DNA origami is a powerful approach for assembling plasmonic nanoparticle dimers and Raman dyes with high yields and excellent positioning control. Here we show how optothermal-induced shrinking of a DNA origami template can be employed to control the gap sizes between two 40 nm gold nanoparticles in a range from 1 to 2 nm. The high field confinement achieved with this optothermal approach was demonstrated by detection of surface-enhanced Raman spectroscopy (SERS) signals from single molecules that are precisely placed within the DNA origami template that spans the nanoparticle gap. By comparing the SERS intensity with respect to the field enhancement in the plasmonic hot-spot region, we found good agreement between measurement and theory. Our straightforward approach for the fabrication of addressable plasmonic nanosensors by DNA origami demonstrates a path toward future sensing applications with single-molecule resolution.

Self-assembly processes allow designing and creating complex nanostructures using molecules as building blocks and surfaces as scaffolds. This autonomous driven construction is possible due to a complex thermodynamic balance of molecule-surface interactions. As such, nanoscale guidance and control over this process is hard to achieve. Here we use the highly localized light-to-chemical-energy conversion of plasmonic materials to spatially cleave Au-S bonds on predetermined locations within a single nanoparticle, enabling a high degree of control over this archetypal system for molecular self-assembly. Our method offers nanoscale precision and high-throughput light-induced tailoring of the surface chemistry of individual and packed nanosized metallic structures by simply varying wavelength and polarization of the incident light. Assisted by single-molecule super-resolution fluorescence microscopy, we image, quantify, and shed light onto the plasmon-induced desorption mechanism. Our results point toward localized distribution of hot electrons, contrary to uniformly distributed lattice heating, as the mechanism inducing Au-S bond breaking. We demonstrate that plasmon-induced photodesorption enables subdiffraction and even subparticle multiplexing. Finally, we explore possible routes to further exploit these concepts for the selective positioning of nanomaterials and the sorting and purification of colloidal nanoparticles.

Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in their ability to process large-scale data sets, to deal effectively with sample heterogeneity, or require subjective user-defined analysis parameters. Here, we develop a supervised machine-learning approach to cluster analysis which is fast and accurate. Trained on a variety of simulated clustered data, the neural network can classify millions of points from a typical singlemolecule localization microscopy data set, with the potential to include additional classifiers to describe different subtypes of clusters. The output can be further refined for the measurement of cluster area, shape, and point-density. We demonstrate this approach on simulated data and experimental data of the kinase Csk and the adaptor PAG in primary human T cell immunological synapses.