Publications

Nanowire-based nanocomposite materials are being developed as transparent and flexible electrodes or as stretchable conductors and dielectrics for biosensing. Here, we theoretically investigate the use of scanning dielectric microscopy (SDM) to characterize these materials in a nondestructive way, with a special focus on the achievable spatial resolution and the possibility of detection of the capacitive coupling between nearby nanowires. Numerical calculations with models involving single and multiple buried nanowires have been performed. We demonstrate that the capacitance gradient spread function of a single buried nanowire consists of a modified Lorenzianan with a cubic decay. We show that the achievable spatial resolution can be determined with good accuracy with the help of this spread function. It is shown that, in general, the spatial resolution worsens when any system parameter decreases the maximum of the nanowire spread function or increases its width, or both. Finally, we show that SDM measurements are also sensitive to the capacitive coupling between nearby nanowires. This latter result is of utmost relevance since the macroscopic electric properties of nanowire nanocomposites largely depend on the electric interaction between nearby nanowires. The present results show that SDM can be a valuable nondestructive subsurface characterization technique for nanowire nanocomposite materials.

JTD Keywords: depth, electrodes, nanocomposites, nanowires, sdm, spatial resolution, subsurface, tomography, Capacitive coupling, Force microscopy, Nanocomposites, Nanowires, Sdm, Spatial resolution, Subsurface

Mapping the biochemical composition of eukaryotic cells without the use of exogenous labels is a long-sought objective in cell biology. Recently, it has been shown that composition maps on dry single bacterial cells with nanoscale spatial resolution can be inferred from quantitative nanoscale dielectric constant maps obtained with the scanning dielectric microscope. Here, it is shown that this approach can also be applied to the much more challenging case of fixed and dry eukaryotic cells, which are highly heterogeneous and show micrometric topographic variations. More importantly, it is demonstrated that the main bottleneck of the technique (the long computation times required to extract the nanoscale dielectric constant maps) can be shortcut by using supervised neural networks, decreasing them from weeks to seconds in a wokstation computer. This easy-to-use data-driven approach opens the door for in situ and on-the-fly label free nanoscale composition mapping of eukaryotic cells with scanning dielectric microscopy. © 2021 The Authors. Small Methods published by Wiley-VCH GmbH

JTD Keywords: eukaryotic cells, label-free mapping, machine learning, nanoscale, scanning dielectric microscopy, Biochemical composition, Cells, Constant, Cytology, Data-driven approach, Dielectric forces, Dielectric materials, Eukaryotic cells, Label-free mapping, Machine learning, Mapping, Microscopy, atomic force, Nanoscale, Nanoscale composition, Nanoscale spatial resolution, Nanotechnology, Scanning, Scanning dielectric microscopy, Supervised neural networks