The molten-salt assisted self-assembly (MASA) process is applicable to fabricate high quality mesoporous metal lithiate thin films that exhibit excellent performance as electrocatalysts for water oxidation.

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual‐VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape‐spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far‐field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure‐property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

In this study, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates using automated image analysis tool.