Covariance mapping [L. J. Frasinski, K. Codling, and P. A. Hatherly, Science 246, 1029 (1989)] is a well-established technique used for the study of mechanisms of laser-induced molecular ionization and decomposition. It measures statistical correlations between fluctuating signals of pairs of detected species (ions, fragments, electrons). A positive correlation identifies pairs of products originating from the same dissociation or ionization event. A major challenge for covariance-mapping spectroscopy is accessing decompositions of large polyatomic molecules, where true physical correlations are overwhelmed by spurious signals of no physical significance induced by fluctuations in experimental parameters. As a result, successful applications of covariance mapping have so far been restricted to low-mass systems, e.g., organic molecules of around 50 daltons (Da). Partial-covariance mapping was suggested to tackle the problem of spurious correlations by taking into account the independently measured fluctuations in the experimental conditions. However, its potential has never been realized for the decomposition of large molecules, because in these complex situations, determining and continuously monitoring multiple experimental parameters affecting all the measured signals simultaneously becomes unfeasible. We introduce, through deriving theoretically and confirming experimentally, a conceptually new type of partial-covariance mapping—self-correcting partial-covariance spectroscopy—based on a parameter extracted from the measured spectrum itself. We use the readily available total ion count as the self-correcting partial-covariance parameter, thus eliminating the challenge of determining experimental parameter fluctuations in covariance measurements of large complex systems. The introduced self-correcting partial covariance enables us to successfully resolve correlations of molecules as large as ${10}^3–{10}^4\mathrm{Da}$, 2 orders of magnitude above the state of the art. This opens new opportunities for mechanistic studies of large molecule decompositions through revealing their fragment-fragment correlations. Moreover, we demonstrate that self-correcting partial covariance is applicable to solving the inverse problem: reconstruction of a molecular structure from its fragment spectrum, within two-dimensional partial-covariance mass spectrometry.

中文翻译：

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基于片段相关性的大分子二维偏协方差质谱

协方差映射[L. J. Frasinski，K。Codling和P.A. Hatherly，科学 246，1029（1989）]是用于研究激光诱导的分子电离和分解机理的成熟技术。它测量被检测物种对（离子，碎片，电子）对的波动信号之间的统计相关性。正相关可识别源自同一解离或电离事件的成对产物。协方差映射光谱学的主要挑战是获得大型多原子分子的分解，其中真实的物理相关性被不具物理意义的虚假信号所淹没，这些虚假信号是由实验参数的波动引起的。结果，迄今为止，协方差映射的成功应用仅限于低质量系统，例如约50道尔顿（Da）的有机分子。建议使用部分协方差映射来解决虚假相关性的问题，方法是考虑到在实验条件下独立测量的波动。但是，由于在这些复杂的情况下，无法确定并连续监控同时影响所有测量信号的多个实验参数，因此尚未实现大分子分解的潜力。通过理论推导和实验确认，我们引入了一种概念上新型的部分协方差映射-自校正部分协方差光谱学-基于从测量光谱本身提取的参数。我们使用现成的总离子数作为自校正的部分协方差参数，因此，消除了在大型复杂系统的协方差测量中确定实验参数波动的挑战。引入的自校正部分协方差使我们能够成功解析与${10}^3–{10}^4达$，比现有技术高2个数量级。通过揭示它们的片段-片段相关性，这为大分子分解的机理研究提供了新的机会。此外，我们证明自校正的部分协方差适用于解决反问题：在二维部分协方差质谱内，从其碎片光谱重建分子结构。