DRIFTS artifacts

The heightened appreciation of resonance problems, in particular, has been quite recent [63, 62], and contrasts the more systematic error associated with numerical stability that grows systematically with the discretization size. Ironically, resonance artifacts are worse in the modern impulse multiple-timestep methods, formulated to be symplectic and reversible the earlier extrapolative variants were abandoned due to energy drifts. 

In some cases a principal components analysis of a spectroscopic- chromatographic data-set detects only one significant PC. This indicates that only one chemical species is present and that the chromatographic peak is pure. However, by the presence of noise and artifacts, such as a drifting baseline or a nonlinear response, conclusions on peak purity may be wrong. Because the peak purity assessment is the first step in the detection and identification of an impurity by factor analysis, we give some attention to this subject in this chapter. 

By revealing all aspects of the signal, the phase detector makes evident all instrumental artifacts which would not be observable with another type of detection. On an FFC instrument, this typically includes thermal field drifts (see Section IV.D) and field instabilities associated with the large dynamic bandwidth of the switching magnet system. 

Detector sensitivity is one of the most important properties of the detector. The problem is to distinguish between the actual component and artifact caused by the pressure fluctuation, bubble, compositional fluctuation, etc. If the peaks are fairly large, one has no problem in distinguishing them however, the smaller the peaks, the more important that the baseline be smooth, free of noise and drift. Baseline noise is the short time variation of the baseline from a straight line. Noise is normally measured "peak-to-peak" i.e., the distance from the top of one such small peak to the bottom of the next. Noise is the factor which limits detector sensitivity. In trace analysis, the operator must be able to distinguish between noise spikes and component peaks. For qualitative purposes, signal/noise ratio is limited by 3. For quantitative purposes, signal/noise ratio should be at least 10. This ensures correct quantification of the trace amounts with less than 2% variance. The baseline should deviate as little as possible from a horizontal line. It is usually measured for a specified time, e.g., 1/2 hour or one hour and called drift. Drift usually associated to the detector heat-up in the first hour after power-on. 

The choice of the training set is important in any pattern-recognition study. Each class must be well represented in the training set. Experimental variables must be controlled or otherwise accounted for by the selection of suitable samples that take into account all sources of variability in the data, for example, lot-to-lot variability. Experimental artifacts such as instrumental drift or sloping baseline must be minimized. Features containing information about differences in the source profile of each class must be present in the data. Otherwise, the classifier is likely to discover rules that do not work well on test samples, i.e., samples that are not part of the original data. 

A number of innovations in FFF design have been introduced recently which have improved resolution and sensitivity and reduced the major obstacles to automation. The introduction of cross-flow recirculation has resulted in better control of system pressures, thereby reducing baseline fluctuation and improving reproducibility [6]. The incorporation of a frit inlet (FI) into the flow FFF channel has permitted the use of hydrodynamic relaxation as a replacement for stop-flow relaxation, thus eliminating pressure fluctuations associated with the latter [7]. FI also reduces sample adhesion to the membrane on the accumulation wall, thereby reducing the likelihood of baseline drift and artifacts [7]. 

After each irradiation a DRIFT spectrum was measured. As expected, all bands are decreasing in intensity, indicating a continuous decomposition of the polymer. This shows that it is possible to follow the decomposition of polyimide using difference spectra up to several thousands of pulses, with no artifacts, e.g., carbonization, interfering with the measurements. 

Qualitative and Quantitative Analysis of the DRIFT Spectra. DRIFT spectra are usually presented in Kubelka-Munk units. DRIFT spectra with small baseline errors can be obtained when measurements are made at ambient temperature. However, if measurements are performed at higher temperatures, IR radiation emitted from the heated sample can affect the collected spectra, especially if MCT detectors are employed. This is even more pronounced when the refractivity of the sample changes with time. The baseline artifacts are added to the collected spectra. 

The data are noisy or have artifacts. Certain types of drifts can mimic EXAFS. 

Beam instability. The use of EXAFS on heterogeneous samples, especially with a microfocused beam, tends to exacerbate certain problems that are well known from other systems. One simple example of such a problem is the sensitivity to beam motion, which results from putting a small beam on an equally small particle. The beam tends to move on the sample during data acquisition, for a variety of reasons. These motions can cause artifacts in the data. For example, during the course of a synchrotron fill, the source point may move, and the decreasing power incident on the optics may also cause position shifts. Vibrations either of the sample or the optics result in increased noise and an increased effective spot size. If the position drifts on a time scale comparable to the length of time required to scan over an EXAFS oscillation, one could get artifacts, which are indistinguishable from EXAFS, except that they do not repeat from scan to scan. If... 

There are various strategies for avoiding these effects, some of which are common to bulk and non-environmental EXAFS. For instance, if the beamline is capable of quick-EXAFS, then each scan will be taken so quickly that slow drifts will not cause artifacts (Gaillard et al. 2001). Some beamlines (MacDowell et al. 2001) create an image of the source on a set of slits, which in turn becomes a fixed virtual source. Attention to mechanics and temperature stability can pay off in terms of beam-position stability. If one is looking at a particle whose fluorescent yield or transmission is very different from that of its surroundings, then it pays to put the beam accurately on an extremum of yield or transmission. That way, small motions only cause second-order perturbations in the signal. This procedure also minimizes the effect of vibrations. 

Figure 5. Exposure artifacts illustrated by the constructive and destructive interference nodes in the resist layer over an underlying step (top figure). The resulting scalloped line edge profiles over such a step crossing showing global linewidth drifts as resist layer thickness changes (bottom figure). (Reproduced with permission from Ref. 29. Copyright 1981, SPIE.)...

Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has been proven to be an excellent means of characterizing coals and related materials. This report is devoted to the evaluation of the technique as a method for situ monitoring of the chemical structural changes wrought in reactions of coal with fluid phases. This technique does not require a supporting medium (matrix) which can contain chemical artifacts which inherently serve as a barrier for access to the solid coal. The rapid response of the Fourier transform infrared technique is further beneficial for kinetic studies related to combustion, liquefaction, gasification, pyrolyses, etc. Experimental equipment and techniques are described for studies over wide ranges of pressure (10 5 Pa to ca 1.5 x 10 kPa) and temperature (298 K to 800 K). 

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