm. The endmembers used were clinopyroxene, orthopyroxene, olivine, plagioclase and agglutinate collected from the lunar sample spectral dataset in RELAB. The results show that the agglutinate, clinopyroxene and olivine are the dominant minerals around the landing site. In location Node E, the abundance of agglutinate can reach up to 70 %, and the abundances of clinopyroxene and olivine are around 10 %. The mean particle sizes and the deviations of these endmembers were retrieved. PSDs of all these endmembers are close to normal distribution, and differences exist in the mean particle sizes, indicating the difference of space weathering rate of these endmembers.


2010 ◽  
Vol 56 (No. 4) ◽  
pp. 154-158 ◽  
Author(s):  
T. Vítěz ◽  
P. Trávníček

Particle size distribution of the sample of waste sawdust and wood shavings mixtures were made with two commonly used methods of mathematical models by Rosin-Rammler (RR model) and by Gates-Gaudin-Schuhmann (GGS model).On the basis of network analysis distribution function F (d) (mass fraction) and density function f (d) (number of particles captured between two screens) were obtained. Experimental data were evaluated using the RR model and GGS model, both models were compared. Better results were achieved with GGS model, which leads to a more accurate separation of the different particle sizes in order to obtain a better industrial profit of the material.


Author(s):  
I. L. Whyte

AbstractThe origins and development of the U100 (U4) thick-walled open-drive sampler are reviewed. The requirements of CP 2001 and BS 5930 are examined in relation to sample quality, and these are shown to be too favourable. Causes of sample disturbance are considered, particularly those due to volume changes, and shown to depend on moisture content, plasticity and particle size distribution. Quality classes possible with conventional U100 samples are suggested, and Classes 3 or 4 are to be generally expected. Class 1 samples are improbable. It is recommended that a general purpose sampler such as the U100 should have a maximum inside clearance of 1% and not ‘about 1%’ as recommended in BS 5930.


2015 ◽  
Vol 133 ◽  
pp. 30-43 ◽  
Author(s):  
David R. Ochsenbein ◽  
Stefan Schorsch ◽  
Fabio Salvatori ◽  
Thomas Vetter ◽  
Manfred Morari ◽  
...  

2017 ◽  
Author(s):  
Yuanyuan Xie ◽  
Xingnan Ye ◽  
Zhen Ma ◽  
Ye Tao ◽  
Ruyu Wang ◽  
...  

Abstract. We characterize a representative haze event from a series of periodic particulate matter (PM) episodes that occurred in Shanghai during winter 2014. Particle size distribution, hygroscopicity, and effective density were measured online, along with analysis of water-soluble inorganic ions and single particle mass spectrometry. Regardless of pollution level, the mass ratio of SNA/PM1.0 (sulfate, nitrate, and ammonium) slightly fluctuated around 0.28 over the whole observation, suggesting that both secondary inorganic compounds and carbonaceous aerosols (including soot and organic matter) contributed substantially to the haze formation. Nitrate was the most abundant ionic species during hazy periods, indicating that NOx contributed more to haze formation in Shanghai than did SO2. The calculated PM concentration from particle size distribution displayed a variation pattern similar to that of measured PM1.0 during the representative PM episode, indicating that enhanced pollution level was attributable to the elevated number of larger particles. The number fraction of the near-hydrophobic group increased as the PM episode developed, indicating accumulation of local emissions. Three "banana-shape" particle evolutions were consistent with the rapid increase in PM1.0 mass loading, indicating rapid size growth by condensation of condensable materials was responsible for the severe haze formation. Both hygroscopicity and effective density of the particles increased considerably with growing particle size during the banana-shaped evolutions, indicating that secondary transformation of NOx and SO2 was a major contributor to the particle growth. Our results suggest that the accumulation of gas-phase and particulate pollutants under stagnant meteorological conditions and subsequent rapid particle growth by secondary processes, were primarily responsible for the haze pollution in Shanghai during wintertime.


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