Application of a two-stage scheme of gas chromatographic analysis of unstable reforming catalysate

1970 ◽  
Vol 6 (9) ◽  
pp. 704-706
Author(s):  
A. P. Dorokhov ◽  
G. V. Emel'yanova ◽  
I. I. Ioffe ◽  
N. P. Mel'nikova ◽  
V. E. Shefter
Keyword(s):  
Chromatographic Analysis ◽  
Two Stage ◽  
Gas Chromatographic Analysis ◽  
Stage Scheme

The mechanism of formation of 2,7-dimethyl-2,6-octadiene in the synthesis of 3-methylene-7-methyl-1,6-octadiene

1983 ◽  
Vol 48 (7) ◽  
pp. 1864-1866
Author(s):  
Jan Bartoň ◽  
Ivan Kmínek
Keyword(s):  
Mass Spectrometry ◽  
Nmr Spectroscopy ◽  
Alkali Metal ◽  
Chromatographic Analysis ◽  
Solid Phase ◽  
Reaction Pathway ◽  
Mechanism Of Formation ◽  
Gas Chromatographic Analysis ◽  
C Nmr ◽  
Visual Observations

2,7-Dimethyl-2,6-octadiene is formed in the catalytic solution for the dimerization of 2-methyl-1,3-butadiene to β-myrcene (3-methylene-7-methyl-1,6-octadiene), as revealed by mass spectrometry and 13C NMR spectroscopy. Visual observations together with the results of gas chromatographic analysis of the catalytic solution suggest that the formation of 2,7-dimethyl-2,6-octadiene is associated with the transition of the alkali metal (sodium) from the solid phase into the solution. A reaction pathway is suggested accounting for the formation of 2,7-dimethyl-2,6-octadiene in the system.

scholarly journals Mesh Denoising via Adaptive Consistent Neighborhood

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 412
Author(s):  
Mingqiang Guo ◽  
Zhenzhen Song ◽  
Chengde Han ◽  
Saishang Zhong ◽  
Ruina Lv ◽  
...  
Keyword(s):  
Graph Cut ◽  
Underlying Surface ◽  
Geometric Features ◽  
Two Stage ◽  
Mesh Denoising ◽  
Normal Field ◽  
Stage Scheme ◽  
Complex Shapes ◽  
Patch Shift

In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.

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