Multiple independent NMR dimensions: smart experiments for complicated problems

如果成功，这项工作将使多种核磁共振方法的化学分辨率提高一倍以上。为什么这很重要？核磁共振波谱是迄今为止确定溶液中物种化学结构的最重要和最广泛使用的工具，但它绝对依赖于区分不同化学环境中的核自旋信号的能力。化学材料或混合物越复杂，不同的环境就越多，核磁共振就越难解决它们之间的差异。这既限制了分子的大小，也限制了混合物的复杂程度，这对核磁共振研究很有帮助。提高核磁共振分辨率的经典方法是增加所用磁场的强度，但这受到磁铁技术的限制。30年来，磁铁强度的提高使核磁共振光谱学的基本分辨率翻了一番，而最强的磁铁现在要花费数百万英镑。一种更有效、更便宜的提高分辨率的方法是使用更复杂的实验，用多个无线电波脉冲激发核自旋，然后使用傅里叶变换的数学方法来解开它们的响应。这被证明是非常有效的，现在使化学家和生物科学家能够解决基本的核磁共振方法无法解决的问题。然而，令人沮丧的是，我们仍然不断地遇到核磁共振分辨率的极限，无论是在大分子中还是在小分子的复杂混合物中。当现有方法达到极限时，来自不同化学位置的信号如此混杂，以至于我们无法区分它们，我们可以做什么来给我们提供化学分辨率呢？这个项目将更进一步，将额外的信息编码到这些重叠的信号中，然后使用先进的统计方法，即所谓的矩阵张量分解，来解开它们。最强大的算法，如并行因子分析(PARAFAC)，需要实验数据以三种或三种以上不同的方式独立变化。我们将设计新的实验，以产生用于张量分析的数据，使用多个独立的核磁共振维度(“Mind”)。使用模块化的脉冲序列元素将允许将这种思维方法整合到一系列现有的实验中，从而提高它们的分辨率。我们将提供允许最终用户轻松实施这些新方法所需的所有内容：控制光谱仪的计算机代码、分析数据的处理软件，以及说明性的例子。这些新的Mind核磁共振实验将在一系列学术和工业研究领域得到广泛应用，包括化学、生物化学、生物、制药、石化、农业化学、保健以及香料和香料。

If successful, this work will more than double the chemical resolution of a wide range of NMR methods. Why does this matter?Nuclear Magnetic Resonance (NMR) spectroscopy is by far the most important and widely-used tool for determining the chemical structures of species in solution, but it relies absolutely on the ability to distinguish between the signals of nuclear spins in different chemical environments. The more complex a chemical material or mixture, the more different environments there are, and the more NMR struggles to resolve the differences between them. This limits both the size of a molecule, and the degree of complexity of a mixture, that can usefully be studied by NMR. The classic way to improve the resolution of NMR is to increase the strength of the magnetic field used, but this is limited by magnet technology. It has taken 30 years for improvements in magnet strength to double the basic resolution of NMR spectroscopy, and the strongest magnets now cost many millions of pounds. A more efficient - and much cheaper - way to improve resolution is to use more sophisticated experiments, exciting the nuclear spins with multiple pulses of radio waves and then disentangling their responses using the mathematics of the Fourier transform. This has proved very effective, and now enables chemists and bioscientists to solve problems that are far out of the reach of basic NMR methods. Frustratingly, though, we still continually come up against the limits of resolution in NMR, whether in large molecules or in complex mixtures of small ones.What can we do to give us chemical resolution when existing methods reach their limits, and the signals from different chemical sites are so intermingled that we cannot tell them apart? This project will go a significant step further, encoding extra information into these overlapping signals and then using advanced statistical methods, so-called matrix tensor decomposition, to disentangle them. The most powerful algorithms, such as parallel factor analysis (PARAFAC), need the experimental data to vary independently in three or more different ways. We will design new experiments that are tailored to produce data for tensor analysis, using multiple independent NMR dimensions ("MIND"). Using modular pulse sequence elements will allow this MIND approach to be incorporated into a range of existing experiments, multiplying their resolving power. We will produce all that is needed to allow end users to implement these new methods easily: computer code to control the spectrometer, processing software to analyse the data, and illustrative examples. These new MIND NMR experiments will have wide application across a range of academic and industrial research areas, including chemistry, biochemistry, biology, pharmacy, petrochemistry, agrochemistry, healthcare, and flavours and fragrances.