Nitrate Δ17O tracer method for determining gross nitrification rates

Copper of different purity levels (4N, 5N) produced by High Pressure Torsion (HPT) with varying processing parameters is investigated utilizing the radiotracer technique. While the degree of deformation is constant, the effect of the applied quasi-hydrostatic pressure and of the impurity concentration on the as deformed samples is analysed. By applying the radio tracer method micro structural aspects are revealed that are not easily accessible by conventional methods. The measurements indicate the formation of a percolating porosity during the HPT process as a function of the applied pressure and (although less pronounced) of the impurity concentration.

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Silo flow-pattern diagnosis using the tracer method

Journal of Food Engineering ◽

10.1016/j.jfoodeng.2008.08.010 ◽

2009 ◽

Vol 91 (1) ◽

pp. 118-125 ◽

Cited By ~ 7

Author(s):

Nathalie Job ◽

Albert Dardenne ◽

Jean-Paul Pirard

Keyword(s):

Flow Pattern ◽

Tracer Method ◽

Pattern Diagnosis

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Application of digital image processing in slope surface runoff velocity analysis under simulated rainfall conditions

Hydrology Research ◽

10.2166/nh.2017.040 ◽

2017 ◽

Vol 49 (4) ◽

pp. 1304-1312

Author(s):

Tiexiong Gong ◽

Yuanjun Zhu

Keyword(s):

Image Processing ◽

Surface Runoff ◽

Lateral Diffusion ◽

Ease Of Use ◽

Velocity Analysis ◽

Slope Gradient ◽

Tracer Method ◽

Dye Tracer ◽

The Mean ◽

High Runoff

Abstract To have accurate runoff velocity, there is need to improve dye tracer method for estimating surface runoff velocity. This can enhance the calculations of relevant hydrologic parameters that will lead to a better understanding of hydrological processes and soil erosion. In this study, an integrated dye tracer and image processing method (IPV) and dye tracer method (AOV), respectively, were used to estimate runoff velocity under three slope gradients (5°, 10°, and 15°) and three slope positions (up-slope, mid-slope, and down-slope). The results showed more variation in runoff velocity under IPV than AOV. Both IPV and AOV were positively correlated with slope gradient. IPV values were close to AOV ones for slope gradients ≤5°, but were significantly different for slope gradients ≥10°. The mean AOV value was 10.6% higher than that of IPV. Regression analysis showed that compared with AOV, IPV overestimated and underestimated runoff under low and high runoff velocity conditions, respectively. The use of image processing in IPV was advantageous because of its ease of use with fewer artificial errors and its suitability for lateral diffusion of runoff. Irrespectively, additional studies are needed to verify and/or improve further the use of this method in runoff velocity analysis.

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