The Dynamic Range Problem

The analog-to-digital converter receives the FID signals in the form of electrical voltages and converts them into binary numbers proportional to 

If the spectral width is inadequate to cover every peak in the spectrum, then some peaks in the downfield or upheld region may fold over and appear superimposed on the spectrum. How can you identify these folded signals  

The dynamic range of the digitizer is very important. The NMR signal 

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We normally avoid protonated solvents, because the very intense solvent peak will obscure nearby protons, and the dynamic range problem will also... 

Isokinetic flow can be achieved if the sampling system monitors the ambient wind velocity and adjusts the pumping speed as appropriate. Unfortunately, this system exacerbates the dynamic-range problem because the system samples faster in high winds, when the aerosol concentration tends to be higher. 

McCoy et a/. have proposed a method termed ERASER based on self-refocused and hard rectangular pulses. The scheme does not generate phase roll. Moy et alP have demonstrated the use of frequency shifted, self-refocused top hat pulses to observe amide resonances, that they termed selective-excitation-corrected spectroscopy (SelECSy). This method, although not providing exceptional water suppression, overcomes the dynamic range problem and produces uniform amide proton excitation... 

The behavior of materials under dynamic load is of considerable importance and interest in most mechanical analyses of design problems where these loads exist. The complex workings of the dynamic behavior problem can best be appreciated by summarizing the range of interactions of dynamic loads that exist for all the different types of materials. Dynamic loads involve the interactions of creep and relaxation loads, vibratory and transient fatigue loads, low-velocity impacts measurable sometimes in milliseconds, high-velocity impacts measurable in microseconds, and hypervelocity impacts as summarized in Fig. 2-4. 

Non-linear concentration/response relationships are as common in pesticide residue analysis as in analytical chemistry in general. Although linear approximations have traditionally been helpful the complexity of physical phenomena is a prime reason that the limits of usefulness of such an approximation are frequently exceeded. In fact, it should be regarded the rule rather than the exception that calibration problems cannot be handled satisfactorily by linear relationships particularly as the dynamic range of analytical methods is fully exploited. This is true of principles as diverse as atomic absorption spectrometry (U. X-ray fluorescence spectrometry ( ), radio-immunoassays (3), electron capture detection (4) and many more. 

The identification of these 123 compounds (see Table I) was made possible only by the synergistic application of several analytical techniques. For example, the very high concentrations of a few compounds in most of the samples (e.g., no. 6,10,46, 81), precluded identification of many of the minor components during GCMS analysis. This dynamic range problem was solved, at least qualitatively, by HPLC followed by mass spectrometry. 

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