Binary Superlattices Research Articles

Scalable Assembly of Crystalline Binary Nanocrystal Superparticles and Their Enhanced Magnetic and Electrochemical Properties.

Self-assembled binary nanocrystal superlattices (BNSLs) represent an important class of solid-state materials with potentially designed properties. In pursuit of widening the range of applications for binary superlattice materials, it is desirable to develop scalable assembly methods that enable high-quality BNSLs with tailored compositions, structures, and morphologies. Here, we report the gram-scale assembly of crystalline binary nanocrystal superparticles with high phase purity through an emulsion-based process. The structure of the resulting BNSL colloids can be tuned in a wide range (AB13, AlB2, MgZn2, NaCl, and CaCu5) by varying the size and/or number ratios of the two nanocrystal components. Access to large-scale, phase-pure BNSL colloids offers vast opportunities for investigating their physiochemical properties, as exemplified by AB13-type CoFe2O4-Fe3O4 binary superparticles. Our results show that CoFe2O4-Fe3O4 binary superparticles not only display enhanced magnetic coupling but also exhibit superior lithium-storage properties. The nonclosed-packed NC packing arrangements of AB13-type binary superparticles are found to play a key role in facilitating lithiation/delithiation kinetics and maintaining structural integrity during repeated cycling. Our work establishes the scalable assembly of high-quality BNSL colloids, which is beneficial for accelerating the exploration of multicomponent nanocrystal superlattices toward various applications.

Many Body Effects and Icosahedral Order in Superlattice Self-Assembly.

We elucidate how nanocrystals "bond" to form ordered structures. For that purpose we consider nanocrystal configurations consisting of regular polygons and polyhedra, which are the motifs that constitute single component and binary nanocrystal superlattices, and simulate them using united atom models. We compute the free energy and quantify many body effects, i.e., those that cannot be accounted for by pair potential (two-body) interactions, further showing that they arise from coalescing vortices of capping ligands. We find that such vortex textures exist for configurations with local coordination number ≤6. For higher coordination numbers, vortices are expelled and nanocrystals arrange in configurations with tetrahedral or icosahedral order. We provide explicit formulas for the optimal separations between nanocrystals, which correspond to the minima of the free energies. Our results quantitatively explain the structure of superlattice nanocrystals as reported in experiments and reveal how packing arguments, extended to include soft components, predict ordered nanocrystal aggregation.

Occurence of Characteristic Peaks in Phononic Multilayer Structures

In this paper the acoustic transmission properties of multilayer structures was analyzed. Were compared binary and aperiodic (Severin, Thue-Morse) superlattices, Calculations were performed using the Transfer Matrix Method (TMM) algorithm. As a superlattice environment in the simulation the water was used. The material used to construct the structure was a PNM-0.38PT piezoelectric. Multilayer types have been selected so that the total number of layers for a given generation is equal in all structures.

Ordered Surface Structuring of Spherical Colloids with Binary Nanoparticle Superlattices

Surface-patterning colloidal matter in the sub-10 nm regime generates exceptional functionality in biology and photonic and electronic materials. Techniques of artificially generating functional patterns in the small nanoscale advanced in a fascinating manner in the last several years. However, they remain often restricted to planar and noncolloidal substrates. Patterning colloidal matter in solution via bottom-up assembly of smaller subunits on larger core particles is highly challenging because it is necessary to force the subunits onto randomly moving objects. Consequently, the non-equilibrium conditions present during nanoparticle self-assembly are difficult to control to eventually achieve the desired material structures. Here, we describe the formation of surface patterns with intrinsic periodic repeats of 8.9 ± 0.9 nm and less on hard, amorphous colloidal core particles by assembling binary nanoparticle superlattices on the curved particle surface. The colloidal environment is preserved during the entire bottom-up crystallization of variable building blocks (here, monodispersed 5 nm Au and 2.4 nm Pd nanoparticles (NPs) and 230 nm SiO2 core particles) into AB13-like, binary, and isotropic superlattice domains on the amorphous cores. The three-dimensional, bottom-up assembly technique is a new tool for patterning colloidal matter in the sub-10 nm surface regime for gaining access to multicomponent metamaterials for bionanoscience, photonics, and electronics.

Necessary conditions for superior thermoelectric power of Si/Au artificial superlattice thin-film

The Si–Ge–Au ternary artificial superlattice thin-films showed superior thermoelectric power with low reproducibility. Superior thermoelectric power was only generated, when nanocrystals existed. Therefore, the origin of superior thermoelectric power was considered to be the quantum size effect of nanocrystals. However, even with the presence of nanocrystals, superior thermoelectric power was often not generated. In order to investigate the generation conditions of superior thermoelectric power in more detail, the samples were simplified to Si–Au binary artificial superlattice samples. Furthermore, annealings were carried out under conditions where nanocrystals were likely to be formed. From the results of Raman scattering spectroscopy and X-ray diffraction (XRD) analysis, the diameter of nanocrystals and the spacing between nanocrystals were calculated with an isotropic three-dimensional mosaic model. It was found that superior thermoelectric power was generated only when the diameter of nanocrystals was 11 nm or less and the spacing between nanocrystals was 3 nm or less.

Machine learning properties of binary wurtzite superlattices

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present contribution, we show that fast and accurate predictions of a wide range of properties of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. While the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.

Assembly of three-dimensional binary superlattices from multi-flavored particles.

Binary superlattices constructed from nano- or micron-sized colloidal particles have a wide variety of applications, including the design of advanced materials. Self-assembly of such crystals from their constituent colloids can be achieved in practice by, among other means, the functionalization of colloid surfaces with single-stranded DNA sequences. However, when driven by DNA, this assembly is traditionally premised on the pairwise interaction between a single DNA sequence and its complement, and often relies on particle size asymmetry to entropically control the crystalline arrangement of its constituents. The recently proposed "multi-flavoring" motif for DNA functionalization, wherein multiple distinct strands of DNA are grafted in different ratios to different colloids, can be used to experimentally realize a binary mixture in which all pairwise interactions are independently controllable. In this work, we use various computational methods, including molecular dynamics and Wang-Landau Monte Carlo simulations, to study a multi-flavored binary system of micron-sized DNA-functionalized particles modeled implicitly by Fermi-Jagla pairwise interactions. We show how self-assembly of such systems can be controlled in a purely enthalpic manner, and by tuning only the interactions between like particles, demonstrate assembly into various morphologies. Although polymorphism is present over a wide range of pairwise interaction strengths, we show that careful selection of interactions can lead to the generation of pure compositionally ordered crystals. Additionally, we show how the crystal composition changes with the like-pair interaction strengths, and how the solution stoichiometry affects the assembled structures.

Free-Standing Metal Oxide Nanoparticle Superlattices Constructed with Engineered Protein Containers Show in Crystallo Catalytic Activity.

The construction of defined nanostructured catalysts is challenging. In previous work, we established a strategy to assemble binary nanoparticle superlattices with oppositely charged protein containers as building blocks. Here, we show that these free-standing nanoparticle superlattices are catalytically active. The metal oxide nanoparticles inside the protein scaffold are accessible for a range of substrates and show oxidase-like and peroxidase-like activity. The stable superlattices can be reused for several reaction cycles. In contrast to bulk nanoparticle-based catalysts, which are prone to aggregation and difficult to characterize, nanoparticle superlattices based on engineered protein containers provide an innovative synthetic route to structurally defined heterogeneous catalysts with control over nanoparticle size and composition.

Binary Superlattice Design by Controlling DNA-Mediated Interactions

Most binary superlattices created using DNA functionalization rely on particle size differences to achieve compositional order and structural diversity. Here we study two-dimensional (2D) assembly of DNA-functionalized micron-sized particles (DFPs), and employ a strategy that leverages the tunable disparity in interparticle interactions, and thus enthalpic driving forces, to open new avenues for design of binary superlattices that do not rely on the ability to tune particle size (i.e., entropic driving forces). Our strategy employs tailored blends of complementary strands of ssDNA to control interparticle interactions between micron-sized silica particles in a binary mixture to create compositionally diverse 2D lattices. We show that the particle arrangement can be further controlled by changing the stoichiometry of the binary mixture in certain cases. With this approach, we demonstrate the ability to program the particle assembly into square, pentagonal, and hexagonal lattices. In addition, different particle types can be compositionally ordered in square checkerboard and hexagonal-alternating string, honeycomb, and Kagome arrangements.

Capping Ligand Vortices as “Atomic Orbitals” in Nanocrystal Self-Assembly

We present a detailed analysis of the interaction between two nanocrystals capped with ligands consisting of hydrocarbon chains by united atom molecular dynamics simulations. We show that the bonding of two nanocrystals is characterized by ligand textures in the form of vortices. These results are generalized to nanocrystals of different types (differing core and ligand sizes) where the structure of the vortices depends on the softness asymmetry. We provide rigorous calculations for the binding free energy, show that these energies are independent of the chemical composition of the cores, and derive analytical formulas for the equilibrium separation. We discuss the implications of our results for the self-assembly of single-component and binary nanoparticle superlattices. Overall, our results show that the structure of the ligands completely determines the bonding of nanocrystals, fully supporting the predictions of the recently proposed Orbifold topological model.

Tunable porous nanoallotropes prepared by post-assembly etching of binary nanoparticle superlattices.

Self-assembly of inorganic nanoparticles has been used to prepare hundreds of different colloidal crystals, but almost invariably with the restriction that the particles must be densely packed. Here, we show that non-close-packed nanoparticle arrays can be fabricated through the selective removal of one of two components comprising binary nanoparticle superlattices. First, a variety of binary nanoparticle superlattices were prepared at the liquid-air interface, including several arrangements that were previously unknown. Molecular dynamics simulations revealed the particular role of the liquid in templating the formation of superlattices not achievable through self-assembly in bulk solution. Second, upon stabilization, all of these binary superlattices could be transformed into distinct "nanoallotropes"-nanoporous materials having the same chemical composition but differing in their nanoscale architectures.

Non–close-packed nanoparticle arrays

Nanomaterials![Figure][1] Etching out one class of nanoparticles yields an open nanoarray. ILLUSTRATION: ELLA MARUSHCHENKO Films of colloidal nanoparticles usually form dense, close-packed lattices. If binary lattices could be made and one component removed, then a more open array could form, as long as the remaining nanoparticles could be stabilized. Udayabhaskararao et al. formed binary superlattices of gold and magnetite nanoparticles at an air-liquid interface that could then be transferred to carbon-coated surfaces (see the Perspective by Kotov). Selective etching of either of the nanoparticles created non–close-packed arrays with vacancies stabilized by the carbon surface. Science , this issue p. [514][2]; see also p. [448][3] [1]: pending:yes [2]: /lookup/doi/10.1126/science.aan6046 [3]: /lookup/volpage/358/448?iss=6362

Cooperative colloidal self-assembly of metal-protein superlattice wires

Material properties depend critically on the packing and order of constituent units throughout length scales. Beyond classically explored molecular self-assembly, structure formation in the nanoparticle and colloidal length scales have recently been actively explored for new functions. Structure of colloidal assemblies depends strongly on the assembly process, and higher structural control can be reliably achieved only if the process is deterministic. Here we show that self-assembly of cationic spherical metal nanoparticles and anionic rod-like viruses yields well-defined binary superlattice wires. The superlattice structures are explained by a cooperative assembly pathway that proceeds in a zipper-like manner after nucleation. Curiously, the formed superstructure shows right-handed helical twisting due to the right-handed structure of the virus. This leads to structure-dependent chiral plasmonic function of the material. The work highlights the importance of well-defined colloidal units when pursuing unforeseen and complex assemblies.

Nanoparticle Superlattices as Quasi-Frank-Kasper Phases.

I show that all phases reported experimentally in binary nanoparticle superlattices can be described as networks of disclinations in an ideal lattice of regular tetrahedra. A set of simple rules is provided to identify the different disclination types from the Voronoi construction, and it is shown that those disclinations completely screen the positive curvature of the ideal tetrahedral lattice. In this way, this study provides a generalization of the well-known Frank-Kasper phases to binary systems consisting of two types of particles, and with a more general type of disclinations, i.e., quasi-Frank-Kasper phases. The study comprises all strategies in nanoparticle self-assembly, whether driven by DNA or hydrocarbon ligands, and establishes the universal tendency of superlattices to develop icosahedral order, which is facilitated by the asymmetry of the particles. Besides its interest in predicting nanoparticle self-assembly, I discuss the implications for models of the glass transition, micelles of diblock polymers, and dendritic molecules, among many others.

Hierarchically self-assembled hexagonal honeycomb and kagome superlattices of binary 1D colloids

Synthesis of binary nanoparticle superlattices has attracted attention for a broad spectrum of potential applications. However, this has remained challenging for one-dimensional nanoparticle systems. In this study, we investigate the packing behavior of one-dimensional nanoparticles of different diameters into a hexagonally packed cylindrical micellar system and demonstrate that binary one-dimensional nanoparticle superlattices of two different symmetries can be obtained by tuning particle diameter and mixing ratios. The hexagonal arrays of one-dimensional nanoparticles are embedded in the honeycomb lattices (for AB2 type) or kagome lattices (for AB3 type) of micellar cylinders. The maximization of free volume entropy is considered as the main driving force for the formation of superlattices, which is well supported by our theoretical free energy calculations. Our approach provides a route for fabricating binary one-dimensional nanoparticle superlattices and may be applicable for inorganic one-dimensional nanoparticle systems.

Narrow-pass-band filters based on binary superlattices with strong impedance contrast

The photonic band structure and transmission coefficient of a dielectric bilayer periodic array with strong impedance contrast are calculated and analyzed. It is found that the photonic band structure has broad pass bands and very narrow gaps. However, because of the strong impedance contrast and the use of lossless dielectric layers, the transmission spectrum exhibits sharp peaks inside the pass bands. The remarkable properties of the superlattice can be used for the fabrication of narrow-pass-band filters.

Directional Carrier Transfer in Strongly Coupled Binary Nanocrystal Superlattice Films Formed by Assembly and in Situ Ligand Exchange at a Liquid–Air Interface

Two species of monodisperse nanocrystals (NCs) can self-assemble into a variety of complex 2D and 3D periodic structures, or binary NC superlattice (BNSL) films, based on the relative number and size of the NCs. BNSL films offer great promise for both fundamental scientific studies and optoelectronic applications; however, the utility of as-assembled structures has been limited by the insulating ligands that originate from the synthesis of NCs. Here we report the application of an in situ ligand exchange strategy at a liquid–air interface to replace the long synthesis ligands with short ligands while preserving the long-range order of BNSL films. This approach is demonstrated for BNSL structures consisting of PbSe NCs of different size combinations and ligands of interest for photovoltaic devices, infrared detectors, and light-emitting diodes. To confirm enhanced coupling introduced by ligand exchange, we show ultrafast (∼1 ps) directional carrier transfer across the type-I heterojunction formed by NCs of different sizes within ligand-exchanged BNSL films. This approach shows the potential promise of functional BNSL films, where the local and long-range energy landscape and electronic coupling can be adjusted by tuning NC composition, size, and interparticle spacing.

Hierarchical Materials Design by Pattern Transfer Printing of Self-Assembled Binary Nanocrystal Superlattices

We demonstrate the fabrication of hierarchical materials by controlling the structure of highly ordered binary nanocrystal superlattices (BNSLs) on multiple length scales. Combinations of magnetic, plasmonic, semiconducting, and insulating colloidal nanocrystal (NC) building blocks are self-assembled into BNSL membranes via the liquid-interfacial assembly technique. Free-standing BNSL membranes are transferred onto topographically structured poly(dimethylsiloxane) molds via the Langmuir-Schaefer technique and then deposited in patterns onto substrates via transfer printing. BNSLs with different structural motifs are successfully patterned into various meso- and microstructures such as lines, circles, and even three-dimensional grids across large-area substrates. A combination of electron microscopy and grazing incidence small-angle X-ray scattering (GISAXS) measurements confirm the ordering of NC building blocks in meso- and micropatterned BNSLs. This technique demonstrates structural diversity in the design of hierarchical materials by assembling BNSLs from NC building blocks of different composition and size by patterning BNSLs into various size and shape superstructures of interest for a broad range of applications.

Multi-atom pattern analysis for binary superlattices.

We present a method for the template-free characterization of binary superlattices. This is an extension of the Neighborhood Graph Analysis method, a technique which evaluates relationships between observed structures based on the topology of their first coordination shell [W. F. Reinhart, et al., Soft Matter, 2017, 13, 4733]. In the present work, we develop a framework for the analysis of multi-atom patterns, which incorporate structural information from the second coordination shell while providing a unified signature for all constituent particles in the superlattice. We construct an efficient metric for making quantitative comparisons between these patterns, making our algorithm the first capable of characterizing partial or defective superlattice structures. As in our previous work, we leverage machine learning techniques to characterize a range of self-assembled crystal structures, discovering a set of emergent collective variables which map each observed pattern into an intuitive global phase space. We demonstrate the method by performing classification of configurations from simulations of binary colloidal self-assembly in two dimensions.

Topological structure prediction in binary nanoparticle superlattices.

Systems of spherical nanoparticles with capping ligands have been shown to self-assemble into beautiful superlattices of fascinating structure and complexity. In this paper, I show that the spherical geometry of the nanoparticle imposes constraints on the nature of the topological defects associated with the capping ligand and that such topological defects control the structure and stability of the superlattices that can be assembled. All these considerations form the basis for the orbifold topological model (OTM) described in this paper. The model quantitatively predicts the structure of super-lattices where capping ligands are hydrocarbon chains in excellent agreement with experimental results, explains the appearance of low packing fraction lattices as equilibrium, why certain similar structures are more stable (bccAB6vs. CaB6, AuCu vs. CsCl, etc.) and many other experimental observations.