No abstract

Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos’ high noisiness and spatial heterogeneity. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. We apply this framework to three typical systems of colloidal nanoparticles, concerning their diffusion and interaction, reaction kinetics, and assembly dynamics, all resolved in real-time and real-space by liquid-phase TEM. A diversity of properties for differently shaped anisotropic nanoparticles are mapped, including the anisotropic interaction landscape of nanoprisms, curvature-dependent and staged etching profiles of nanorods, and an unexpected kinetic law of first-order chaining assembly of concave nanocubes. These systems representing properties at the nanoscale are otherwise experimentally inaccessible. Compared to the prevalent image segmentation methods, U-Net shows a superior capability to predict the position and shape boundary of nanoparticles from highly noisy and fluctuating background—a challenge common and sometimes inevitable in liquid-phase TEM videos. We expect our framework to push the potency of liquid-phase TEM to its full quantitative level and to shed insights, in high-throughput and statistically significant fashion, on the nanoscale dynamics of synthetic and biological nanomaterials.

We use liquid-phase transmission electron microscopy (LP-TEM) to characterize the structure and dynamics of a solution-phase superlattice assembled from gold nanoprisms at the single particle level. The lamellar structure of the superlattice, determined by a balance of interprism interactions, is maintained and resolved under low-dose imaging conditions typically reserved for biomolecular imaging. In this dose range, we capture dynamic structural changes in the superlattice in real time, where contraction and smaller steady-state lattice constants are observed at higher electron dose rates. Quantitative analysis of the contraction mechanism based on a combination of direct LP-TEM imaging, ensemble small-angle X-ray scattering, and theoretical modeling allows us to elucidate: (1) the superlattice contraction in LP-TEM results from the screening of electrostatic repulsion due to as much as a 6-fold increase in the effective ionic strength in the solution upon electron beam illumination; and (2) the lattice constant serves as a means to understand the mechanism of the in situ interaction modulation and precisely calibrate electron dose rates with the effective ionic strength of the system. These results demonstrate that low-dose LP-TEM is a powerful tool for obtaining structural and kinetic properties of nanoassemblies in liquid conditions that closely resemble real experiments. We anticipate that this technique will be especially advantageous for those structures with heterogeneity or disorder that cannot be easily probed by ensemble methods and will provide important insight that will aid in the rational design of sophisticated reconfigurable nanomaterials.

Numerous mechanisms have been studied for chemical reactions to provide quantitative predictions on how atoms spatially arrange into molecules. In nanoscale colloidal systems, however, less is known about the physical rules governing their spatial organization, i.e., self-assembly, into functional materials. Here, we monitor real-time self-assembly dynamics at the single nanoparticle level, which reveal marked similarities to foundational principles of polymerization. Specifically, using the prototypical system of gold triangular nanoprisms, we show that colloidal self-assembly is analogous to polymerization in three aspects: ensemble growth statistics following models for step-growth polymerization, with nanoparticles as linkable “monomers”; bond angles determined by directional internanoparticle interactions; and product topology determined by the valency of monomeric units. Liquid-phase transmission electron microscopy imaging and theoretical modeling elucidate the nanometer-scale mechanisms for these polymer-like phenomena in nanoparticle systems. The results establish a quantitative conceptual framework for self-assembly dynamics that can aid in designing future nanoparticle-based materials.

For decades, one of the overarching objectives of self-assembly science has been to define the rules necessary to build functional, artificial materials with rich and adaptive phase behavior from the bottom-up. To this end, the computational and experimental efforts of chemists, physicists, materials scientists, and biologists alike have built a body of knowledge that spans both disciplines and length scales. Indeed, today control of self-assembly is extending even to supramolecular and molecular levels, where crystal engineering and design of porous materials are becoming exciting areas of exploration. Nevertheless, at least at the nanoscale, there are many stones yet to be turned. While recent breakthroughs in nanoparticle (NP) synthesis have amassed a vast library of nanoscale building blocks, NP-NP interactions in situ remain poorly quantified, in large part due to technical and theoretical impediments. While increasingly many applications for self-assembled architectures are being demonstrated, it remains difficult to predict-and therefore engineer-the pathways by which these structures form. Here, we describe how investigations using liquid-phase transmission electron microscopy (TEM) have begun to play a role in pursuing some of these long-standing questions of fundamental and far-reaching interest. Liquid-phase TEM is unique in its ability to resolve the motions and trajectories of single NPs in solution, making it a powerful tool for studying the dynamics of NP self-assembly. Since 2012, liquid-phase TEM has been used to investigate the self-assembly behavior of a variety of simple, metallic NPs. In this Account, however, we focus on our work with anisotropic NPs, which we show to have very different self-assembly behavior, and especially on how analysis methods we and others in the field are developing can be used to convert their motions and trajectories revealed by liquid-phase TEM into quantitative understanding of underlying interactions and dynamics. In general, liquid-phase TEM studies may help bridge enduring gaps in the understanding and control of self-assembly at the nanoscale. For one, quantification of NP-NP interactions and self-assembly dynamics will inform both computational and statistical mechanical models used to describe nanoscale phenomena. Such understanding will also lay the groundwork for establishing new and generalizable thermodynamic and kinetic design rules for NP self-assembly. Synergies with NP synthesis will enable investigations of building blocks with novel, perhaps even evolving or active behavior. Moreover, in the long run, we foresee the possibility of applying the guidelines and models of fundamental nanoscale interactions which are uncovered under liquid-phase TEM to biological and biomimetic systems at similar dimensions.