Phase-sensitive γ-encoded recoupling of heteronuclear dipolar interactions and 1H chemical shift anisotropy

IF 1.8 3区 化学 Q4 CHEMISTRY, PHYSICAL
Frédéric A. Perras, Alexander L. Paterson, Takeshi Kobayashi
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引用次数: 1

Abstract

γ-encoded recoupling sequences are known to produce strong amplitude modulations that lead to sharp doublets when Fourier transformed. These doublets depend very little on the recoupled tensor asymmetry and thus enable for the straightforward determination of dynamic order parameters. It can, however, be difficult to measure small anisotropies, or small order parameters, using such sequences; the resonances from the doublet may overlap with each other, or with the zero-frequency glitch. This limitation has prevented the widespread use of 1H chemical shift anisotropy (CSA) for the measurement of dynamics, particularly for CH protons which typically have CSAs of only a few ppm when immobile. Here, we introduce a simple modification to the traditional 1H CSA and proton-detected local field pulse sequences that enables the acquisition of a hypercomplex dataset and the removal of the uncorrelated magnetization that results in the zero-frequency glitch. These new sequences then yield a frequency shift in the indirect dimension, rather than a splitting, which is easily identifiable even in cases of weak interactions.

Abstract Image

异核偶极相互作用的相敏γ编码重耦合和1H化学位移各向异性
已知γ编码的重耦序列在傅里叶变换时产生强烈的振幅调制,导致尖锐的重偶。这些重态几乎不依赖于重耦张量的不对称性,因此可以直接确定动态序参数。然而,使用这种序列很难测量小的各向异性或小的顺序参数;来自重偶态的共振可能彼此重叠,或者与零频率故障重叠。这一限制阻碍了1H化学位移各向异性(CSA)在动力学测量中的广泛使用,特别是对于固定时CSA通常只有几个ppm的CH质子。在这里,我们对传统的1H CSA和质子检测的局部场脉冲序列进行了简单的修改,从而能够获取超复杂的数据集,并去除导致零频率故障的不相关磁化。这些新的序列在间接维度上产生频移,而不是分裂,即使在弱相互作用的情况下也很容易识别。
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来源期刊
CiteScore
5.30
自引率
9.40%
发文量
42
审稿时长
72 days
期刊介绍: The journal Solid State Nuclear Magnetic Resonance publishes original manuscripts of high scientific quality dealing with all experimental and theoretical aspects of solid state NMR. This includes advances in instrumentation, development of new experimental techniques and methodology, new theoretical insights, new data processing and simulation methods, and original applications of established or novel methods to scientific problems.
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