Self-assembly of plasmonic chiral superstructures with intense chiroptical activity

Chiral Plasmonic Pinwheels Exhibit Orientation-Independent Linear Differential Scattering under Asymmetric Illumination

Plasmonic nanoantennas have considerably stronger polarization-dependent optical properties than their molecular counterparts, inspiring photonic platforms for enhancing molecular dichroism and providing fundamental insight into light-matter interactions. One such insight is that even achiral nanoparticles can yield strong optical activity when they are asymmetrically illuminated from a single oblique angle instead of evenly illuminated. This effect, called extrinsic chirality, results from the overall chirality of the experimental geometry and strongly depends on the orientation of the incident light. Although extrinsic chirality has been well-characterized, an analogous effect involving linear polarization sensitivity has not yet been discussed. In this study, we investigate the differential scattering of rotationally symmetric chiral plasmonic pinwheels when asymmetrically irradiated with linearly polarized light. Despite their high rotational symmetry, we observe substantial linear differential scattering that is maintained over all pinwheel orientations. We demonstrate that this orientation-independent linear differential scattering arises from the broken mirror and rotational symmetries of our overall experimental geometry. Our results underscore the necessity of considering both the rotational symmetry of the nanoantenna and the experimental setup, including illumination direction and angle, when performing plasmon-enhanced chiroptical characterizations. Our results demonstrate spectroscopic signatures of an effect analogous to extrinsic chirality for linear polarizations.

Tailoring long-range superlattice chirality in molecular self-assemblies via weak fluorine-mediated interactions

Controllable fabrication of enantiospecific molecular superlattices is a matter of imminent scientific and technological interest. Herein, we demonstrate that long-range superlattice chirality in molecular self-assemblies can be tailored by tuning the interplay of weak intermolecular non-covalent interactions between hexaphenylbenzene-based enantiomers. By means of high-resolution scanning tunneling microscopy measurements, we demonstrate that the functionalization of a hexaphenylbenzene-based molecule with fluorine (F) atoms leads to the formation of molecular self-assemblies with distinct long-range chiral recognition patterns. We employed density functional theory calculations to quantify F-mediated lone pair F⋯π, C-H⋯F, and F⋯F interactions attributed to the distinct enantiospecific molecular self-organizations. Our findings underpin a viable route to fabricate long-range chiral recognition patterns in supramolecular assemblies by engineering the weak non-covalent intermolecular interactions.

Machine Vision Automated Chiral Molecule Detection and Classification in Molecular Imaging

Scanning probe microscopy (SPM) is recognized as an essential characterization tool in a broad range of applications, allowing for real-space atomic imaging of solid surfaces, nanomaterials, and molecular systems. Recently, the imaging of chiral molecular nanostructures via SPM has become a matter of increased scientific and technological interest due to their imminent use as functional platforms in a wide scope of applications, including nonlinear chiroptics, enantioselective catalysis, and enantiospecific sensing. Due to the time-consuming and error-prone image analysis process, a highly efficient analytic framework capable of identifying complex chiral patterns in SPM images is needed. Here, we adopted a state-of-the-art machine vision algorithm to develop a one-image-one-system deep learning framework for the analysis of SPM images. To demonstrate its accuracy and versatility, we employed it to determine the chirality of the molecules comprising two supramolecular self-assemblies with two distinct chiral organization patterns. Our framework accurately detected the position and labeled the chirality of each molecule. This framework underpins the tremendous potential of machine learning algorithms for the automated recognition of complex SPM image patterns in a wide range of research disciplines.