摘要
The coupling of atomic force microscopy with infrared spectroscopy(AFM-IR)offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution.However,in order to fully utilize the measurement capability of AFM-IR,a three-dimensional dataset(2D map with a spectroscopic dimension)needs to be acquired,which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan.In this paper,we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm.This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image,greatly reducing data acquisition time.As a result,we are able to generate highresolution maps of component distribution,produce chemical maps at any wavenumber available in the spectral range,and perform correlative analysis of the physical and chemical properties of the samples.We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition.We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.
基金
Algorithm development was part of the AI Initiative,sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory(S.J.,R.K.V),managed by UT-Battelle,LLC,for the U.S.Department of Energy(DOE)
The plant sciences portion of this work was supported by the Center for Engineering MechanoBiology(CEMB),an NSF Science and Technology Center,under grant agreement CMMI:15-48571(N.B.and M.F.).
