科研之友
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摘要
A detailed depth characterization of multilayered polymeric systems is a very attractive topic. Currently, the use of cluster primary ion beams in time-of-flight secondary ion mass spectrometry allows molecular depth profiling of organic and polymeric materials. Because typical raw data may contain thousands of peaks, the amount of information to manage grows rapidly and widely, so that data reduction techniques become indispensable in order to extract the most significant information from the given dataset. Here, we show how the wavelet-based signal processing technique can be applied to the compression of the giant raw data acquired during time-of-flight secondary ion mass spectrometry molecular depth-profiling experiments. We tested the approach on data acquired by analyzing a model sample consisting of polyelectrolyte-based multilayers spin-cast on silicon. Numerous wavelet mother functions and several compression levels were investigated. We propose some estimators of the filtering quality in order to find the highest 'safe' approximation value in terms of peaks area modification, signal to noise ratio, and mass resolution retention. The compression procedure allowed to obtain a dataset straightforwardly 'manageable' without any peak-picking procedure or detailed peak integration. Moreover, we show that multivariate analysis, namely, principal component analysis, can be successfully combined to the results of the wavelet-filtering, providing a simple and reliable method for extracting the relevant information from raw datasets.
- 出版日期2016-6
