Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic-resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark-field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180° tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

Biomaterials derived via programmable supramolecular protein assembly provide a viable means of constructing precisely defined structures. Here, we present programmed superstructures of AuPt nanoparticles (NPs) on carbon nanotubes (CNTs) that exhibit distinct electrocatalytic activities with respect to the nanoparticle positions via rationally modulated peptide-mediated assembly. De novo designed peptides assemble into six-helix bundles along the CNT axis to form a suprahelical structure. Surface cysteine residues of the peptides create AuPt-specific nucleation site, which allow for precise positioning of NPs onto helical geometries, as confirmed by 3-D reconstruction using electron tomography. The electrocatalytic model system, i.e., AuPt for oxygen reduction, yields electrochemical response signals that reflect the controlled arrangement of NPs in the intended assemblies. Our design approach can be expanded to versatile fields to build sophisticated functional assemblies.