Resolution enhancement linear prediction

Second, the resolution achieved in a 2-D experiment, particularly in the carbon domain is nowhere near as good as that in a 1-D spectrum. You might remember that we recommended a typical data matrix size of 2 k (proton) x 256 (carbon). There are two persuasive reasons for limiting the size of the data matrix you acquire - the time taken to acquire it and the shear size of the thing when you have acquired it This data is generally artificially enhanced by linear prediction and zero-filling, but even so, this is at best equivalent to 2 k in the carbon domain. This is in stark contrast to the 32 or even 64 k of data points that a 1-D 13C would typically be acquired into. For this reason, it is quite possible to encounter molecules with carbons that have very close chemical shifts which do not resolve in the 2-D spectra but will resolve in the 1-D spectrum. So the 1-D experiment still has its place. 

Linear prediction Method of enhancing resolution by artificially extending the FID using predicted valued based on existing data from the FID. 

For the 2-D experiments described here, the spectral width of the proton dimension should be equal to that determined for the 1-D proton spectrum, and the spectral width of the carbon dimension should be equal to that determined for the 1-D carbon spectrum. If the 1-D carbon spectral width has not yet been determined, then 0 - 200 ppm is a reasonable default range. A good compromise of data set size versus adequate resolution is 2048 points in the t2 dimension and 512 points in the ti dimension. This data set size is appropriate for all the 2-D experiments described in this section. Linear prediction can be used to enhance the apparent spectral resolution of truncated data. 

As a result of that study, the combination of covariance processing and NUS afforded a decrease in experimental time by a factor of two relative to linear sampling and covariance processing. It even shortened the total acquisition time by a factor of four as compared to conventionally sampled data and FT processing. Again, enhanced resolution and improved sensitivity were observed. The proof of concept was predicted to be amenable to other homonuclear 2D NMR experiments on liquid, aligned or solid samples. 

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