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Galvanic replacement (GR) is a simple and widely used approach to synthesize hollow nanostructures for applications in catalysis, plasmonics, and biomedical research. The reaction is driven by the difference in electrochemical potential between two metals in a solution. However, transient stages of this reaction are not fully understood. Here, we show using liquid cell transmission electron microscopy that silver (Ag) nanocubes become hollow via the nucleation, growth, and coalescence of voids inside the nanocubes, as they undergo GR with gold (Au) ions at different temperatures. These direct in situ observations indicate that void formation due to the nanoscale Kirkendall effect occurs in conjunction with GR. Although this mechanism has been suggested before, it has not been verified experimentally until now. These experiments can inform future strategies for deriving such nanostructures by providing insights into the structural transformations as a function of Au ion concentration, oxidation state of Au, and temperature. Hollow nanoparticles can be synthesized by galvanic replacement or the Kirkendall effect, which are generally regarded as two separate processes. Here, the authors use liquid TEM to follow the entire galvanic replacement of Ag nanocubes, finding experimental evidence that the Kirkendall effect is a key intermediate stage during hollowing.

At elevated temperatures, bimetallic nanomaterials change their morphologies because of the interdiffusion of atomic species, which also alters their properties. The Kirkendall effect (KE) is a well-known phenomenon associated with such interdiffusion. Here, we show how KE can manifest in bimetallic nanoparticles (NPs) by following core–shell NPs of Au and Pd during heat treatment with in situ transmission electron microscopy. Unlike monometallic NPs, these core–shell NPs did not evolve into hollow core NPs. Instead, nanoscale voids formed at the bimetallic interface and then, migrated to the NP surface. Our results show that: (1) the direction of vacancy flow during interdiffusion reverses due to the higher vacancy formation energy of Pd compared to Au, and (2) nanoscale voids migrate during heating, contrary to conventional assumptions of immobile voids and void shrinkage through vacancy emission. Our results illustrate how void behavior in bimetallic NPs can differ from an idealized picture based on atomic fluxes and have important implications for the design of these materials for high-temperature applications. At elevated temperatures, bimetallic nanomaterials often change their composition and morphology due to the interdiffusion of atomic species, which can affect their properties. Here, the authors use in situ transmission electron microscopy to provide new insights into the Kirkendall effect and void motion in core-shell nanoparticles of Au and Pd.

Three-dimensional (3D) imaging of thin, extended specimens at nanometer resolution is critical for applications in biology, materials science, advanced synthesis, and manufacturing. One route to 3D imaging is tomography, which requires a tilt series of a local region. However, capturing images at higher tilt angles is infeasible for such thin, extended specimens. Here, we explore a suitable alternative to reconstruct the 3D volume using a single, energy-filtered, bright-field coherent image. We show that when our specimen is homogeneous and amorphous, simultaneously inferring local depth and thickness for 3D imaging is possible in the near-field limit. We demonstrated this technique with a transmission electron microscope to fill a glaring gap for rapid, accessible 3D nanometrology. This technique is applicable, in general, to any coherent bright field imaging with electrons, photons, or any other wavelike particles. The authors experimentally demonstrate with a transmission electron microscope that single-shot 3D imaging is possible in the near-field limit, by simultaneously inferring local depth and thickness. The proposed reconstruction method uses priors from the homogenously amorphous specimen, and it can be extended for imaging multi-layered samples.

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).