Revealing Reactions between the Electron Beam and Nanoparticle Capping Ligands with Correlative Fluorescence and Liquid-Phase Electron Microscopy

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

Oriented Assembly of Lead Halide Perovskite Nanocrystals

Cesium lead halide nanostructures have highly tunable optical and optoelectronic properties. Establishing precise control in forming perovskite single-crystal nanostructures is key to unlocking the full potential of these materials. However, studying the growth kinetics of colloidal cesium lead halides is challenging due to their sensitivity to light, electron beam, and environmental factors like humidity. In this study, in situ observations of CsPbBr3-particle dynamics were made possible through extremely low dose liquid cell transmission electron microscopy, showing that oriented attachment is the dominant pathway for the growth of single-crystal CsPbBr3 architectures from primary nanocubes. In addition, oriented assembly and fusion of ligand-stabilized cubic CsPbBr3 nanocrystals are promoted by electron beam irradiation or introduction of polar additives that both induce partial desorption of the original ligands and polarize the nanocube surfaces. These findings advance our understanding of cesium lead halide growth mechanisms, aiding the controlled synthesis of other perovskite nanostructures.

Direct Imaging of "Patch-Clasping" and Relaxation in Robust and Flexible Nanoparticle Assemblies

Polymer patching on inorganic nanoparticles (NPs) enables multifunctionality and directed self-assembly into nonclosely packed optical and mechanical metamaterials. However, experimental demonstration of such assemblies has been scant due to challenges in leveraging patch-induced NP-NP attractions and understanding NP self-assembly dynamics. Here we use low-dose liquid-phase transmission electron microscopy to visualize the dynamic behaviors of tip-patched triangular nanoprisms upon patch-clasping, where polymer patches interpenetrate to form cohesive bonds that connect NPs. Notably, these bonds are longitudinally robust but rotationally flexible. Patch-clasping is found to allow highly selective tip-tip assembly, interconversion between dimeric bowtie and sawtooth configurations, and collective structural relaxation of NP networks. The integration of single particle tracking, polymer physics theory, and molecular dynamics simulation reveals the macromolecular origin of patch-clasping-induced NP dynamics. Our experiment-computation integration can aid the design of stimuli-responsive nanomaterials, such as topological metamaterials for chiral sensors, waveguides, and nanoantennas.

Confinement Effects on the Structure of Entropy-Induced Supercrystals

Depletion-induced self-assembly is routinely used to separate plasmonic nanoparticles (NPs) of different shapes, but less often for its ability to create supercrystals (SCs) in suspension. Therefore, these plasmonic assemblies have not yet reached a high level of maturity and their in-depth characterization by a combination of in situ techniques is still very much needed. In this work, gold triangles (AuNTs) and silver nanorods (AgNRs) are assembled by depletion-induced self-assembly. Small Angle X-ray Scattering (SAXS) and scanning electron microscopy (SEM) analysis shows that the AuNTs and AgNRs form 3D and 2D hexagonal lattices in bulk, respectively. The colloidal crystals are also imaged by in situ Liquid-Cell Transmission Electron Microscopy. Under confinement, the affinity of the NPs for the liquid cell windows reduces their ability to stack perpendicularly to the membrane and lead to SCs with a lower dimensionality than their bulk counterparts. Moreover, extended beam irradiation leads to disassembly of the lattices, which is well described by a model accounting for the desorption kinetics highlighting the key role of the NP-membrane interaction in the structural properties of SCs in the liquid-cell. The results shed light on the reconfigurability of NP superlattices obtained by depletion-induced self-assembly, which can rearrange under confinement.

Unraveling chemical processes during nanoparticle synthesis with liquid phase electron microscopy and correlative techniques

Liquid phase transmission electron microscopy (LPTEM) has enabled unprecedented direct real time imaging of physicochemical processes during solution phase synthesis of metallic nanoparticles. LPTEM primarily provides images of nanometer scale, and sometimes atomic scale, metal nanoparticle crystallization processes, but provides little chemical information about organic surface ligands, metal-ligand complexes and reaction intermediates, and redox reactions. Likewise, complex electron beam-solvent interactions during LPTEM make it challenging to pinpoint the chemical processes, some involving exotic highly reactive radicals, impacting nanoparticle formation. Pairing LPTEM with correlative solution synthesis, ex situ chemical analysis, and theoretical modeling represents a powerful approach to gain a holistic understanding of the chemical processes involved in nanoparticle synthesis. In this feature article, we review recent work by our lab and others that has focused on elucidating chemical processes during nanoparticle synthesis using LPTEM and correlative chemical characterization and modeling, including mass and optical spectrometry, fluorescence microscopy, solution chemistry, and reaction kinetic modeling. In particular, we show how these approaches enable investigating redox chemistry during LPTEM, polymeric and organic capping ligands, metal deposition mechanisms on plasmonic nanoparticles, metal clusters and complexes, and multimetallic nanoparticle formation. Future avenues of research are discussed, including moving beyond electron beam induced nanoparticle formation by using light and thermal stimuli during LPTEM. We discuss prospects for real time LPTEM imaging and online chemical analysis of reaction intermediates using microfluidic flow reactors.

Liquid-Phase Transmission Electron Microscopy for Reliable In Situ Imaging of Nanomaterials

Liquid-phase transmission electron microscopy (LPTEM) is a powerful in situ visualization technique for directly characterizing nanomaterials in the liquid state. Despite its successful application in many fields, several challenges remain in achieving more accurate and reliable observations. We present LPTEM in chemical and biological applications, including studies for the morphological transformation and dynamics of nanoparticles, battery systems, catalysis, biomolecules, and organic systems. We describe the possible interactions and effects of the electron beam on specimens during observation and present sample-specific approaches to mitigate and control these electron-beam effects. We provide recent advances in achieving atomic-level resolution for liquid-phase investigation of structures anddynamics. Moreover, we discuss the development of liquid cell platforms and the introduction of machine-learning data processing for quantitative and objective LPTEM analysis.