Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors.

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes ("tadpoles" and "boomerangs"), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

Beyond nothingness in the formation and functional relevance of voids in polymer films.

Voids-the nothingness-broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour. Using polyamide membranes for molecular separation as a representative system, three-dimensional electron tomography at nanometre resolution reveals nanovoid formation from coalescence of oligomers, supported by coarse-grained molecular dynamics simulations. Void analysis provides otherwise inaccessible inputs for accurate fittings of methanol permeance for polyamide membranes. Three-dimensional structural graphs accounting for the tortuous nanovoids within, measure higher apparent moduli with polyamide membranes of higher graph rigidity. Our study elucidates the significance of nanovoids beyond the nothingness, impacting the synthesis‒morphology‒function relationships of complex nanomaterials.

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.

Electron Microscopy Studies of Soft Nanomaterials.

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. The even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes.

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure-function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1-3 GPa, which is consistent with crystalline proteins. Overall, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Symmetry-breaking in patch formation on triangular gold nanoparticles by asymmetric polymer grafting.

Synthesizing patchy particles with predictive control over patch size, shape, placement and number has been highly sought-after for nanoparticle assembly research, but is fraught with challenges. Here we show that polymers can be designed to selectively adsorb onto nanoparticle surfaces already partially coated by other chains to drive the formation of patchy nanoparticles with broken symmetry. In our model system of triangular gold nanoparticles and polystyrene-b-polyacrylic acid patch, single- and double-patch nanoparticles are produced at high yield. These asymmetric single-patch nanoparticles are shown to assemble into self-limited patch‒patch connected bowties exhibiting intriguing plasmonic properties. To unveil the mechanism of symmetry-breaking patch formation, we develop a theory that accurately predicts our experimental observations at all scales-from patch patterning on nanoparticles, to the size/shape of the patches, to the particle assemblies driven by patch‒patch interactions. Both the experimental strategy and theoretical prediction extend to nanoparticles of other shapes such as octahedra and bipyramids. Our work provides an approach to leverage polymer interactions with nanoscale curved surfaces for asymmetric grafting in nanomaterials engineering.

Chiral assemblies of pinwheel superlattices on substrates.

The unique topology and physics of chiral superlattices make their self-assembly from nanoparticles highly sought after yet challenging in regard to (meta)materials1-3. Here we show that tetrahedral gold nanoparticles can transform from a perovskite-like, low-density phase with corner-to-corner connections into pinwheel assemblies with corner-to-edge connections and denser packing. Whereas corner-sharing assemblies are achiral, pinwheel superlattices become strongly mirror asymmetric on solid substrates as demonstrated by chirality measures. Liquid-phase transmission electron microscopy and computational models show that van der Waals and electrostatic interactions between nanoparticles control thermodynamic equilibrium. Variable corner-to-edge connections among tetrahedra enable fine-tuning of chirality. The domains of the bilayer superlattices show strong chiroptical activity as identified by photon-induced near-field electron microscopy and finite-difference time-domain simulations. The simplicity and versatility of substrate-supported chiral superlattices facilitate the manufacture of metastructured coatings with unusual optical, mechanical and electronic characteristics.

Self-Assembly of Repetitive Segment and Random Segment Polymer Architectures.

Recent advances in chemical synthesis have created new methodologies for synthesizing sequence-controlled synthetic polymers, but rational design of monomer sequence for desired properties remains challenging. In this work, we synthesize periodic polymers with repetitive segments using a sequence-controlled ring-opening metathesis polymerization (ROMP) method, which draws inspiration from proteins containing repetitive sequence motifs. The repetitive segment architecture is shown to dramatically affect the self-assembly behavior of these materials. Our results show that polymers with identical repetitive sequences assemble into uniform spherical nanoparticles after thermal annealing, whereas copolymers with random placement of segments with different sequences exhibit disordered assemblies without a well-defined morphology. Overall, these results bring a new understanding to the role of periodic repetitive sequences in polymer assembly.

Seeking regularity from irregularity: unveiling the synthesis-nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning.

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis-nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning.

Biological morphogenesis has inspired many efficient strategies to diversify material structure and functionality using a fixed set of components. However, implementation of morphogenesis concepts to design soft nanomaterials is underexplored. Here, we study nanomorphogenesis in the form of the three-dimensional (3D) crumpling of polyamide membranes used for commercial molecular separation, through an unprecedented integration of electron tomography, reaction-diffusion theory, machine learning (ML), and liquid-phase atomic force microscopy. 3D tomograms show that the spatial arrangement of crumples scales with monomer concentrations in a form quantitatively consistent with a Turing instability. Membrane microenvironments quantified from the nanomorphologies of crumples are combined with the Spiegler-Kedem model to accurately predict methanol permeance. ML classifies vastly heterogeneous crumples into just four morphology groups, exhibiting distinct mechanical properties. Our work forges quantitative links between synthesis and performance in polymer thin films, which can be applicable to diverse soft nanomaterials.

Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos.

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.

Imaging how thermal capillary waves and anisotropic interfacial stiffness shape nanoparticle supracrystals.

Development of the surface morphology and shape of crystalline nanostructures governs the functionality of various materials, ranging from phonon transport to biocompatibility. However, the kinetic pathways, following which such development occurs, have been largely unexplored due to the lack of real-space imaging at single particle resolution. Here, we use colloidal nanoparticles assembling into supracrystals as a model system, and pinpoint the key role of surface fluctuation in shaping supracrystals. Utilizing liquid-phase transmission electron microscopy, we map the spatiotemporal surface profiles of supracrystals, which follow a capillary wave theory. Based on this theory, we measure otherwise elusive interfacial properties such as interfacial stiffness and mobility, the former of which demonstrates a remarkable dependence on the exposed facet of the supracrystal. The facet of lower surface energy is favored, consistent with the Wulff construction rule. Our imaging-analysis framework can be applicable to other phenomena, such as electrodeposition, nucleation, and membrane deformation.

Tip-Patched Nanoprisms from Formation of Ligand Islands.

We apply the concept of "island formation" established for planar substrates, where ligands laterally cluster as they adsorb, to preparing nanoparticles (NPs) with precisely sized surface patches. Using gold triangular nanoprisms and 2-naphthalenethiols (2-NAT) as a prototypical system, we show that the preferential adsorption of 2-NAT on the prism tips leads to formation of tip patches. The patches are rendered visible for direct transmission electron microscopy and atomic force microscopy imaging upon attaching polystyrene-b-poly(acrylic acid). Using this method, the shape of patchy prisms is varied from small lobed, big lobed, trefoil, T-shaped to a reuleaux triangle by increasing the 2-NAT-to-prism concentration ratio. This trend matches with predictions of island formation as elucidated by our self-consistent field theory modeling, from which we exclude Langmuir adsorption. The tip-patched prisms assemble into unexpected twisted dimers due to the patch-patch interactions. We expect the island formation as a generalizable strategy to make patchy NPs of various shapes for emergent assemblies and applications.

Effects of Particle Size on Mg2+ Ion Intercalation into λ-MnO2 Cathode Materials.

An emergent theme in mono- and multivalent ion batteries is to utilize nanoparticles (NPs) as electrode materials based on the phenomenological observations that their short ion diffusion length and large electrode-electrolyte interface can lead to improved ion insertion kinetics compared to their bulk counterparts. However, the understanding of how the NP size fundamentally relates to their electrochemical behaviors (e.g., charge storage mechanism, phase transition associated with ion insertion) is still primitive. Here, we employ spinel λ-MnO2 particles as a model cathode material, which have effective Mg2+ ion intercalation but with their size effect poorly understood to investigate their operating mechanism via a suite of electrochemical and structural characterizations. We prepare two differently sized samples, the small nanoscopic λ-MnO2 particles (81 ± 25 nm) and big micron-sized ones (814 ± 207 nm) via postsynthesis size-selection. Analysis of the charge storage mechanisms shows that the stored charge from Mg2+ ion intercalation dominates in both systems and is ∼10 times higher in small particles than that in the big ones. From both X-ray diffraction and atomic-resolution scanning transmission electron microscopy imaging, we reveal a fundamental difference in phase transition of the differently sized particles during Mg2+ ion intercalation: the small NPs undergo a solid-solution-like phase transition which minimizes lattice mismatch and energy penalty for accommodating new phases, whereas the big particles follow conventional multiphase transformation. We show that this pathway difference is related to the improved electrochemical performance (e.g., rate capability, cycling performance) of small particles over the big ones which provides important insights in encoding within the particle dimension, that is, the single-phase transition pathway in high-performance electrode materials for multivalent ion batteries.