Diffusion-Dependent Nanoparticle Assembly in Thin Films of Supramolecular Nanocomposites: Effects of Particle Size and Supramolecular Morphology

Well-Ordered Nanoparticles/Block Copolymer Nanosheets with a Controllable Location of Nanoparticles

Precisely controlling the spatial distributions and arrangements of metal nanoparticles (NPs) into block copolymers is of great importance for fabricating novel nanomaterials with the desired optical and electronic properties. Herein, we develop a simple yet versatile strategy to prepare organic/inorganic nanosheets formed by the coassembly of polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP) and PS tethered gold nanoparticles (AuNPs@PS) within emulsion droplets. The arrangement of the AuNPs@PS building blocks within the block copolymers (BCP)/AuNPs nanosheets can be adjusted by tuning the effective size ratio (λeff), which can be controlled by the core diameter of the AuNPs and the molecular weight of the PS. Furthermore, the content of the AuNPs is also another essential parameter to manipulate the structures of the nanosheets with the specific λeff. Thus, the BCP/AuNPs hybrid nanosheets with controllable distributions and arrangements of the AuNPs were successfully prepared via tuning of λeff and the content of AuNPs. This study provides a facile way to fabricate well-ordered hybrid nanosheets.

Functional composites by programming entropy-driven nanosheet growth

Nanomaterials must be systematically designed to be technologically viable1-5. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration6,7. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible8-13. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing14-17. Here, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm-2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology3,4,18. This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly.

Using Machine Learning to Predict and Understand Complex Self-Assembly Behaviors of a Multicomponent Nanocomposite

Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships between parameters, so they are suited to complex systems without a solid theoretical foundation. Here, a dataset of 595 microscopy images of nanocomposite thin films is used to train a series of ML models. Correlations between the input and output parameters are examined, providing new insights into the system. Finally, the most successful ML model is used to predict the structures of new nanocomposite compositions. The results confirm that ML techniques can be used to improve the efficiency of nanocomposite device design. More broadly, the current study suggests some of the advantages and challenges associated with applying ML to complex systems.

Thermal Percolation in Well-Defined Nanocomposite Thin Films

Thermal percolation in polymer nanocomposites─the rapid increase in thermal transport due to the formation of networks among fillers─is the subject of great interest in thermal management ranging from general utility in multifunctional nanocomposites to high-conductivity applications such as thermal interface materials. However, It remains a challenging subject encompassing both experimental and modeling hurdles. Successful reports of thermal percolation are exclusively found in high-aspect-ratio, conductive fillers such as graphene, albeit at filler loadings significantly higher than the electrical percolation threshold. This anomaly was attributed to the lower filler-matrix thermal conductivity contrast ratio k_f/k_m ∼10⁴ compared to electrical conductivity ∼10¹²-10¹⁶. In a randomly dispersed composite, the effect of a low contrast ratio is further accentuated by uncertainties in the morphology of the percolating network and presence of other phases such as disconnected aggregates and colloidal dispersions. Thus, the general properties of percolating networks are convoluted as they lack a defined structure. In contrast, a prototypical system with controllable nanofiller placement enables the elucidation of structure-property relations such as filler size, loading, and assembly. Using self-assembled nanocomposites with a controlled 1,2,3-dimension nanoparticle (NP) arrangement, we demonstrate that thermal percolation can be achieved in spite of using spherical, nonconductive fillers (k_f/k_m ∼60) at a low volume fraction (9 vol %). We observe that the effects of volume fraction, interfacial thermal resistance, and filler conductivity on thermal conductivity depart from effective medium approximations. Most notably, contrast ratio plays a minor role in thermal percolation above k_f/k_m ∼60─a common range for semiconducting nanoparticles/polymer ratios. Our findings bring new perspectives and insights to thermal percolation in nanocomposites, where the limits in contrast ratio, interfacial thermal conductance, and filler size are established.

Orbital Angular Momentum from Self-Assembled Concentric Nanoparticle Rings

Ring-shaped nanostructures can focus, filter, and manipulate electromagnetic waves, but are challenging to incorporate into devices using standard nanofabrication techniques. Directed self-assembly (DSA) of block copolymers (BCPs) on lithographically patterned templates has successfully been used to fabricate concentric rings and spirals as etching masks. However, this method is limited by BCP phase behavior and material selection. Here, a straightforward approach to generate ring-shaped nanoparticle assemblies in thin films of supramolecular nanocomposites is demonstrated. DSA is used to guide the formation of concentric rings with radii spanning 150-1150 nm and ring widths spanning 30-60 nm. When plasmonic nanoparticles are used, ring nanodevice arrays can be fabricated in one step, and the completed devices produce high-quality orbital angular momentum (OAM). Nanocomposite DSA simplifies and streamlines nanofabrication by producing metal structures without etching or deposition steps; it also introduces interparticle coupling as a new design axis. Detailed analysis of the nanoparticle ring assemblies confirms that the supramolecular system self-regulates the spatial distribution of its components, and thus exhibits a degree of flexibility absent in DSA of BCPs alone, where structures are determined by polymer-pattern incommensurability. The present studies also provide guidelines for developing self-regulating DSA as an alternative to incommensurability-driven methods.

Distinctive Dielectric Permittivity of Hierarchical Nanostructures with Ordered Nanoparticle Networks Self-Assembled from AB-g-NP/AC Block Copolymer Mixtures

Directing nanoparticles into ordered organization in polymer matrix to improve macroscopic properties of nanocomposites remains a challenge. Herein, by means of theoretical simulations, we show the high permittivity of hybrid nanostructures designed with mixtures of AB block copolymer-grafted nanoparticles and lamella-forming AC diblock copolymers. The grafted nanoparticles self-assemble into parallel stripes or highly ordered networks in the lamellae of the AC diblock copolymers. The ordered nanoparticle networks, including honeycomb-like and kagomé networks, provide bending and conductive pathways for concentrating electric fields, which results in the improvement of the permittivity. We envisage that this strategy will open a gateway to prepare hierarchically ordered functional nanocomposites with distinctive dielectric properties.

Ordered polymer composite materials: challenges and opportunities

Polymer nanocomposites containing nanoscale fillers are an important class of materials due to their ability to access a wide variety of properties as a function of their composition. In order to take full advantage of these properties, it is critical to control the distribution of nanofillers within the parent polymer matrix, as this structural organization affects how the two constituent components interact with one another. In particular, new methods for generating ordered arrays of nanofillers represent a key underexplored research area, as emergent properties arising from nanoscale ordering can be used to introduce novel functionality currently inaccessible in random composites. The knowledge gained from developing such methods will provide important insight into the thermodynamics and kinetics associated with nanomaterial and polymer assembly. These insights will not only benefit researchers working on new composite materials, but will also deepen our understanding of soft matter systems in general. In this review, we summarize contemporary research efforts in manipulating nanofiller organization in polymer nanocomposites and highlight future challenges and opportunities for constructing ordered nanocomposite materials.

Kinetic Monitoring of Block Copolymer Self-Assembly Using In Situ Spectroscopic Ellipsometry

Understanding the kinetic pathways of self-assembly in block copolymers (BCPs) has been a long-standing challenge, mostly due to limitations of in situ monitoring techniques. Here, we demonstrate an approach that uses optical birefringence, determined by spectroscopic ellipsometry (SE), as a measure of domain formation in cylinder- and lamellae-forming BCP films. The rapid experimental acquisition time in SE (ca. 1 sec) enables monitoring of the assembly/disassembly kinetics of BCP films during solvent-vapor annealing (SVA). We demonstrate that upon SVA, BCP films form ordered domains that are stable in the swollen state, but disorder upon rapid drying. Surprisingly, the disassembly during drying strongly depends on the duration of solvent exposure in the swollen state, explaining previous observations of loss of order in SVA processes. SE thus allows for decoupling of BCP self-assembly and disordering that occurs during solvent annealing and solvent evaporation, which is difficult to probe using other, slower techniques.

NMR Studies of Block Copolymer-Based Supramolecules in Solution

Hierarchical assemblies from block copolymer (BCP)-based supramolecules have shown immense potential as programmable materials owing to their versatility for incorporating functional molecules and provide access to arrays of hierarchical structures. However, there remains a knowledge gap on the formation of the supramolecule in solution. Here, we applied NMR techniques to investigate the solution-phase behavior of the most studied supramolecular systems, polystyrene-block-poly(4-vinylpyridine)(3-pentadecylphenol) (PS-b-P4VP(PDP)_r). The results show that the supramolecule likely adopts a coil-comb conformation, despite the small molecule's (PDP) rapid exchange between the bonded and free states. The exchange rate (>10⁴ s^-1) exceeds the NMR time scale at the frequency of interest. The supramolecules form under dilute conditions (∼2 vol %) and are attributed to the enthalpic gain of the hydrogen bonding between the PDP and 4VP. As the solute concentration increases (>10 vol %), the supramolecule forms micelle-like aggregates with PDP accumulated within the comb-block's pervaded volume based on analysis of the apparent molecular weight, viscosity, and chain dynamics. This work sheds light on the long-standing question regarding the evolution of the constituents in the BCP-based supramolecule in solution and provides valuable guidance toward their solution-based processing and morphological control.