Open AccessCorrelated Materials Characterization via Multimodal Chemical and Functional Imaging
Author(s) -
Alex Belianinov,
Anton V. Ievlev,
Matthias Lorenz,
Nikolay Borodinov,
Benjamin Doughty,
Sergei V. Kalinin,
Facundo M. Fernández,
Olga S. Ovchinnikova
Publication year - 2018
Publication title -
acs nano
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.554
H-Index - 382
eISSN - 1936-086X
pISSN - 1936-0851
DOI - 10.1021/acsnano.8b07292
Subject(s) - chemical imaging , characterization (materials science) , nanotechnology , microscopy , computer science , resolution (logic) , materials science , artificial intelligence , hyperspectral imaging , physics , optics
Multimodal chemical imaging simultaneously offers high-resolution chemical and physical information with nanoscale and, in select cases, atomic resolution. By coupling modalities that collect physical and chemical information, we can address scientific problems in biological systems, battery and fuel cell research, catalysis, pharmaceuticals, photovoltaics, medicine, and many others. The combined systems enable the local correlation of material properties with chemical makeup, making fundamental questions of how chemistry and structure drive functionality approachable. In this Review, we present recent progress and offer a perspective for chemical imaging used to characterize a variety of samples by a number of platforms. Specifically, we present cases of infrared and Raman spectroscopies combined with scanning probe microscopy; optical microscopy and mass spectrometry; nonlinear optical microscopy; and, finally, ion, electron, and probe microscopies with mass spectrometry. We also discuss the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for the interpretation of multidimensional data acquired from multimodal studies.