Chemical Shifts in H NMR Spectroscopy

We said previously that differences in chemical shifts are cansed hy the small local magnetic fields of electrons surrounding the different nnclei. Nuclei that are more strongly shielded hy electrons require a higher applied field to bring them into resonance and therefore absorb on the right side of the NMR chart. Nuclei that are less strongly shielded need a lower applied field for resonance and therefore absorb on the left of the NMR chart. 

Most chemical shifts fall within the 0 to 10 S range, which can be divided into the five regions shown in Table 11.2. By remembering the positions of these regions, it s often possible to tell at a glance what kinds of protons a molecule contains. 

WORKED EXAMPLE n.3 Predicting Chemical Shifts in H NMR Spectra 

Methyl 2,2-dimethylpropanoate (CH3)3CC02CH3 has two peaks in its NMR spectrum. What are their approximate chemical shifts  

Identify the types of hydrogens in the molecule, and note whether each is alkyl, vinylic, or next to an electronegative atom. Then predict where each absorbs, using Table 11.3 if necessary. 

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The discussions in this section briefly described the use of local descriptors. Another application of local descriptors is the characterization of atoms in nuclear magnetic resonance (NMR) spectroscopy. This is described later with an application for the prediction of chemical shifts in H-NMR spectroscopy, where protons were represented by their local RDF descriptors. 

Another approach has been developed for the prediction chemical shifts in H-NMR spectroscopy. In this case, special proton descriptors were applied to characterize the chemical environment of protons. It can be shown that 3D proton descriptors in combination with geometric descriptors can successfully be used for the fast and accurate prediction of H-NMR chemical shifts of organic compounds. The results indicate that a neural network can make predictions of at least the same quality as those of commercial packages, especially with rigid structures where 3D effects are strong. The performance of the method is remarkable considering a relatively small data set that is required for training. A particularly useful feature of the neural network approach is that the system can be easily dynamically trained for specific types of compounds. 

The prediction of chemical shifts in H-NMR spectroscopy is usually more problematic than in C-NMR. Experimental conditions can have an influence on the chemical shifts in H-NMR spectroscopy and structural effects are difficult to estimate. In particular, stereochemistry and 3D effects have been addressed in the context of empirical H-NMR chemical shift prediction only in a few specific situations [81,82]. Most of the available databases lack stereochemical labeling, assignments for diastereo-topic protons, and suitable representations for the 3D environment of hydrogen nuclei [83]. This is the point where local RDF descriptors seemed to be a promising tool. 

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