Multiple independent NMR dimensions: smart experiments for complicated problems

如果成功，这项工作将使各种NMR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（NMR）光谱是迄今为止用于确定溶液中物质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核自旋信号的能力。化学材料或混合物越复杂，环境的差异就越大，NMR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，而这些都可以通过NMR进行有效的研究。提高NMR分辨率的经典方法是增加所用磁场的强度，但这受到磁体技术的限制。人们花了30年的时间改进磁铁的强度，使核磁共振光谱的基本分辨率提高了一倍，现在最强的磁铁价值数百万英镑。提高分辨率的一种更有效--也更便宜--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，然后用傅里叶变换的数学方法解开它们的响应。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NMR方法无法解决的问题。然而，令人沮丧的是，无论是对大分子还是对小分子的复杂混合物，我们仍然不断地遇到核磁共振分辨率的极限，当现有的方法达到极限时，我们能做些什么来给我们化学分辨率呢？来自不同化学位点的信号如此混杂，以至于我们无法区分它们。该项目将更进一步，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更多种不同的方式独立变化。我们将设计新的实验，专门为张量分析产生数据，使用多个独立的NMR维度（“MIND”）。使用模块化的脉冲序列元素将允许这种MIND方法被纳入一系列现有的实验中，增加其分辨率。我们将生产所有需要让最终用户轻松实现这些新方法：计算机代码来控制光谱仪，处理软件来分析数据，和说明性的例子。这些新的MIND NMR实验将广泛应用于一系列学术和工业研究领域，包括化学，生物化学，生物学，制药，石油化学，农业化学，医疗保健以及香料和香精。

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.