Multilaminate Energy Storage Films from Entropy-Driven Self-Assembled Supramolecular Nanocomposites.

Composite materials comprising polymers and inorganic nanoparticles (NPs) are promising for energy storage applications, though challenges in controlling NP dispersion often result in performance bottlenecks. Realizing nanocomposites with controlled NP locations and distributions within polymer microdomains is highly desirable for improving energy storage capabilities but is a persistent challenge, impeding the in-depth understanding of the structure-performance relationship. In this study, a facile entropy-driven self-assembly approach is employed to fabricate block copolymer-based supramolecular nanocomposite films with highly ordered lamellar structures, which are then used in electrostatic film capacitors. The oriented interfacial barriers and well-distributed inorganic NPs within the self-assembled multilaminate nanocomposites effectively suppress leakage current and mitigate the risk of breakdown, showing superior dielectric strength compared to their disordered counterparts. Consequently, the lamellar nanocomposite films with optimized composition exhibit high energy efficiency (>90% at 650 MV m-1), along with remarkable energy density and power density. Moreover, finite element simulations and statistical modeling have provided theoretical insights into the impact of the lamellar structure on electrical conduction, electric field distribution, and electrical tree propagation. This work marks a significant advancement in the design of organic-inorganic hybrids for energy storage, establishing a well-defined correlation between microstructure and performance.

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.

Observation of an Orientational Glass in a Superlattice of Elliptically-Faceted CdSe Nanocrystals.

Extensive prior work has shown that colloidal inorganic nanocrystals coated with organic ligand shells can behave as artificial atoms and, as such, form superlattices with different crystal structures and packing densities. Although ordered superlattices present a high degree of long-range positional order, the relative crystallographic orientation of the inorganic nanocrystals with respect to each other tends to be random. Recent works have shown that superlattices can achieve orientational alignment through combinations of nanocrystal faceting and ligand modification, as well as selective metal particle attachment to particular facets. These studies have focused on the assembly of high-symmetry nanocrystals, such as cubes and cuboctahedra. Here, we study the assembly of elliptically faceted CdSe/CdS core/shell nanocrystals with one distinctive crystallographic orientation along the major elliptical axis. We show that the nanocrystals form an unexpectedly well-ordered translational superlattice, with a degree of order comparable to that achieved with higher-symmetry nanocrystals. Additionally, we show that, due to the particles' faceted shape, the superlattice is characterized by an orientational glass phase in which only certain orientations are possible due to entropically frustrated crystallization. In this phase, the nanocrystals do not exhibit a local orientational ordering but rather have distinct orientations that emerge at different locations within the same domain. The distinct orientations are a result of a facet-to-facet lock-in mechanism that occurs during the self-assembly process. These facet-to-facet alignments force the nanocrystals to tilt on different lattice planes forming different projections that we termed apparent polydispersity. Our experimental realization of an orientational glass phase for multifaceted semiconducting nanocrystals can be used to investigate how this phase is formed and how it can be utilized for potential optical, electrical, and thermal transport applications.

Backbone-Photodegradable Polymers by Incorporating Acylsilane Monomers via Ring-Opening Metathesis Polymerization.

Materials capable of degradation upon exposure to light hold promise in a diverse range of applications including biomedical devices and smart coatings. Despite the rapid access to macromolecules with diverse compositions and architectures enabled by ring-opening metathesis polymerization (ROMP), a general strategy to introduce facile photodegradability into these polymers is lacking. Here, we report copolymers synthesized via ROMP that can be degraded by cleaving the backbone in both solution and solid states under irradiation with a 52 W, 390 nm Kessil LED to generate heterotelechelic low-molecular-weight fragments. To the best of our knowledge, this work represents the first instance of the incorporation of acylsilanes into a polymer backbone. Mechanistic investigation of the degradation process supports the intermediacy of an α-siloxy carbene, formed via a 1,2-photo Brook rearrangement, which undergoes insertion into water followed by cleavage of the resulting hemiacetal.

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.

Diversifying Composition Leads to Hierarchical Composites with Design Flexibility and Structural Fidelity.

Although significant progress has been made in the self-assembly of nanostructures, present successes heavily rely on precision in building block design, composition, and pair interactions. These requirements fundamentally limit our ability to synthesize macroscopic materials where the likelihood of impurity inclusion escalates and, more importantly, to access molecular-to-nanoscopic-to-microscopic-to-macroscopic hierarchies, since the types and compositions of building blocks vary at each stage. Inspired by biological blends and high-entropy alloys, we hypothesize that diversifying the blend's composition can overcome these limitations. Increasing the number of components increases mixing entropy, leading to the dispersion of different components and, as a result, enhances interphase miscibility, weakens the dependence on specific pair interactions, and enables long-range cooperativity. This hypothesis is validated in complex blends containing small molecules, block copolymer-based supramolecules, and nanoparticles/colloidal particles. Hierarchically structured composites can be obtained with formulation flexibility in the filler selection and blend composition. It is worth noting that, by adding small molecules, we can solve the size constraint that plagues traditional block copolymer/nanoparticle blends. Detailed characterization and simulation further confirm that each component is distributed to locally mediate unfavorable interactions, cooperatively mitigate composition fluctuations, and retain structural fidelity. Furthermore, the blends have sufficient mobility to access tunable microstructures without compromising the order of the nanostructure. Besides establishing a kinetically viable pathway to release current constraints in the composite design and to navigate uncertainties during structure formation over multiple length scales, the present study demonstrates that entropy-driven behaviors can be realized in systems beyond high-entropy alloys despite inherent differences between metal alloys and organic/inorganic hybrids.