Understanding AFM-IR Signal Dependence on Sample Thickness and Laser Excitation: Experimental and Theoretical Insights

Anal Chem. 2024 Oct 15;96(41):16195-16202. doi: 10.1021/acs.analchem.4c02834. Epub 2024 Oct 4.

Abstract

Photothermal induced resonance (PTIR), also known as atomic force microscopy-infrared (AFM-IR), enables nanoscale IR absorption spectroscopy by transducing the local photothermal expansion and contraction of a sample with the tip of an atomic force microscope. PTIR spectra enable material identification at the nanoscale and can measure sample composition at depths >1 μm. However, implementation of quantitative, multivariate, nanoscale IR analysis requires an improved understanding of PTIR signal transduction and of the intensity dependence on sample characteristics and measurement parameters. Here, PTIR spectra measured on three-dimensional printed conical structures up to 2.5 μm tall elucidate the signal dependence on sample thickness for different IR laser repetition rates and pulse lengths. Additionally, we develop a model linking sample thermal expansion dynamics to cantilever excitation amplitudes that includes samples that do not fully thermalize between consecutive pulses. Remarkable qualitative agreement between experiments and theory demonstrates a monotonic increase in the PTIR signal intensity with thickness, with decreasing sensitivities at higher repetition rates, while signal intensity is nearly unaffected by laser pulse length. Although we observe slight deviations from linearity over the entire 2.5 μm thickness range, the signal's approximate linearity for bands of sample thicknesses up to ≈500 nm suggests that samples with comparably low topographic variations are most amenable to quantitative analysis. Importantly, we measure absorptive undistorted profiles in PTIR spectra for strongly absorbing modes, up to ≈1650 nm, and >2500 nm for other modes. These insights are foundational toward quantitative nanoscale PTIR analyses and material identification, furthering their impact across many applications.