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Co-registered Topographical, Band Excitation Nanomechanical and Mass Spectral Imaging using a Combined Atomic Force Microscop...

Publication Type
Journal
Journal Name
ACS Nano
Publication Date
Page Numbers
4260 to 4269
Volume
9
Issue
4

The advancement of a hybrid atomic force microscopy/mass spectrometry imaging platform demonstrating for the first time co-registered topographical, band excitation nanomechanical, and mass spectral imaging of a surface using a single instrument is reported. The mass spectrometry-based chemical imaging component of the system utilized nanothermal analysis probes for pyrolytic surface sampling followed by atmospheric pressure chemical ionization of the gas phase species produced with subsequent mass analysis. The basic instrumental setup and operation are discussed and the multimodal imaging capability and utility are demonstrated using a phase separated polystyrene/poly(2-vinylpyridine) polymer blend thin film. The topography and band excitation images showed that the valley and plateau regions of the thin film surface were comprised primarily of one of the two polymers in the blend with the mass spectral chemical image used to definitively identify the polymers at the different locations. Data point pixel size for the topography (390 nm x 390 nm), band excitation (781 nm x 781 nm), mass spectrometry (690 nm x 500 nm) images was comparable and submicrometer in all three cases, but the data voxel size for each of the three images was dramatically different. The topography image was uniquely a surface measurement, whereas the band excitation image included information from an estimated 10 nm deep into the sample and the mass spectral image from 110-140 nm in depth. Because of this dramatic sampling depth variance, some differences in the band excitation and mass spectrometry chemical images were observed and were interpreted to indicate the presence of a buried interface in the sample. The spatial resolution of the mass spectral image was estimated to be between 1.5 m – 2.6 m, based on the ability to distinguish surface features in that image that were also observed in the other images.