Depth scale calibration

Depth scale calibration of an SIMS depth profile requires the determination of the sputter rate used for the analysis from the crater depth measurement. An analytical technique for depth scale calibration of SIMS depth profiles via an online crater depth measurement was developed by De Chambost and co-workers.103 The authors proposed an in situ crater depth measurement system based on a heterodyne laser interferometer mounted onto the CAMECA IMS Wf instrument. It was demonstrated that crater depths can be measured from the nm to p,m range with accuracy better than 5 % in different matrices whereas the reproducibility was determined as 1 %.103 SIMS depth profiling of CdTe based solar cells (with the CdTe/CdS/TCO structure) is utilized for growing studies of several matrix elements and impurities (Br, F, Na, Si, Sn, In, O, Cl, S and ) on sapphire substrates.104 The Sn02 layer was found to play an important role in preventing the diffusion of indium from the indium containing TCO layer. 

Finally, it is difficult to calibrate the depth scale in a depth profile. This situation is made more complicated by different sputtering rates of materials. Despite these shortcomings, depth profiling by simultaneous ion sputtering/aes is commonly employed, because it is one of the few techniques that can provide information about buried interfaces, albeit in a destructive manner. 

In this idealized case, the profile can be obtained by varying the incident particle energy E0 stepwise within a certain interval. Yield calibration is done by a standard in an analogous way as described in equation (4). Close to the surface equation (5) can be simplified by the so-called surface approximation which consists in replacing S(E) by S(E0), i.e. by the value of the energy loss at the surface. The expression for the depth scale is then simply dR(E0) — (E0—ER)/S(E0). 

The Beckmann thermometer used with the bomb calorimeter should be calibrated for the normal depth of immersion with which it is used. To cover the normal range of laboratory temperatures, this calibration should be obtained for three settings of the zero on the scale, convenient values being 10, 15, and 20°C. Such a series of calibrations allows automatically for emergent stem corrections and variations in the value of the degree on the thermometer scale with different quantities of mercury in the bulb, in addition to those arising from inherent variations in the diameter of the capillary bore. 

Figure 10. Borehole temperature versus depth as measured in central Gieeidand, and as simulated by Culfey et al. (1995). The model was a coupled heat- and ice-flow computation driven by the record of the ice converted to temperature assuming a linear relation. The spatial dependence of on temperature is approximately 0.65 per mil per degree, which provides a poor fit to the data the optimal model differs from this by a factor of 2, and provides a much better fit. (A) The profile from the bed (height 0) to the surface (height 1, or -3050 m) (B) the upper portion the profiles on an expanded temperature scale. In Figure lOA, the optimal and measured curves are indistinguishable in lOB, the small differences between the optimal and measured curves indicate time-variation of the calibration of 5 0 to temperature.

The scale-error-complexity (SEC) surfaces. Instead of observing the prediction error with respect to resolution, it is also possible to monitor the complexity of the calibration/classification model. In PLS this can be measured by the number of PLS factors needed. How the error (e.g. RMSECV, RMSEP, PRESS) changes with varying the added scale and model complexity can be observed in scale-error-complexity (SEC) surfaces. In this case the first axis is the scales, the second axis is the model complexity (for PLS this is the number factors) and the third axis is the error. The complexity dimension is not limited to the number of PLS factors. For example classification and regression trees (CART) a measure based on tree depth and branching could be used [45],... 

Two conceptual models of groundwater flow beneath A u have been simulated using PARADIGM. In the first case, the matrix model, pressure diffusion is within a 3D homogeneous matrix and the diffusion coefficient, D, is calibrated such that the peak pressure reaches 2 km depth after 4.5 months. This time delay matches the observed delay in peak seismicity for cluster a, which has the most events. The matrix mesh for the local scale model is that shown in Figure 4, without the fault plane. 

The accuracy of the test method relies critically on the accurate determination of the area function, which varies with tip geometry. At the scale of many nanoindentation experiments, minute differences in tip geometry—such as those caused by wear induced by repeated experiments—can have a large effect on the area function. For this reason, the area function is typically measured by indenting a known material to several depths and computing the function from Equation 39.37. This experimentally determined tip function is then used to interpret further tests on unknown materials. Fused silica (or quartz) is commonly used to calibrate the tip, that is, determine the area tip function, because its material response is nearly perfectly elastic and it does not exhibit adhesion with diamond indenter tips. 

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