DETERMINATION OF HIGH-PRECISION ISOTOPE RATIOS FROM EXPERIMENTAL DISTRIBUTIONS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 自然过程引起的同位素变异性为研究各种复杂现象提供了重要信息，如确定特定样品的成因、物种的饮食研究、树木的硝化速率等。这些测量需要非常高精度地确定所涉及的特定元素的同位素比率。同位素比质谱仪(IRMS)是进行这种高精度分析的常用工具。IRMS仪器只接受数量有限的气体形式的样品分析物，这些气体必须代表原始样品的同位素特征，这导致缺乏灵活性。这项工作旨在通过根据实验的同位素分布估计元素同位素丰度来克服IRMS固有的局限性。 实验同位素分布是对分子中存在的各种元素的同位素丰度的间接测量。它可以由每个单独原子的同位素丰度的联合卷积来表示。发展了数学技术，以求出各种元素的已知同位素丰度贡献，然后计算感兴趣元素的相应未知值。通过求解卷积方程，根据每个观测到的同位素峰生成所需同位素丰度的估计值，据报道，最终结果是所获得的每个单独值的平均值。 为了生成已知元素组成和同位素丰度的给定分子的实验同位素分布，进行了计算机生成的模拟。这样得到的结果符合所开发的理论框架，以便在计算中考虑到其他元素的丰度值，以从每个同位素峰估算碳的同位素丰度。这些估计结果与它们的真实值很好地吻合。增加用于生成实验同位素分布的离子数量将极大地改进估计。这是因为在无限离子数目的限制下，实验的同位素分布接近于其理论对应的分布，并且真实地代表了其成分的组成。高分子量分子被证明是特别有利的，因为在同位素分布中存在更多的同位素峰，导致更多的信息量。初步结果表明，在足够多的离子和多次实验的情况下，可以区分碳同位素丰度变化很小的样品。例如，该估计可以区分样本是来自海洋浮游生物(C13丰度=1.09%，Delta C13=-19.5)，还是来自以C4植物为食的动物的肉类(C13丰度=1.1%，Delta C13=-12.5)。实验数据的结果将给出真实丰度值与其估计值的比较。这种方法消除了将样品转化为气体形式的需要。结果适用于任何类型的质谱计，也适用于任何类型的样品。如果其他元素的同位素丰度是先验已知的，则该结果还可以扩展到估计任何未知元素的同位素丰度。 在《分析化学》上发表了一篇论文，详细介绍了这种方法和计算。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Isotope variability due to natural processes provides important information for studying a variety of complex phenomenon like determining the genesis of a given sample, dietary studies of species, nitrification rates in trees etc. These measurements require very high-precision determination of isotope ratios of a particular element involved. Isotope Ratio Mass Spectrometers (IRMS) are widely employed tools for such a high-precision analysis. IRMS instruments accept the sample analyte in the form of only a limited number of gases which must represent the isotopic characteristics of the original sample, which causes lack of flexibility. This work aims at overcoming the limitations inherent to IRMS by estimating the elemental isotopic abundance from the experimental isotopic distribution. Experimental isotopic distribution is an indirect measure of the isotopic abundances of various elements present in the molecule. It can be represented by the joint convolution of the isotopic abundances of each of the individual atoms. Mathematical techniques have been developed in order to factor out the known isotopic abundance contributions from various elements followed by calculation of the corresponding unknown values for the element of interest. An estimate for the required isotopic abundance is generated from each of the observed isotopic peaks by solving the convolution equations, and the final result is reported to be the mean of each of the individual values obtained. Computer generated simulations were carried out in order to generate the experimental isotopic distributions for a given molecule with known elemental composition and isotopic abundances. The results thus obtained were subjected to the developed theoretical framework in order to estimate the isotopic abundance for Carbon from each isotopic peak, with the abundance values for the other elements being taken into consideration in the calculation. These estimated results are shown to be in good agreement with their true values. Increasing the number of ions for generating the experimental isotopic distribution greatly improves the estimate. This is because in the limit of infinite number of ions, experimental isotopic distribution approaches its theoretical counterpart and is a true representative of the composition of its constituents. High molecular weight molecules are shown to be particularly advantageous because of the presence of larger number of isotopic peaks in the isotopic distribution leading to a greater amount of information. Initial results reveal that with sufficiently high number of ions and multiple experiments, it is possible to distinguish between the samples varying very slightly in the Carbon isotopic abundance. For example, the estimate can distinguish whether the sample originated from marine plankton (C13 abundance = 1.09%, Delta C13=-19.5) or meat from an animal feeding on a C4 plant (C13 abundance=1.1%, Delta C13=-12.5). Results from the experimental data will be presented comparing the true abundance value with its estimated counterpart. This approach eliminates the need to convert the sample into gaseous form. The results are applicable to any type of mass spectrometer, and for any type of sample. The results can also be extended to estimate the isotopic abundance of any unknown element provided the isotopic abundance of the other elements are known a priori. A paper was published detailing the method and calculations in Analytical Chemistry.