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多维模式识别在气相分子的高分辨率 2D 和 3D 光谱中的应用。

Multidimensional Pattern Recognition in High-Resolution 2D and 3D Spectra of Gas-Phase Molecules.

机构信息

Department of Chemistry and Biochemistry, Spelman College, 350 Spelman Lane SW, Atlanta, Georgia 30314, United States.

出版信息

Acc Chem Res. 2023 Jun 6;56(11):1295-1303. doi: 10.1021/acs.accounts.2c00637. Epub 2023 Mar 14.

Abstract

ConspectusWhen molecules transition from the condensed phase to the gas phase, their spectra undergo a dramatic transformation as well; each peak in a condensed-phase spectrum can yield thousands of peaks in the gas phase because the molecules are free to rotate and those rotational motions are quantized. These gas-phase spectra contain a wealth of detailed information about molecular structure and behavior, but peak densities are often so high that congestion obscures the patterns needed to assign peaks and extract molecular constants. This Account describes how coherent multidimensional techniques not only reduce peak densities and congestion in gas-phase spectra but also create multidimensional patterns that are easy to recognize and interpret. First, all peaks with the same vibrational quantum numbers form rotational patterns such as X's, double parabolas, and asterisks. These rotational patterns are composed of basic units and can provide immediate information about the molecule's structure, behavior, and rotational selection rules. Second, groups of these rotational patterns can be arranged into vibrational patterns that form arrays of rectangles or parallelograms. These vibrational patterns can be used to determine wave-mixing processes and measure vibrational constants. Coherent multidimensional spectroscopy therefore automatically separates vibrational and rotational information and then sorts peaks by vibrational and rotational quantum number. Furthermore, if the sample is composed of a mixture, then these patterns can also sort peaks by species, and higher-dimensional techniques can even provide the ability to select a species in the mixture. These techniques have successfully produced highly patterned 2D and 3D spectra for samples that otherwise generate patternless spectra such as isotopologue mixtures and vibronically perturbed molecules such as NO.High densities of states can lead to congestion and perturbations that make it difficult to accurately assign peaks using the information that is traditionally available from 1D spectra: a peak's intensity and its frequency. Coherent 2D and 3D techniques are well-suited for dealing with and learning from perturbations because the coordinate of each peak in multidimensional space includes multiple frequency values. Accurate assignments are possible when peaks in 2D or 3D spectra that are perturbed along one frequency axis are unperturbed along an orthogonal frequency axis. Furthermore, patterns often repeat in adjacent rows or columns, so regions that are less congested can be used to resolve or identify key peaks or patterns in regions that are severely congested. Perturbations can make the spacings within multidimensional rotational and vibrational patterns slightly irregular, but these automatically generated patterns remain easy to recognize and analyze.This Account describes three high-resolution coherent multidimensional spectroscopy techniques, the types of patterns they can produce, and how information can be extracted from these patterns. This work is being conducted at Spelman College, a historically Black college for women where all of the students are undergraduates. The resulting techniques are not only highly effective for dealing with some of the most congested, perturbed, and challenging spectroscopic systems, but they are relatively easy to use, moderate in price to set up, and quick to run.

摘要

概述当分子从凝聚相过渡到气相时,它们的光谱也会发生剧烈的变化;凝聚相光谱中的每个峰都可以在气相中产生数千个峰,因为分子可以自由旋转,并且这些旋转运动是量子化的。这些气相光谱包含有关分子结构和行为的丰富详细信息,但由于峰密度通常非常高,因此拥挤会掩盖用于分配峰和提取分子常数所需的模式。本说明描述了相干多维技术如何不仅降低气相光谱中的峰密度和拥挤程度,而且还创建易于识别和解释的多维模式。首先,所有具有相同振动量子数的峰都形成旋转模式,例如 X 形、双抛物线和星号。这些旋转模式由基本单元组成,并且可以立即提供有关分子结构、行为和旋转选择规则的信息。其次,可以将这些旋转模式组排列成形成矩形或平行四边形阵列的振动模式。这些振动模式可用于确定波混合过程并测量振动常数。因此,相干多维光谱自动分离振动和旋转信息,然后按振动和旋转量子数对峰进行排序。此外,如果样品由混合物组成,则这些模式还可以按物种对峰进行排序,而更高维的技术甚至可以提供在混合物中选择物种的能力。这些技术已经成功地为样品生成了高度图案化的 2D 和 3D 光谱,否则这些样品会产生无图案的光谱,例如同位素混合物和振动受激分子,例如 NO。高密度状态会导致拥挤和干扰,这使得使用传统上从 1D 光谱中获得的信息准确分配峰变得困难:峰的强度及其频率。相干 2D 和 3D 技术非常适合处理和学习干扰,因为多维空间中每个峰的坐标都包含多个频率值。当在二维或三维光谱中沿一个频率轴受到干扰的峰在正交频率轴上不受干扰时,就可以进行准确的分配。此外,图案通常会在相邻行或列中重复,因此较少拥挤的区域可用于解决或识别严重拥挤区域中的关键峰或图案。干扰会使多维旋转和振动模式中的间距略微不规则,但这些自动生成的模式仍然易于识别和分析。本说明描述了三种高分辨率相干多维光谱技术、它们可以产生的图案类型以及如何从这些图案中提取信息。这项工作是在斯佩尔曼学院进行的,这是一所历史悠久的黑人女子学院,所有学生都是本科生。所得到的技术不仅对处理一些最拥挤、受干扰和具有挑战性的光谱系统非常有效,而且它们易于使用、价格适中且易于设置、运行速度也很快。

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