Temperini, M.E.; Spedalieri, C.; Intze, A.; Giliberti, V.; Ortolani, M.; Kneipp, J.; Veber, A.: Understanding In-Liquid Sample Environments for Infrared Nanospectroscopy of Soft Materials. Analytical Chemistry early view (2026)
10.1021/acs.analchem.6c03284
Open Accesn Version
Abstract:
Recent advances in infrared (IR) scattering-type scanning near-field optical microscopy (s-SNOM) have enabled label-free IR spectroscopy of biomolecules and cells in native aqueous environments with nanoscale spatial resolution. This is achieved using an ultrathin membrane that prevents direct mechanical interaction between the atomic force microscope probe and the liquid while permitting the evanescent optical field to detect the underlying sample. In this configuration, the membrane influences the far- and near-field response of the microscope, requiring the multilayered sample system to be considered when interpreting near-field interactions. Here, we present a systematic study of ultrathin silicon nitride (SiNx) and silicon carbide (SiC) membranes, investigating their near-field optical response both in a dry condition and in contact with aqueous solutions of bovine serum albumin and DNA molecules. In addition, we demonstrate nanoscale IR (nano-IR) imaging and spectroscopy of individual aggregates of α-synuclein protein in the form of fibrils, in a liquid environment under a SiNx membrane. Results indicate that the membrane material not only influences the available transparency window but also introduces spectral changes determined by the optical properties of the membranes and the adhesion of the sample to the substrate. Furthermore, we show that finite dipole modeling can reproduce the near-field spectra of membrane-biomolecule stacked samples, providing a consistent framework for interpreting experimental nano-IR results. This comparative study provides insights relevant for selecting membrane materials for mid-IR s-SNOM experiments in liquid, highlighting the advantages and limitations of SiNx and SiC materials, while identifying potential sources of artifacts and deviations from the idealized model.