2007
DOI: 10.1016/j.jmr.2007.02.003
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Computer optimized spectral aliasing in the indirect dimension of 1H–13C heteronuclear 2D NMR experiments. A new algorithm and examples of applications to small molecules

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Cited by 53 publications
(29 citation statements)
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“…Powerful methods exist to choose a spectral width that is as small as possible without generating any ambiguities due to folding [25][26][27]. If the proton spectrum is unknown, one should repeat the experiment with a slightly different spectral width in the direct x 2 dimension.…”
Section: Theorymentioning
confidence: 99%
“…Powerful methods exist to choose a spectral width that is as small as possible without generating any ambiguities due to folding [25][26][27]. If the proton spectrum is unknown, one should repeat the experiment with a slightly different spectral width in the direct x 2 dimension.…”
Section: Theorymentioning
confidence: 99%
“…A straightforward and efficient approach has been developed by Jeannerat [9,10], based on the optimization of spectral aliasing. In this method, 2D peaks are folded along the indirect dimension, resulting in a smaller spectral range to be sampled.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, the spectral aliasing method described above [9,10] cannot be employed in the k-domain, as Fourier Transform is not applied in the ultrafast dimension. Therefore, Pelupessy et al proposed to add a band-selective refocusing pulse flanked by a bipolar gradient pair before the mixing period to recover signals lying out of the observed range [22].…”
Section: Introductionmentioning
confidence: 99%
“…These include relaxation optimization [1], minimal sampling in the indirect dimension combined with aliasing or specialised processing methods [2][3][4][5][6][7] and reduced dimensionality (projection) methods [2]. These methods all rely on time-dependent intensity-modulation of the directly observed signals which is transformed to produce multidimensional spectra where chemical shifts can be measured from frequency axes.…”
Section: Introductionmentioning
confidence: 99%