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Determination of very broad particle size distributions via interference optics in the analytical ultracentrifuge

机译:通过干扰光学在分析超传递纤维中的光纤测定非常宽的粒度分布

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Dispersions play a big role in industry. Important characteristics such as viscosity and gloss depend on the particle size and the particle size distribution of these systems. Industrial dispersions often have a broad particle size distribution. If the particle size is in the submicron range, particle size distributions of this type can best be analysed by analytical ultracentrifugation as shown in a big round-robin test which was carried out by Bayer. When particles become smaller and their transmission increases, turbidity optics have to be substituted by interference optics. This leads to a more tedious way of transforming measured data into particle size distributions. This has to do with the enormous broad range of s values which often belongs to such a particle size distribution, for example from 1 to 300 Sv. As a consequence not only several interference pictures have to be taken at different times but also pictures at different rotor speeds. For this example the rotor speeds are 3,000, 10,000, 25,000 and 40,000 rpm. The new computer program for data evaluation converts the i = f(r) picture into a i = f (d) function using the well-known Stokes law. Then it collects the number of fringes for a given diameter interval. This concentration is computed for each of the 500–1,500 interference pictures and an average value is taken for the final result. By the application of our new program we can do our interference work in a semiautomated way and avoid the arbitrary man-made selection of pictures, which saves time and increases precision.
机译:Dispersions在工业中发挥着重要作用。重要特性,如粘度和光泽依赖于这些系统的粒度和粒度分布。工业分散体通常具有宽的粒度分布。如果粒径在亚微米范围内,则可以最好地通过分析超速离心分析这种类型的粒度分布,如拜耳执行的大循环试验中所示。当粒子变小并且其传输增加时,浊度光学器件必须被干扰光学器件代替。这导致更繁琐的方式将测量数据转化为粒度分布。这与通常属于这种粒度分布的巨大宽范围的S值,例如1至300 sv。结果不仅在不同的时间采取了几种干涉图片,而且还有不同转子速度的图像。对于该示例,转子速度为3,000,10,000,25,000和40,000rpm。用于数据评估的新计算机程序将i = f(r)图片转换为使用众所周知的Stokes Lave的I = F(d)函数。然后它收集给定直径间隔的条纹的数量。对于500-1,500个干扰图像中的每一个计算该浓度,并且为最终结果拍摄平均值。通过应用我们的新计划,我们可以以半仿古方式进行干扰工作,避免采用任意人类的图片选择,可节省时间并提高精度。

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