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飞秒动力学数据的无模型反卷积

Model-free deconvolution of femtosecond kinetic data.

作者信息

Bányász Akos, Keszei Erno

机构信息

Eötvös University Budapest, Department of Physical Chemistry, 1518 Budapest 112, P.O. Box 32, Hungary.

出版信息

J Phys Chem A. 2006 May 18;110(19):6192-207. doi: 10.1021/jp057486w.

Abstract

Though shorter laser pulses can also be produced, pulses of the 100 fs range are typically used in femtosecond kinetic measurements, which are comparable to characteristic times of the studied processes, making detection of the kinetic response functions inevitably distorted by convolution with the pulses applied. A description of this convolution in terms of experiments and measurable signals is given, followed by a detailed discussion of a large number of available methods to solve the convolution equation to get the undistorted kinetic signal, without any presupposed kinetic or photophysical model of the underlying processes. A thorough numerical test of several deconvolution methods is described, and two iterative time-domain methods (Bayesian and Jansson deconvolution) along with two inverse filtering frequency-domain methods (adaptive Wiener filtering and regularization) are suggested to use for the deconvolution of experimental femtosecond kinetic data sets. Adaptation of these methods to typical kinetic curve shapes is described in detail. We find that the model-free deconvolution gives satisfactory results compared to the classical "reconvolution" method where the knowledge of the kinetic and photophysical mechanism is necessary to perform the deconvolution. In addition, a model-free deconvolution followed by a statistical inference of the parameters of a model function gives less biased results for the relevant parameters of the model than simple reconvolution. We have also analyzed real-life experimental data and found that the model-free deconvolution methods can be successfully used to get undistorted kinetic curves in that case as well. A graphical computer program to perform deconvolution via inverse filtering and additional noise filters is also provided as Supporting Information. Though deconvolution methods described here were optimized for femtosecond kinetic measurements, they can be used for any kind of convolved data where measured experimental shapes are similar.

摘要

尽管也可以产生更短的激光脉冲,但在飞秒动力学测量中通常使用100飞秒范围内的脉冲,这与所研究过程的特征时间相当,使得动力学响应函数的检测不可避免地因与所施加脉冲的卷积而失真。本文给出了根据实验和可测量信号对这种卷积的描述,随后详细讨论了大量可用的方法来求解卷积方程以获得未失真的动力学信号,而无需对潜在过程进行任何预先假定的动力学或光物理模型。描述了对几种去卷积方法的全面数值测试,并建议使用两种迭代时域方法(贝叶斯和扬松去卷积)以及两种逆滤波频域方法(自适应维纳滤波和正则化)对实验飞秒动力学数据集进行去卷积。详细描述了这些方法对典型动力学曲线形状的适应性。我们发现,与经典的“再卷积”方法相比,无模型去卷积给出了令人满意的结果,在经典方法中,进行去卷积需要动力学和光物理机制的知识。此外,与简单的再卷积相比,无模型去卷积之后对模型函数参数进行统计推断会使模型相关参数的偏差更小。我们还分析了实际实验数据,发现在这种情况下无模型去卷积方法也可以成功用于获得未失真的动力学曲线。作为支持信息,还提供了一个通过逆滤波和附加噪声滤波器进行去卷积的图形化计算机程序。尽管这里描述的去卷积方法是针对飞秒动力学测量进行优化的,但它们可用于任何测量实验形状相似的卷积数据。

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