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Stanciu, Stefan G.

Publications and source records attributed to Stanciu, Stefan G..

Scattering-type Scanning Near-Field Optical Microscopy of Polymer-Coated Gold Nanoparticles

Scattering-type scanning near-field optical microscopy (s-SNOM) has emerged over the past years as a powerful characterization tool that can probe important properties of advanced materials and biological samples in a label-free manner, with spatial resolutions lying in the nanoscale realm. In this work, we explore such usefulness in relationship with an interesting class of materials: polymer-coated gold nanoparticles (NPs). As thoroughly discussed in recent works, the interplay between the Au core and the polymeric shell has been found to be important in many applications devoted to biomedicine. We investigate bare Au NPs next to polystyrenesulfonate (PSS) and poly(diallyldimethylammonium chloride) (PDDA) coated ones under 532 nm laser excitation, an wavelength matching the surface plasmon band of the custom-synthesized nanoparticles. We observe consistent s-SNOM phase signals in the case of bare and shallow-coated Au NPs, whereas for thicker shell instances, these signals fade. For all investigated samples, the s-SNOM amplitude signals were found to be very weak, which may be related to reduced scattering efficiency due to absorption of the incident beam. We consider these observations important, as they may facilitate studies and applications in nanomedicine and nanotechnology where the precise positioning of polymer-coated Au NPs with nanoscale resolution is needed besides their dielectric function and related intrinsic optical properties, which are also quantitatively available with s-SNOM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗