Digital Processing Techniques for Transmission Electron Microscope Images of Combustion-generated Soot Bing Hu and Jiangang Lu Department of Civil and Environmental Engineering University of Wisconsin – Madison

Motivation and Background Quantified characterization of flame-generated soot is critical for soot research. TEM-based study of soot properties is a reliable approach to quantifying soot size and morphology. Limited to the quality of TEM images, this approach may be facing challenges.

Objective By applying extensive digital image processing techniques to TEM images of soot particles, images with high qualities in senses of machine detection as well human visual inspection can be achieved. Developed an accurate as well as efficient computational analysis of soot size and morphology based on automatic computer detection. soot evolution in non-premixed turbulent flames burning ethylene. spherule

Typical TEM Images of Soot Low contrast, noise Pseudo edges caused by electron diffraction Here is the picture of our lab.

Approach Enhance contrast by gray level transformation. Reduce noise by low-pass filtering. Eliminate pseudo bright edges by blurring filtering. Segmentation of foreground from background by thresholding. Compensate for imperfect thresholding by morphological processing. Identify objects by morphology processing and segmentation. Computational analysis based on pixel value. Raynold No. =10,000, which is in turbulent zone. During the test, probes were cleaned frequently to prevent clogging of surfaces by soot particles. before and after each measurement, laser light received by detector is checked twice to ensure same experiment condition in each test. Data processing speed is 200 data points over 10 s. Those data were averaged to achieve repeatability within 10%. Typical experimental uncertainties (95% confidence) of optical measurements were estimated to be about less than 30%, mainly due to finite sampling times, collection arrangement of optics,and particle refractive index . a specified time(typically on the order of 100 ms); Philips EM430T transmission electron microscope operating at 100kV ; amount of total particle coverage on a TEM grid was generally less than 15% ; special markings on the grids made it possible to achieve a precise correspondence between the flame locations and microscope coordinates. various magnifications (7,400-42,500) ; 800-2,000 aggregates were analyzed; projected areas, Aa, and maximum lengths, L, of particles/aggregates within an image

Contrast Enhancement

Noise/fines detail Removal Figure 4:from a minimum of 0.13 ppm at the lowest location of x/d = 40 to a maximum of 1.6 ppm at x/d =80. two probe distances of 2.28 mm and 4.56 mm were used. there was up to 40% difference between the results for different probe distances. reliable estimations of fv below x/d =40 were not possible because of the very low extinction levels (I/I0 →1), resulting in high experimental uncertainties at such locations in the flame. fv were in good agreement with the ex-situ TS/TEM estimations until x/d = 80. particle number densities in the range of 7.610 10 –14 10 10 between x/d = 40-120 Fv=dp*=Summation: diameter of single soot*aggregate size in each aggregate* number of aggregate. This is to say, number of aggregate is almost same. Or say, soot is growing. New soot is only adding to the present aggregate without more new aggregate in the increasing part of the parabolic curve. With decreasing part of the parabolic curve, that is to say, number of aggregate is decreasing.

Thresholding Global Thresholding Adaptive Local Thresholding Actually, from our TEM test result in this figure, the soot spherule size is really almost same in the above tiny volume, even same in the same height above the burner. Moreover, soot spherule size increased a little from 20 nm to 37 nm with increasing height until x/d = 150. In contrast to the constant soot spherule size in the tiny volume and in the same height,; Aggregate sizes in an axial location of x/d = 80 can be seen to vary considerably in above tiny volume. Their distribution range from 10 to 1000, 3 amount of magnitude difference. However, the mean number of the aggregate size is in the range of 20-100, which can not be shown here. Those soot aggregate size look like anarchy, but not without control. Actually, those soot aggregate is in a fractal distribution with almost same fractal dimension D. Here, The linear least-square fit to the data shown in the figure yielded a slope of 1.61, which is the parameter describing those anarchy soot aggregate. This test result D=1.61, is similar with other researches test result. Moreover, this fractal dimension number is almost same in the whole flame area, which will be shown in the light scattering test result. Another parameter which is very important to describe those tiny anarchy soot particle aggregate is kg. With kL = 1.67, corresponded to a fractal prefactor of kg= 3.2.

Morphologic Processing

Object Extraction and Measurement Identify objects through extracting connected components. Measure maximum length. Measure projected area.

Summary and Conclusions An economical, accurate, and rapid image processing and analysis approach has been developed for analyzing soot morphology information from the Transmission Electron Microscope images. The techniques involved in this study include gray level transformation, convolution filtering, histogram analysis, thresholding, edge detection, image opening, extraction of connected components, and computational pixel analysis.