通过自旋动态平均场理论从微观角度理解核磁共振信号

IF 1.8 3区 化学 Q4 CHEMISTRY, PHYSICAL
Timo Gräßer , Thomas Hahn , Götz S. Uhrig
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引用次数: 0

摘要

最近开发的无序自旋动态均场理论(spinDMFT)可以很好地捕捉核自旋的自旋动态。关键量是自旋自相关性。为了计算自由感应衰变(FID),还需要对相关性。它们可以在中等大小的自旋簇上计算,这些自旋簇与自旋DMFT 第一步确定的动态平均场耦合。我们把这种多功能方法称为非局部自旋DMFT(nl-spinDMFT)。nl-spinDMFT 的一个特殊优势是,我们知道 FID 的贡献来自何处。我们将 nl-spinDMFT 与 CaF 的实验数据进行比较,以说明 nl-spinDMFT 的优势。此外,spinDMFT 还提供了解释金刚烷中 C 的核自旋 FID 的动态平均场,直至一些静态噪声。金刚烷中的自旋哈恩回波不受静态噪声的影响,与自旋DMFT 的结果非常吻合,无需进一步拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Microscopic understanding of NMR signals by dynamic mean-field theory for spins

Microscopic understanding of NMR signals by dynamic mean-field theory for spins

Microscopic understanding of NMR signals by dynamic mean-field theory for spins

A recently developed dynamic mean-field theory for disordered spins (spinDMFT) is shown to capture the spin dynamics of nuclear spins very well. The key quantities are the spin autocorrelations. In order to compute the free induction decay (FID), pair correlations are needed in addition. They can be computed on spin clusters of moderate size which are coupled to the dynamic mean fields determined in a first step by spinDMFT. We dub this versatile approach non-local spinDMFT (nl-spinDMFT). It is a particular asset of nl-spinDMFT that one knows from where the contributions to the FID stem. We illustrate the strengths of nl-spinDMFT in comparison to experimental data for CaF2. Furthermore, spinDMFT provides the dynamic mean fields explaining the FID of the nuclear spins of 13C in adamantane up to some static noise. The spin Hahn echo in adamantane is free from effects of static noise and agrees excellently with the spinDMFT results without further fitting.

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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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