Observation of Liquid-Liquid-Phase Separation and Vesicle Spreading during Supported Bilayer Formation via Liquid-Phase Transmission Electron Microscopy

Observing the Dynamics of an Electrochemically Driven Active Material with Liquid Electron Microscopy

Electrochemical liquid electron microscopy has revolutionized our understanding of nanomaterial dynamics by allowing for direct observation of their electrochemical production. This technique, primarily applied to inorganic materials, is now being used to explore the self-assembly dynamics of active molecular materials. Our study examines these dynamics across various scales, from the nanoscale behavior of individual fibers to the micrometer-scale hierarchical evolution of fiber clusters. To isolate the influences of the electron beam and electrical potential on material behavior, we conducted thorough beam-sample interaction analyses. Our findings reveal that the dynamics of these active materials at the nanoscale are shaped by their proximity to the electrode and the applied electrical current. By integrating electron microscopy observations with reaction-diffusion simulations, we uncover that local structures and their formation history play a crucial role in determining assembly rates. This suggests that the emergence of nonequilibrium structures can locally accelerate further structural development, offering insights into the behavior of active materials under electrochemical conditions.

Liquid-liquid phase separation as triggering factor of fibril formation

Liquid-liquid phase separation (LLPS) refers to the phenomenon, where a homogeneous solution spontaneously undergoes a transition into two or more immiscible phases. Through transient weak multivalent macromolecular interactions, a homogeneous solution can spontaneously separate into two phases: one rich in biomolecules and the other poor in biomolecules. Phase separation is believed to serve as the physicochemical foundation for the formation of membrane-less organelles (MLOs) and bio-molecular condensates within cells. Moreover, numerous biological processes depend on LLPS, such as transcription, immunological response, chromatin architecture, DNA damage response, stress granule formation, viral infection, etc. Abnormalities in phase separation can lead to diseases, such as cancer, neurodegeneration, and metabolic disorders. LLPS is regulated by various factors, such as concentration of molecules undergoing LLPS, salt concentration, pH, temperature, post-translational modifications, and molecular chaperones. Recent research on LLPS of biomolecules has progressed rapidly and led to the development of databases containing information pertaining to various aspects of the biomolecule separation analysis. However, more comprehensive research is still required to fully comprehend the specific molecular mechanisms and biological effects of LLPS.

Correlating electrochemical stimulus to structural change in liquid electron microscopy videos using the structural dissimilarity metric

In-situ liquid cell transmission electron microscopy (LCTEM) with electrical biasing capabilities has emerged as an invaluable tool for directly imaging electrode processes with high temporal and spatial resolution. However, accurately quantifying structural changes that occur on the electrode and subsequently correlating them to the applied stimulus remains challenging. Here, we present structural dissimilarity (DSSIM) analysis as segmentation-free video processing algorithm for locally detecting and quantifying structural change occurring in LCTEM videos. In this study, DSSIM analysis is applied to two in-situ LCTEM videos to demonstrate how to implement this algorithm and interpret the results. We show DSSIM analysis can be used as a visualization tool for qualitative data analysis by highlighting structural changes which are easily missed when viewing the raw data. Furthermore, we demonstrate how DSSIM analysis can serve as a quantitative metric and efficiently convert 3-dimensional microscopy videos to 1-dimenional plots which makes it easy to interpret and compare events occurring at different timepoints in a video. In the analyses presented here, DSSIM is used to directly correlate the magnitude and temporal scale of structural change to the features of the applied electrical bias. ImageJ, Python, and MATLAB programs, including a user-friendly interface and accompanying documentation, are published alongside this manuscript to make DSSIM analysis easily accessible to the scientific community.

Liquid-liquid phase separation induced auto-confinement

Confinement allows macromolecules and biomacromolecules to attain arrangements typically unachievable through conventional self-assembly processes. In the field of block copolymers, confinement has been achieved by preparing thin films and controlled solvent evaporation through the use of emulsions. A significant advantage of the confinement-driven self-assembly process is its ability to enable block copolymers to form particles with complex internal morphologies, which would otherwise be inaccessible. Here, we show that liquid-liquid phase separation (LLPS) can induce confinement during the self-assembly of a model block copolymer system. Since this confinement is driven by the block copolymers' tendency to undergo LLPS, we define this confinement type as auto-confinement. This study adds to the growing understanding of how LLPS influences block copolymer self-assembly and provides a new method to achieve confinement driven self-assembly.

Revealing microscopic dynamics: in situ liquid-phase TEM for live observations of soft materials and quantitative analysis via deep learning

In various domains spanning materials synthesis, chemical catalysis, life sciences, and energy materials, in situ transmission electron microscopy (TEM) methods exert a profound influence. These methodologies enable the real-time observation and manipulation of gas-phase and liquid-phase reactions at the nanoscale, facilitating the exploration of pivotal reaction mechanisms. Fundamental research areas like crystal nucleation, growth, etching, and self-assembly have greatly benefited from these techniques. Additionally, their applications extend across diverse fields such as catalysis, batteries, bioimaging, and drug delivery kinetics. However, the intricate nature of 'soft matter' presents a challenge due to the unique molecular properties and dynamic behavior of these substances that remain insufficiently understood. Investigating soft matter within in situ liquid-phase TEM settings demands further exploration and advancement compared to other research domains. This research harnesses the potential of in situ liquid-phase TEM technology while integrating deep learning methodologies to comprehensively analyze the quantitative aspects of soft matter dynamics. This study centers on diverse phenomena, encompassing surfactant molecule nucleation, block copolymer behavior, confinement-driven self-assembly, and drying processes. Furthermore, deep learning techniques are employed to precisely analyze Ostwald ripening and digestive ripening dynamics. The outcomes of this study not only deepen the understanding of soft matter at its fundamental level but also serve as a pivotal foundation for developing innovative functional materials and cutting-edge devices.

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.

Exploration of Organic Nanomaterials with Liquid-Phase Transmission Electron Microscopy

ConspectusOrganic, soft materials with solution-phase nanoscale structures, such as emulsions, hydrogels, and thermally responsive materials, are inherently difficult to directly image via dry state and cryogenic-transmission electron microscopy (TEM). Therefore, we lack a routine microscopy method with sufficient resolution that can, in tandem with scattering techniques, probe the morphology and dynamics of these and many related systems. These challenges motivate liquid cell (LC) TEM method development, aimed at making the technique generally available and routine. To date, the field has been and continues to be dominantly focused on analyzing solution-phase inorganic materials. These mostly metallic nanoparticles have been studied at electron fluxes that can allow for high-resolution imaging, in the range of hundreds to thousands of e- Å-2 s-1. Despite excellent contrast, in these cases, one often contends with knock-on damage, direct radiolysis, and sensitization of the solvent by virtue of enhanced secondary electron production by the impinging electron beam. With an interest in soft materials, we face both related and distinct challenges, especially in achieving a high-enough contrast within solvated liquid cells. Additionally, we must be aware of artifacts associated with high-flux imaging conditions in terms of direct radiolysis of the solvent and the sensitive materials themselves. Regardless, with care, it has become possible to gain real insight into both static and dynamic organic nanomaterials in solution. This is due, in large part, to key advances that have been made, including improved sample preparation protocols, image capture technologies, and image analysis, which have allowed LCTEM to have utility. To enable solvated soft matter characterization by LCTEM, a generalizable multimodal workflow was developed by leveraging both experimental and theoretical precedents from across the LCTEM field and adjacent works concerned with solution radiolysis and nanoparticle tracking analyses. This workflow consists of (1) modeling electron beam-solvent interactions, (2) studying electron beam-sample interactions via LCTEM coupled with post-mortem analysis, (3) the construction of "damage plots" displaying sample integrity under varied imaging and sample conditions, (4) optimized LCTEM imaging, (5) image processing, and (6) correlative analysis via X-ray or light scattering. In this Account, we present this outlook and the challenges we continue to overcome in the direct imaging of dynamic solvated nanoscale soft materials.

Ouzo Effect Examined at the Nanoscale via Direct Observation of Droplet Nucleation and Morphology

Herein, we present the direct observation via liquid-phase transmission electron microscopy (LPTEM) of the nucleation and growth pathways of structures formed by the so-called "ouzo effect", which is a classic example of surfactant-free, spontaneous emulsification. Such liquid-liquid phase separation occurs in ternary systems with an appropriate cosolvent such that the addition of the third component extracts the cosolvent and makes the other component insoluble. Such droplets are homogeneously sized, stable, and require minimal energy to disperse compared to conventional emulsification methods. Thus, ouzo precipitation processes are an attractive, straightforward, and energy-efficient technique for preparing dispersions, especially those made on an industrial scale. While this process and the resulting emulsions have been studied by numerous indirect techniques (e.g., X-ray and light scattering), direct observation of such structures and their formation at the nanoscale has remained elusive. Here, we employed the nascent technique of LPTEM to simultaneously evaluate droplet growth and nanostructure. Observation of such emulsification and its rate dependence is a promising indication that similar LPTEM methodologies may be used to investigate emulsion formation and kinetics.

Controlling self-assembling co-polymer coatings of hydrophilic polysaccharide substrates via co-polymer block length ratio

HYPOTHESIS

The degree of polymerization of amphiphilic di-block co-polymers, which can be varied with ease in computer simulations, provides a means to control self-assembling di-block co-polymer coatings on hydrophilic substrates.

SIMULATIONS

We examine self-assembly of linear amphiphilic di-block co-polymers on hydrophilic surface via dissipative particle dynamics simulations. The system models a glucose based polysaccharide surface on which random co-polymers of styrene and n-butyl acrylate, as the hydrophobic block, and starch, as the hydrophilic block, forms a film. Such setups are common in e.g. hygiene, pharmaceutical, and paper product applications.

FINDINGS

Variation of the block length ratio (35 monomers in total) reveals that all examined compositions readily coat the substrate. However, strongly asymmetric block co-polymers with short hydrophobic segments are best in wetting the surface, whereas approximately symmetric composition leads to most stable films with highest internal order and well-defined internal stratification. At intermediate asymmetries, isolated hydrophobic domains form. We map the sensitivity and stability of the assembly response for a large variety of interaction parameters. The reported response persists for a wide polymer mixing interactions range, providing general means to tune surface coating films and their internal structure, including compartmentalization.

Liquid–Liquid Phase Separation of Biomacromolecules and Its Roles in Metabolic Diseases

Liquid–liquid phase separation (LLPS) compartmentalizes and concentrates biomacromolecules into liquid-like condensates, which underlies membraneless organelles (MLOs) formation in eukaryotic cells. With increasing evidence of the LLPS concept and methods, this phenomenon as a novel principle accounts for explaining the precise spatial and temporal regulation of cellular functions. Moreover, the phenomenon that LLPS tends to concentrate proteins is often accompanied by several abnormal signals for human diseases. It is reported that multiple metabolic diseases are strongly associated with the deposition of insoluble proteinaceous aggregating termed amyloids. At present, recent studies have observed the roles of LLPS in several metabolic diseases, including type 2 diabetes mellitus (T2DM), Alzheimer’s disease (AD), and metabolic bone diseases (MBDs). This review aims to expound on the current concept and methods of LLPS and summarize its vital roles in T2DM, AD, and MBDs, uncover novel mechanisms of these metabolic diseases, and thus provide powerful potential therapeutic strategies and targets for ameliorating these metabolic diseases.