Infrared spectroscopic imaging combines the ability to record molecular content with the ability to visualize chemistry in its spatial diversity. Given the need to record a significantly larger quantity of data than a typical microscopy image (MB vs. GB) and the extensive bandwidth of the spectra (~10 m), trade-offs often have to be made between the closely related considerations of signal to noise ratio, spatial-spectral coverage, resolution and optical arrangements. Here, we present a path from rigorous theory to modeling and design to realizing the advantages offered by new ideas on fundamentally changing these trade-offs. We first describe a new microscope design for increased speed and rapid coverage that is useful for biomedical and clinical tissue imaging. Next, we describe a configuration to measure chirality in samples that promises higher spectral information that present methods. Finally, we present a new approach to nanoscale IR imaging that provides greater fidelity and speed at unprecedented levels of signal to noise ratio. Finally, we show how emerging machine learning approaches can further augment these advances. For each instrumentation advance, examples of use cases will be presented.
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