Generic phase diagram of binary superlattices

Theory and simulation of ligand functionalized nanoparticles - a pedagogical overview

Synthesizing reconfigurable nanoscale synthons with predictive control over shape, size, and interparticle interactions is a holy grail of bottom-up self-assembly. Grand challenges in their rational design, however, lie in both the large space of experimental synthetic parameters and proper understanding of the molecular mechanisms governing their formation. As such, computational and theoretical tools for predicting and modeling building block interactions have grown to become integral in modern day self-assembly research. In this review, we provide an in-depth discussion of the current state-of-the-art strategies available for modeling ligand functionalized nanoparticles. We focus on the critical role of how ligand interactions and surface distributions impact the emergent, pre-programmed behaviors between neighboring particles. To help build insights into the underlying physics, we first define an "ideal" limit - the short ligand, "hard" sphere approximation - and discuss all experimental handles through the lens of perturbations about this reference point. Finally, we identify theories that are capable of bridging interparticle interactions to nanoscale self-assembly and conclude by discussing exciting new directions for this field.

Macroscopic photonic single crystals via seeded growth of DNA-coated colloids

Photonic crystals-a class of materials whose optical properties derive from their structure in addition to their composition-can be created by self-assembling particles whose sizes are comparable to the wavelengths of visible light. Proof-of-principle studies have shown that DNA can be used to guide the self-assembly of micrometer-sized colloidal particles into fully programmable crystal structures with photonic properties in the visible spectrum. However, the extremely temperature-sensitive kinetics of micrometer-sized DNA-functionalized particles has frustrated attempts to grow large, monodisperse crystals that are required for photonic metamaterial applications. Here we describe a robust two-step protocol for self-assembling single-domain crystals that contain millions of optical-scale DNA-functionalized particles: Monodisperse crystals are initially assembled in monodisperse droplets made by microfluidics, after which they are grown to macroscopic dimensions via seeded diffusion-limited growth. We demonstrate the generality of our approach by assembling different macroscopic single-domain photonic crystals with metamaterial properties, like structural coloration, that depend on the underlying crystal structure. By circumventing the fundamental kinetic traps intrinsic to crystallization of optical-scale DNA-coated colloids, we eliminate a key barrier to engineering photonic devices from DNA-programmed materials.

A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures

Binary colloidal superlattices (BSLs) have demonstrated enormous potential for the design of advanced multifunctional materials that can be synthesized via colloidal self-assembly. However, mechanistic understanding of the three-dimensional self-assembly of BSLs is largely limited due to a lack of tractable strategies for characterizing the many two-component structures that can appear during the self-assembly process. To address this gap, we present a framework for colloidal crystal structure characterization that uses branched graphlet decomposition with deep learning to systematically and quantitatively describe the self-assembly of BSLs at the single-particle level. Branched graphlet decomposition is used to evaluate local structure via high-dimensional neighborhood graphs that quantify both structural order (e.g., body-centered-cubic vs face-centered-cubic) and compositional order (e.g., substitutional defects) of each individual particle. Deep autoencoders are then used to efficiently translate these neighborhood graphs into low-dimensional manifolds from which relationships among neighborhood graphs can be more easily inferred. We demonstrate the framework on in silico systems of DNA-functionalized particles, in which two well-recognized design parameters, particle size ratio and interparticle potential well depth can be adjusted independently. The framework reveals that binary colloidal mixtures with small interparticle size disparities (i.e., A- and B-type particle radius ratios of r _A/r _B = 0.8 to r _A/r _B = 0.95) can promote the self-assembly of defect-free BSLs much more effectively than systems of identically sized particles, as nearly defect-free BCC-CsCl, FCC-CuAu, and IrV crystals are observed in the former case. The framework additionally reveals that size-disparate colloidal mixtures can undergo nonclassical nucleation pathways where BSLs evolve from dense amorphous precursors, instead of directly nucleating from dilute solution. These findings illustrate that the presented characterization framework can assist in enhancing mechanistic understanding of the self-assembly of binary colloidal mixtures, which in turn can pave the way for engineering the growth of defect-free BSLs.

Understanding the binary interactions of noble metal and semiconductor nanoparticles

Molecular dynamics simulations are used to study the solvation and effective pair interactions of Au (1.2 nm) and CdSe (2.2 nm) nanoparticles passivated with alkanethiol and alkylamine ligands, respectively, for two different chain lengths in vacuum and n-hexane at 300 K. The solvation studies focus on quantifying the ligand and solvent shell structures, which are used to rationalize the interactions of nanoparticles in solution. To investigate the effective pair interactions, we compute the isotropic potential of mean forces (PMFs) between two nanoparticles and also analyze the anisotropy in the interactions that arises as a result of ligand shell fluctuations. Both isotropic and anisotropic contributions to the effective pair interactions between the two classes of nanoparticles are compared as a function of the ligand chain length and the solvent quality. It is demonstrated that the inclusion of the anisotropic aspect in the interparticle interactions is essential to properly describe the self-assembly thermodynamics of passivated nanoparticles. The implications of the coarse-grained modeling of the formation of binary nanocrystal superlattices (BNSLs) are considered.

Pattern detection in colloidal assembly: A mosaic of analysis techniques

Characterization of the morphology, identification of patterns and quantification of order encountered in colloidal assemblies is essential for several reasons. First of all, it is useful to compare different self-assembly methods and assess the influence of different process parameters on the final colloidal pattern. In addition, casting light on the structures formed by colloidal particles can help to get better insight into colloidal interactions and understand phase transitions. Finally, the growing interest in colloidal assemblies in materials science for practical applications going from optoelectronics to biosensing imposes a thorough characterization of the morphology of colloidal assemblies because of the intimate relationship between morphology and physical properties (e.g. optical and mechanical) of a material. Several image analysis techniques developed to investigate images (acquired via scanning electron microscopy, digital video microscopy and other imaging methods) provide variegated and complementary information on the colloidal structures under scrutiny. However, understanding how to use such image analysis tools to get information on the characteristics of the colloidal assemblies may represent a non-trivial task, because it requires the combination of approaches drawn from diverse disciplines such as image processing, computational geometry and computational topology and their application to a primarily physico-chemical process. Moreover, the lack of a systematic description of such analysis tools makes it difficult to select the ones more suitable for the features of the colloidal assembly under examination. In this review we provide a methodical and extensive description of real-space image analysis tools by explaining their principles and their application to the investigation of two-dimensional colloidal assemblies with different morphological characteristics.

Grand canonical inverse design of multicomponent colloidal crystals

Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. Here, we present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.

On the Convective Self-Assembly of Colloidal Particles in Nanofluid Based on in Situ Measurements of Interaction Forces

While the currently available techniques for the self-assembly of colloidal particles show great promise owing to their simplicity and high efficiency, they are plagued by the fact that they result in colloidal crystals with defects. Here, in order to overcome this problem, we propose a strategy that uses a suspension of nanoparticles (i.e., a nanofluid) as the "solvent" for the colloidal particles. We fabricated colloidal films of microspheres using such a nanofluid suspension and performed in situ measurements of the interaction forces between the microspheres in the nanofluid. This was done in order to systematically elucidate the effects of the nanoparticle size and the thickness of the electric double layer (Debye length) on the self-assembly process. The obtained results confirm that the use of the nanofluid results in a monolayer with a higher degree of order than that in the case of films formed using pure water. Further, the optimal size of the nanoparticles is determined based on the balance between their physical size and the Debye length. We also show that the lodging of the nanoparticles between the microspheres decreases both the lubrication force and the friction force between them. Thus, in this study, we show, for the first time, that a nanofluid can be used in the self-assembly process for improving the regularity of the fabricated colloidal particle arrays, as it inhibits the aggregation of the particles and limits the lubrication and friction forces between them.

A Journey Through the Landscapes of Small Particles in Binary Colloidal Assemblies: Unveiling Structural Transitions from Isolated Particles to Clusters upon Variation in Composition

Two-dimensional (2D) amorphous binary colloidal assemblies composed of particles of two different sizes are characterized by the loss of hexagonal close-packing for larger particles, occurring when the size ratio between small (S) and large (L) particles dSdL exceeds a certain threshold value. For moderately low particle number ratios NSNL large particles still retain a denser arrangement with transitions from hexagonal symmetry to the coexistence of different types of symmetries as NSNL progressively departs from 0 to higher values. On the other hand, small particles reveal sparser arrangements: shape identification and quantification of structural transitions in small particle arrangements appear particularly challenging. In this article, we investigate their shapes and transitions for amorphous binary colloidal particles assembled at the air/water interface. For the quantitative characterization of the evolution in particle arrangements for NSNL variable between 0.5 and 2, we develop an innovative procedure for morphological analysis, combining Minkowski functionals, Voronoi diagrams and ad hoc techniques to recognize and classify specific features. Such a powerful approach has revealed a wide variety of landscapes featuring isolated particles, dimers, chains, small clusters evolving with the colloidal suspension composition. Our method can be applied to the analysis of spatial configurations of sparse colloidal patterns obtained in different conditions.

Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly

We demonstrate a method based on symmetry to predict the structure of self-assembling, multicomponent colloidal mixtures. This method allows us to feasibly enumerate candidate structures from all symmetry groups and is many orders of magnitude more computationally efficient than combinatorial enumeration of these candidates. In turn, this permits us to compute ground-state phase diagrams for multicomponent systems. While tuning the interparticle potentials to produce potentially complex interactions represents the conventional route to designing exotic lattices, we use this scheme to demonstrate that simple potentials can also give rise to such structures which are thermodynamically stable at moderate to low temperatures. Furthermore, for a model two-dimensional colloidal system, we illustrate that lattices forming a complete set of 2-, 3-, 4-, and 6-fold rotational symmetries can be rationally designed from certain systems by tuning the mixture composition alone, demonstrating that stoichiometric control can be a tool as powerful as directly tuning the interparticle potentials themselves.

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.

Pattern Formation in Binary Colloidal Assemblies: Hidden Symmetries in a Kaleidoscope of Structures

In this study, we present a detailed investigation of the morphology of binary colloidal structures formed by self-assembly at air/water interface of particles of two different sizes, with a size ratio such that the larger particles do not retain a hexagonal arrangement in the binary assembly. While the structure and symmetry of binary mixtures in which such hexagonal order is preserved has been thoroughly scrutinized, binary colloids in the regime of nonpreservation of the hexagonal order have not been examined with the same level of detail due also to the difficulty in finding analysis tools suitable to recognize hidden symmetries in seemingly amorphous and disordered arrangements. For this purpose, we resorted to a combination of different analysis tools based on computational geometry and computational topology to get a comprehensive picture of the morphology of the assemblies. By carrying out an extensive investigation of binary assemblies in this regime with variable concentration of smaller particles with respect to larger particles, we identify the main patterns that coexist in the apparently disordered assemblies and detect transitions in the symmetries upon increase in the number of small particles. As the concentration of small particles increases, large particle arrangements become more dilute and a transition from hexagonal to rhombic and square symmetries occurs, accompanied also by an increase in clusters of small particles; the relative weight of each specific symmetry can be controlled by varying the composition of the assemblies. The demonstration of the possibility to control the morphology of apparently disordered binary colloidal assemblies by varying experimental conditions and the definition of a route for the investigation of disordered assemblies are important for future studies of complex colloidal patterns to understand self-assembly mechanisms and to tailor the physical properties of colloidal assemblies.

Nonisotropic Self-Assembly of Nanoparticles: From Compact Packing to Functional Aggregates

Quantum strongly correlated systems that exhibit interesting features in condensed matter physics often need an unachievable temperature or pressure range in classical materials. One solution is to introduce a scaling factor, namely, the lattice parameter. Synthetic heterostructures named superlattices or supracrystals are synthesized by the assembling of colloidal atoms. These include semiconductors, metals, and insulators for the exploitation of their unique properties. Most of them are currently limited to dense packing. However, some of desired properties need to adjust the colloidal atoms neighboring number. Here, the current state of research in nondense packing is summarized, discussing the benefits, outlining possible scenarios and methodologies, describing examples reported in the literature, briefly discussing the challenges, and offering preliminary conclusions. Penetrating such new and intriguing research fields demands a multidisciplinary approach accounting for the coupling of statistic physics, solid state and quantum physics, chemistry, computational science, and mathematics. Standard interactions between colloidal atoms and emerging fields, such as the use of Casimir forces, are reported. In particular, the focus is on the novelty of patchy colloidal atoms to meet this challenge.

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.

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.

Self-Assembly of Mesophases from Nanoparticles

A growing number of crystalline and quasi-crystalline structures have been formed by coating nanoparticles with ligands, polymers, and DNA. The design of nanoparticles that assemble into mesophases, such as those formed by block copolymers, would combine the order, mobility, and stimuli responsive properties of mesophases with the electronic, magnetic, and optical properties of nanoparticles. Here we use molecular simulations to demonstrate that binary mixtures of unbound particles with simple short-ranged pair interactions produce the same mesophases as block copolymers and surfactants, including lamellar, hexagonal, gyroid, body-centered cubic, face-centered cubic, perforated lamellar, and semicrystalline phases. The key to forming the mesophases is the frustrated attraction between particles of different types, achieved through control over interparticle size and over strength and softness of the interaction. Experimental design of nanoparticles with effective interactions described by the potentials of this work would provide a distinct, robust route to produce ordered tunable liquid crystalline mesophases from nanoparticles.

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.

Soft Skyrmions, Spontaneous Valence and Selection Rules in Nanoparticle Superlattices

A number of bewildering paradoxes arise in the field of nanoparticle self-assembly: nominal low density superlattices, strong stability of low coordination sites, and a clear but imperfect correlation between lattice stability and the maximum of hard sphere packing, despite the fact that that nanocrystals themselves are, through their ligands, very much compressible. In this study, I show that by regarding nanocrystals as pseudotopological objects ("soft skyrmions"), it is possible to identify and classify the ligand textures that determine their bonding. These textures consist of interacting vortices, where the total vorticity defines a spontaneous valence (coordination). Furthermore, skyrmion interactions are governed by two simple assumptions, which lead to a set of selection rules for superlattice structure. Besides resolving all the above paradoxes, the predictions are completely supported by more than one hundred sixty experiments gathered from the literature, including a wide range of nanocrystal cores and ligands (saturated or unsaturated hydrocarbons, amines, polystyrene, etc.). How those results can be used for addressing more complex structures and guiding future experiments is also addressed.

Tracing the Surfactant-Mediated Nucleation, Growth, and Superpacking of Gold Supercrystals Using Time and Spatially Resolved X-ray Scattering

The nucleation and growth process of gold supercrystals in a surfactant diffusion approach is followed by simultaneous small- and wide-angle X-ray scattering (SAXS/WAXS), supplemented with scanning electron microscopy. The results indicate that supercrystal nucleation can be activated efficiently upon placing a concentrated surfactant solution of a nematic phase on top of a gold nanocrystal solution droplet trapped in the middle of a vertically oriented capillary tube. Supercrystal nuclei comprised of tens of gold nanocubes are observed nearly instantaneously in the broadened liquid-liquid interface zone of a steep gradient of surfactant concentration, revealing a diffusion-kinetics-controlled nucleation process. Once formed, the nuclei can sediment into the naoncrystal zone below, and grow efficiently into cubic or tetragonal supercrystals of ∼1 μm size within ∼100 min. Supercrystals matured during sedimentation in the capillary can accumulate and face-to-face align at the bottom liquid-air interface of the nanocrystal droplet. This is followed by superpacking of the supercrystals into highly oriented hierarchical sheets, with a huge number of gold nanocubes aligned for largely coherent crystallographic orientations.