Speaker
Description
Phytoglycogen (PG) is a natural biopolymer extracted from sweet corn that is composed of highly branched chains of glucose molecules. Its special dendritic chain architecture results in monodisperse nanoparticles with a diameter of 42 nm that are decorated with short chains, and its chemically simple composition results in biodegradability and nontoxicity. This combination of properties makes PG desirable for a variety of biophysical, biomedical, and cosmetic research and applications. We have used atomic force microscopy force spectroscopy (AFM-FS) to study the morphology and mechanical properties of individual PG particles covalently bonded to a flat gold substrate using an intervening self-assembled monolayer of 4-mercaptophenylboronic acid [1]. In AFM-FS, the AFM tip is repeatedly pressed into and retracted from the sample while rastering over a small area of the sample. This allows the measurement of the topography of the sample as well as different mechanical properties, including the sample stiffness and the adhesion forces between the AFM tip and the sample. Recently, we developed an analysis of the AFM-FS data using a supervised machine learning (ML) classifier to identify the location of individual PG nanoparticles on the gold substrate [2]. The ML classifier uses features engineered from individual force-distance curves at each pixel in an AFM scan that describe the mechanical properties of the material interacting with an AFM tip at that location. In the present study, we use the supervised machine learning approach to quantify the AFM tip-PG particle adhesion by identifying and analyzing the force peaks observed during the retraction of the AFM tip in terms of the extensible worm-like chain model. This allows the determination of the contour length distribution of the short chains that decorate the PG particle surface, for which we observe a well-defined peak that is consistent with the average length of the short chains measured using small angle neutron scattering (SANS) [3].
[1] B. Baylis et al., Biomacromolecules 22, 2985 (2021).
[2] B. Baylis et al., Soft Matter (2026).
[3] J. Simmons et al., Biomacromolecules 21, 4053 (2020).
| Keyword-1 | Atomic Force Microscopy |
|---|---|
| Keyword-2 | Phytoglycogen Nanoparticles |
| Keyword-3 | Force Spectroscopy |