Model-free deconvolution of femtosecond kinetic data.
Model-free deconvolution of femtosecond kinetic data.
复制标题
飞秒动力学数据的无模型反卷积。
DOI：
10.1021/jp057486w
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
E. Keszei
中科院分区：
文献类型：
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作者：
A. Banyasz;E. Keszei
虽然也可以产生较短的激光脉冲，但通常在飞秒动力学测量中使用100 fs范围的脉冲，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的脉冲的卷积而失真。给出了在实验和可测量信号方面的这种卷积的描述，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，而无需任何预先假定的动力学或物理模型的基础过程。对几种反卷积方法进行了深入的数值试验，提出了两种时域迭代方法（Bayesian反卷积和Jansson反卷积）沿着以及两种频域逆滤波方法（自适应Wiener滤波和正则化）用于实验飞秒动力学数据集的反卷积。这些方法的适应典型的动力学曲线形状的详细描述。我们发现，无模型的反褶积给出了令人满意的结果相比，经典的“reconvolution”方法的动力学和物理机制的知识是必要的，以执行反褶积。此外，无模型反褶积，然后对模型函数的参数进行统计推断，与简单的反褶积相比，对模型的相关参数给出了更少的偏差结果。我们还分析了实际的实验数据，发现无模型反卷积方法也可以成功地用于在这种情况下得到不失真的动力学曲线。通过逆滤波和附加噪声滤波器执行反卷积的图形计算机程序也作为支持信息提供。虽然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，但是它们可以用于测量的实验形状相似的任何类型的卷积数据。
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.