A mean-field theory for predicting single polymer collapse induced by neutral crowders

Macromolecular crowding can induce the collapse of a single long polymer into a globular form due to depletion forces of entropic nature. This phenomenon has been shown to play a significant role in compacting the genome within the bacterium Escherichia coli into a well-defined region of the cell known as the nucleoid. Motivated by the biological significance of this process, numerous theoretical and computational studies have searched for the primary determinants of the behavior of polymer-crowder phases. However, our understanding of this process remains incomplete and there is debate on a quantitatively unified description. In particular, different simulation studies with explicit crowders have proposed different order parameters as potential predictors for the collapse transition. In this work, we present a comprehensive analysis of published simulation data obtained from different sources. Based on the common behavior we find in this data, we develop a unified phenomenological model that we show to be predictive. Finally, to further validate the accuracy of the model, we conduct new simulations on polymers of various sizes, and investigate the role of jamming of the crowders.

Finding the differences: Classical nucleation perspective on homogeneous melting and freezing of hard spheres

By employing brute-force molecular dynamics, umbrella sampling, and seeding simulations, we investigate homogeneous nucleation during melting and freezing of hard spheres. We provide insights into these opposing phase transitions from the standpoint of classical nucleation theory. We observe that melting has both a lower driving force and a lower interfacial tension than freezing. The lower driving force arises from the vicinity of a spinodal instability in the solid and from a strain energy. The lower interfacial tension implies that the Tolman lengths associated with melting and freezing have opposite signs, a phenomenon that we interpret with Turnbull's rule. Despite these asymmetries, the nucleation rates for freezing and melting are found to be comparable.

Depletion-induced crystallization of anisotropic triblock colloids

The intricate interplay between colloidal particle shape and precisely engineered interaction potentials has paved the way for the discovery of unprecedented crystal structures in both two and three dimensions. Here, we make use of anisotropic triblock colloidal particles composed of two distinct materials. The resulting surface charge heterogeneity can be exploited to generate regioselective depletion interactions and directional bonding. Using extensive molecular dynamics simulations and a dimensionality reduction analysis approach, we map out state diagrams for the self-assembly of such colloids as a function of their aspect ratio and for varying depletant features in a quasi two-dimensional set-up. We observe the formation of a wide variety of crystal structures such as a herringbone, brick-wall, tilted brick-wall, and (tilted) ladder-like structures. More specifically, we determine the optimal parameters to enhance crystallization, and investigate the nucleation process. Additionally, we explore the potential of using crystalline monolayers as templates for deposition, thereby creating complex three-dimensional structures that hold promise for future applications.

Coarse-Grained Many-Body Potentials of Ligand-Stabilized Nanoparticles from Machine-Learned Mean Forces

Colloidal nanoparticles self-assemble into a variety of superstructures with distinctive optical, structural, and electronic properties. These nanoparticles are usually stabilized by a capping layer of organic ligands to prevent aggregation in the solvent. When the ligands are sufficiently long compared to the dimensions of the nanocrystal cores, the effective coarse-grained forces between pairs of nanoparticles are largely affected by the presence of neighboring particles. In order to efficiently investigate the self-assembly behavior of these complex colloidal systems, we propose a machine-learning approach to construct effective coarse-grained many-body interaction potentials. The multiscale methodology presented in this work constitutes a general bottom-up coarse-graining strategy where the coarse-grained forces acting on coarse-grained sites are extracted from measuring the vectorial mean forces on these sites in reference fine-grained simulations. These effective coarse-grained forces, i.e., gradients of the potential of mean force or of the free-energy surface, are represented by a simple linear model in terms of gradients of structural descriptors, which are scalar functions that are rotationally invariant. In this way, we also directly obtain the free-energy surface of the coarse-grained model as a function of all coarse-grained coordinates. We expect that this simple yet accurate coarse-graining framework for the many-body potential of mean force will enable the characterization, understanding, and prediction of the structure and phase behavior of relevant soft-matter systems by direct simulations. The key advantage of this method is its generality, which allows it to be applicable to a broad range of systems. To demonstrate the generality of our method, we also apply it to a colloid-polymer model system, where coarse-grained many-body interactions are pronounced.

A colloidal viewpoint on the sausage catastrophe and the finite sphere packing problem

It is commonly believed that the most efficient way to pack a finite number of equal-sized spheres is by arranging them tightly in a cluster. However, mathematicians have conjectured that a linear arrangement may actually result in the densest packing. Here, our combined experimental and simulation study provides a physical realization of the finite sphere packing problem by studying arrangements of colloids in a flaccid lipid vesicle. We map out a state diagram displaying linear, planar, and cluster conformations of spheres, as well as bistable states which alternate between cluster-plate and plate-linear conformations due to membrane fluctuations. Finally, by systematically analyzing truncated polyhedral packings, we identify clusters of 56 ≤ N ≤ 70 number of spheres, excluding N = 57 and 63, that pack more efficiently than linear arrangements.

The effect of shape, polydispersity, charge, and fraction of crystallite bundles on the cholesteric pitch of cellulose nanocrystal suspensions

Using Onsager-Straley's second-virial theory, we investigate the cholesteric pitch of cellulose nanocrystal (CNC) suspensions. We model the CNCs as hard chiral bundles of microfibrils and examine the effect of the shape of these chiral bundles, characterized by aspect ratio and chirality, on the cholesteric pitch. Additionally, we explore the impact of length polydispersity and surface charge on the cholesteric phase of CNCs. Furthermore, we consider binary mixtures of twisted bundles and achiral primary crystallites to provide a more realistic representation of CNC suspensions. Our findings reveal that the degree of bundle twisting significantly affects the helical twisting of the cholesteric phase. We also observe that the average particle length and length polydispersity have substantial effects on strongly twisted bundles but minimal effects on weakly twisted ones. Finally, our study indicates that as the range of electrostatic interactions increases, the transfer of chirality from the microscopic to macroscopic length scales becomes masked, resulting in an increase in the cholesteric pitch. In the case of binary mixtures, the bundles act as chiral dopants, and an increasing fraction of bundles progressively enhances the helical twisting of the cholesteric phase.

Brute-force nucleation rates of hard spheres compared with rare-event methods and classical nucleation theory

We determine nucleation rates of hard spheres using brute-force molecular dynamics simulations. We overcome nucleation barriers of up to 28 kBT, leading to a rigorous test of nucleation rates obtained from rare-event methods and classical nucleation theory. Our brute-force nucleation rates show excellent agreement with umbrella sampling simulations by Filion et al. [J. Chem. Phys. 133, 244115 (2010)] and seeding simulations by Espinosa et al. [J. Chem. Phys. 144, 034501 (2016)].

Real-Time In Situ Observation of CsPbBr3 Perovskite Nanoplatelets Transforming into Nanosheets

The manipulation of nano-objects through heating is an effective strategy for inducing structural modifications and therefore changing the optoelectronic properties of semiconducting materials. Despite its potential, the underlying mechanism of the structural transformations remains elusive, largely due to the challenges associated with their in situ observations. To address these issues, we synthesize temperature-sensitive CsPbBr₃ perovskite nanoplatelets and investigate their structural evolution at the nanoscale using in situ heating transmission electron microscopy. We observe the morphological changes that start from the self-assembly of the nanoplatelets into ribbons on a substrate. We identify several paths of merging nanoplates within ribbons that ultimately lead to the formation of nanosheets dispersed randomly on the substrate. These observations are supported by molecular dynamics simulations. We correlate the various paths for merging to the random orientation of the initial ribbons along with the ligand mobility (especially from the edges of the nanoplatelets). This leads to the preferential growth of individual nanosheets and the merging of neighboring ones. These processes enable the creation of structures with tunable emission, ranging from blue to green, all from a single material. Our real-time observations of the transformation of perovskite 2D nanocrystals reveal a route to achieve large-area nanosheets by controlling the initial orientation of the self-assembled objects with potential for large-scale applications.

Flexible Colloidal Molecules with Directional Bonds and Controlled Flexibility

Colloidal molecules are ideal model systems for mimicking real molecules and can serve as versatile building blocks for the bottom-up self-assembly of flexible and smart materials. While most colloidal molecules are rigid objects, the development of colloidal joints has made it possible to endow them with conformational flexibility. However, their unrestricted range of motion does not capture the limited movement and bond directionality that is instead typical of real molecules. In this work, we create flexible colloidal molecules with an in situ controllable motion range and bond directionality by assembling spherical particles onto cubes functionalized with complementary surface-mobile DNA. By varying the sphere-to-cube size ratio, we obtain colloidal molecules with different coordination numbers and find that they feature a constrained range of motion above a critical size ratio. Using theory and simulations, we show that the particle shape together with the multivalent bonds creates an effective free-energy landscape for the motion of the sphere on the surface of the cube. We quantify the confinement of the spheres on the surface of the cube and the probability to change facet. We find that temperature can be used as an extra control parameter to switch in situ between full and constrained flexibility. These flexible colloidal molecules with a temperature switching motion range can be used to investigate the effect of directional yet flexible bonds in determining their self-assembly and phase behavior, and may be employed as constructional units in microrobotics and smart materials.

Shape-induced crystallization of binary DNA-functionalized nanocubes

Leveraging the anisotropic shape of DNA-functionalized nanoparticles holds potential for shape-directed crystallization of a wide collection of superlattice structures. Using coarse-grained molecular dynamics simulations, we study the self-assembly of a binary mixture of cubic gold nanoparticles, which are functionalized by complementary DNA strands. We observe the spontaneous self-assembly of simple cubic (SC), plastic body-centered tetragonal (pBCT), and compositionally disordered plastic body-centered tetragonal (d-pBCT) phases due to hybridization of the DNA strands. We systematically investigate the effect of length, grafting density, as well as rigidity of the DNA strands on the self-assembly behavior of cubic nanoparticles. We measure the potential of mean force between DNA-functionalized nanocubes for varying rigidity of the DNA strands and DNA lengths. Using free-energy calculations, we find that longer and flexible DNA strands can lead to a phase transformation from SC to the pBCT phase due to a gain in entropy arising from the orientational degrees of freedom of the nanocubes in the pBCT phase. Our results may serve as a guide for self-assembly experiments on DNA-functionalized cubic nanoparticles.

Crystal Polymorph Selection Mechanism of Hard Spheres Hidden in the Fluid

Nucleation plays a critical role in the birth of crystals and is associated with a vast array of phenomena, such as protein crystallization and ice formation in clouds. Despite numerous experimental and theoretical studies, many aspects of the nucleation process, such as the polymorph selection mechanism in the early stages, are far from being understood. Here, we show that the hitherto unexplained excess of particles in a face-centered-cubic (fcc)-like environment, as compared to those in a hexagonal-close-packed (hcp)-like environment, in a crystal nucleus of hard spheres can be explained by the higher order structure in the fluid phase. We show using both simulations and experiments that in the metastable fluid phase, pentagonal bipyramids, clusters with fivefold symmetry known to be inhibitors of crystal nucleation, transform into a different cluster, Siamese dodecahedra. These clusters are closely similar to an fcc subunit, which explains the higher propensity to grow fcc than hcp in hard spheres. We show that our crystallization and polymorph selection mechanism is generic for crystal nucleation from a dense, strongly correlated fluid phase.

Dynamics of supercooled liquids from static averaged quantities using machine learning

We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. 
Compared to techniques based on particle propensity, our method is built upon a theoretical framework that uses as input and output system-averaged quantities, thus being easier to apply in an experimental context where particle resolved information is not available. 
In this work, we train a deep neural network to predict the self intermediate scattering function of binary mixtures using their static structure factor as input. 
While its performance is excellent for the temperature range of the training data, the model also retains some transferability in making decent predictions at temperatures lower than the ones it was trained for, or when we use it for similar systems. 
We also develop an evolutionary strategy that is able to construct a realistic memory function underlying the observed non-Markovian dynamics.
This method lets us conclude that the memory function of supercooled liquids can be effectively parameterized as the sum of two stretched exponentials, which physically corresponds to two dominant relaxation modes.

Steering self-organisation through confinement

Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement is an action over a system that limits its units' translational and rotational degrees of freedom, thus also influencing the system's phase space probability density; it can function as either a catalyst or inhibitor of self-organisation. Confinement can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework and perspective for future research, we examine the role of confinement in the self-organisation of soft-matter systems and identify overarching scientific challenges that need to be addressed to harness its full scientific and technological potential in soft matter and related fields. By drawing analogies with other disciplines, this framework will accelerate a common deeper understanding of self-organisation and trigger the development of innovative strategies to steer it using confinement, with impact on, e.g., the design of smarter materials, tissue engineering for biomedicine and in guiding active matter.

Coulombic Surface-Ion Interactions Induce Nonlinear and Chemistry-Specific Charging Kinetics

While important for many industrial applications, chemical reactions responsible for the charging of solids in water are often poorly understood. We theoretically investigate the charging kinetics of solid-liquid interfaces and find that the time-dependent equilibration of surface charge contains key information not only on the reaction mechanism, but also on the valency of the reacting ions. We construct a nonlinear differential equation describing surface charging by combining chemical Langmuir kinetics and electrostatic Poisson-Boltzmann theory. Our results reveal a clear distinction between late-time (near-equilibrium) and short-time (far-from-equilibrium) relaxation rates, the ratio of which contains information on the charge valency and ad- or desorption mechanism of the charging process. Similarly, we find that single-ion reactions can be distinguished from two-ion reactions, as the latter show an inflection point during equilibration. Interestingly, such inflection points are characteristic of autocatalytic reactions, and we conclude that the Coulombic ion-surface interaction is an autocatalytic feedback mechanism.

Ion Current Rectification and Long-Range Interference in Conical Silicon Micropores

Fluidic devices exhibiting ion current rectification (ICR), or ionic diodes, are of broad interest for applications including desalination, energy harvesting, and sensing, among others. For such applications a large conductance is desirable, which can be achieved by simultaneously using thin membranes and wide pores. In this paper we demonstrate ICR in micrometer sized conical channels in a thin silicon membrane with pore diameters comparable to the membrane thickness but both much larger than the electrolyte screening length. We show that for these pores the entrance resistance is key not only to Ohmic conductance around 0 V but also for understanding ICR, both of which we measure experimentally and capture within a single analytic theoretical framework. The only fit parameter in this theory is the membrane surface potential, for which we find that it is voltage dependent and its value is excessively large compared to the literature. From this we infer that surface charge outside the pore strongly contributes to the observed Ohmic conductance and rectification by a different extent. We experimentally verify this hypothesis in a small array of pores and find that ICR vanishes due to pore-pore interactions mediated through the membrane surface, while Ohmic conductance around 0 V remains unaffected. We find that the pore-pore interaction for ICR is set by a long-ranged decay of the concentration which explains the surprising finding that the ICR vanishes for even a sparsely populated array with a pore-pore spacing as large as 7 μm.

Splay-bend nematic phases of bent colloidal silica rods induced by polydispersity

Liquid crystal (LC) phases are in between solids and liquids with properties of both. Nematic LCs composed of rod-like molecules or particles exhibit long-range orientational order, yielding characteristic birefringence, but they lack positional order, allowing them to flow like a liquid. This combination of properties as well as their sensitivity to external fields make nematic LCs fundamental for optical applications e.g. liquid crystal displays (LCDs). When rod-like particles become bent, spontaneous bend deformations arise in the LC, leading to geometric frustration which can be resolved by complementary twist or splay deformations forming intriguing twist-bend (NTB) and splay-bend (NSB) nematic phases. Here, we show experimentally that the elusive NSB phases can be stabilized in systems of polydisperse micron-sized bent silica rods. Our results open avenues for the realization of NTB and NSB phases of colloidal and molecular LCs.

Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials

Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.

Guiding the self-assembly of colloidal diamond

The assembly of colloidal cubic diamond is a challenging process since the shape and interaction parameters and the thermodynamic conditions where this structure is stable are elusive. The simultaneous use of shape-anisotropic particles and strong directional interactions has proven to be a successful path to exclusively nucleate this structure. Here, using molecular dynamics simulations, we explore in detail the conditions where nucleation of cubic diamond from tetrahedral building blocks is favored. In particular, we focus on the effect of depletion and DNA-mediated interactions to form and stabilize this cubic diamond crystal. We find that a particular balance between the strength and range of the depletion interactions enhances the self-assembly of stable cubic diamond, leading to a narrow region where this structure is nucleated. Moreover, we determine that stronger short-range depletion attractions may arrest the system leading to the formation of percolating diamond networks or fully disordered gel structures. Accordingly, the internal arrangements of these structures exhibit a distinct variation in terms of fractal dimension and the presence of six-membered rings that increasingly acquire internal strain as the arrest gets more pronounced. With these results we provide a clear route for the self-assembly of cubic colloidal diamond, towards the realization of crystals with superior photonic properties.

Kinetic Phase Diagram for Nucleation and Growth of Competing Crystal Polymorphs in Charged Colloids

We determine the kinetic phase diagram for nucleation and growth of crystal phases in a suspension of charged colloids. Exploiting the seeding approach in extensive simulations, we calculate nucleation barrier heights for face-centered cubic (fcc) and body-centered cubic (bcc) phases for varying screening lengths and supersaturations. We determine for the entire metastable fluid region the crystal polymorph with the lowest nucleation barrier, and find a regime close to the triple point where metastable bcc can form due to a lower nucleation barrier, even though fcc is the stable phase. For higher supersaturation, we find that the difference in barrier heights decreases and we observe a mix of hexagonal close-packed, fcc, and bcc structures in the growth of crystalline seeds as well as in spontaneously formed crystals. Our kinetic phase diagram rationalizes the different crystallization mechanisms observed in previous work.

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically-symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally-efficient simulations of model systems consisting of colloidal particles with anisotropic shape: mixtures of colloidal rods and nonadsorbing polymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer. We find good agreement with results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well-described by the orientation-dependent ML potentials.

Crack barriers for thick SiN using dicing

Silicon nitride (SiN) waveguides need to be thick to show low dispersion which is desired for nonlinear applications. However, high quality thick SiN produced by chemical vapour deposition (CVD) contains high internal stress, causing it to crack. Crack-free wafers with thick SiN can be produced by adding crack barriers. We demonstrate the use of dicing trenches as a simple single-step method to produce high quality (loss<0.5 dB/cm) crack-free SiN. We show Kerr-comb generation in a ring resonator to highlight the high quality and low dispersion of the waveguides.

Coupling between splay deformations and density modulations in splay-bend phases of bent colloidal rods

Using a grand-canonical Landau-de Gennes theory for colloidal suspensions of bent (banana-shaped) rods, we investigate how spatial deformations in the nematic director field affect the local density of twist-bend and splay-bend nematic phases. The grand-canonical character of the theory naturally relates the local density to the local nematic order parameter S. In the splay-bend phase, we find S and hence the local density to modulate periodically along one spatial direction. As a consequence the splay-bend phase has the key symmetries of a smectic rather than a nematic phase. By contrast we find that S and hence the local density do not vary in space in the twist-bend phase, which is therefore a proper nematic phase. The theoretically predicted one-dimensional density modulations in splay-bend phases are in agreement with recent simulations.

Inverse design of soft materials via a deep learning-based evolutionary strategy

Colloidal self-assembly—the spontaneous organization of colloids into ordered structures—has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.

Modeling the cholesteric pitch of apolar cellulose nanocrystal suspensions using a chiral hard-bundle model

Cellulose nanocrystals (CNCs) are naturally sourced elongated nanocolloids that form cholesteric phases in water and apolar solvents. It is well accepted that CNCs are made of bundles of crystalline microfibrils clustered side-by-side, and there is growing evidence that each individual microfibril is twisted. Yet, the origin of the chiral interactions between CNCs remains unclear. In this work, CNCs are described with a simple model of chiral hard splinters, enabling the prediction of the pitch using density functional theory and Monte Carlo simulations. The predicted pitch P compares well with experimental observations in cotton-based CNC dispersions in apolar solvents using surfactants but also with qualitative trends caused by fractionation or tip sonication in aqueous suspensions. These results suggest that the bundle shape induces an entropy-driven chiral interaction between CNCs, which is the missing link in explaining how chirality is transferred from the molecular scale of cellulose chains to the cholesteric order.

Machine learning many-body potentials for colloidal systems

Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions.

From predictive modelling to machine learning and reverse engineering of colloidal self-assembly

An overwhelming diversity of colloidal building blocks with distinct sizes, materials and tunable interaction potentials are now available for colloidal self-assembly. The application space for materials composed of these building blocks is vast. To make progress in the rational design of new self-assembled materials, it is desirable to guide the experimental synthesis efforts by computational modelling. Here, we discuss computer simulation methods and strategies used for the design of soft materials created through bottom-up self-assembly of colloids and nanoparticles. We describe simulation techniques for investigating the self-assembly behaviour of colloidal suspensions, including crystal structure prediction methods, phase diagram calculations and enhanced sampling techniques, as well as their limitations. We also discuss the recent surge of interest in machine learning and reverse-engineering methods. Although their implementation in the colloidal realm is still in its infancy, we anticipate that these data-science tools offer new paradigms in understanding, predicting and (inverse) design of novel colloidal materials.

Oscillatory shear-induced bcc-fcc martensitic transformation in a colloidal suspension with long-range repulsive interactions

We perform non-equilibrium Brownian dynamics simulations to investigate the out-of-equilibrium phase behavior of a suspension of charged colloids under external oscillatory shear. We independently vary the frequency f and the maximum strain amplitude γ_max of the oscillations and map out an out-of-equilibrium phase diagram in the f-γ_max plane. Similar to what has been observed in earlier studies on colloidal hard spheres, we find the formation of a twinned face-centered-cubic phase in a specific range of γ_max, which displays a martensitic transition to a body-centered-cubic crystal within half of the oscillation cycle. We provide a comprehensive analysis of these structures and show how the system transforms from one to the other. We also report evidence of a sliding layer phase and a string phase.

Nonconventional Phases of Colloidal Nanorods with a Soft Corona

Using computer simulations, we investigate the phase behavior of hard-core spherocylinders with a length-to-diameter ratio L/σ=5 and coated by a soft deformable corona of length λ/σ=1.35. When quasi-two-dimensional layers are formed in smectic and solid phases at low temperatures, the competition between the two intrinsic length scales of the parallel aligned particles leads to the stabilization of different in-plane lattices of nonconventional symmetry, including low-density hexagonal, square, and high-density hexagonal crystals, as well as an intriguing dodecagonal quasicrystal. Our Letter opens up the opportunity to control the assembly of anisotropic nanoparticles into structures with preengineered symmetry-dependent physical properties.

A generalized density-modulated twist-splay-bend phase of banana-shaped particles

In 1976, Meyer predicted that bend distortions of the nematic director field are complemented by deformations of either twist or splay, yielding twist-bend and splay-bend nematic phases, respectively. Four decades later, the existence of the splay-bend nematic phase remains dubious, and the origin of these spontaneous distortions uncertain. Here, we conjecture that bend deformations of the nematic director can be complemented by simultaneous distortions of both twist and splay, yielding a twist-splay-bend nematic phase. Using theory and simulations, we show that the coupling between polar order and bend deformations drives the formation of modulated phases in systems of curved rods. We find that twist-bend phases transition to splay-bend phases via intermediate twist-splay-bend phases, and that splay distortions are always accompanied by periodic density modulations due to the coupling of the particle curvature with the non-uniform curvature of the splayed director field, implying that the twist-splay-bend and splay-bend phases of banana-shaped particles are actually smectic phases.

The so-called twist-bend and splay-bend nematic liquid crystal phases are important concepts for studying bent-core mesogens. Chiappini et al. use a theory/simulation approach to suggest that the transition proceed via a twist-splay-bend phase which may be obscured by density modulations.

Spontaneous organization of supracolloids into three-dimensional structured materials

Periodic nano- or microscale structures are used to control light, energy and mass transportation. Colloidal organization is the most versatile method used to control nano- and microscale order, and employs either the enthalpy-driven self-assembly of particles at a low concentration or the entropy-driven packing of particles at a high concentration. Nonetheless, it cannot yet provide the spontaneous three-dimensional organization of multicomponent particles at a high concentration. Here we combined these two concepts into a single strategy to achieve hierarchical multicomponent materials. We tuned the electrostatic attraction between polymer and silica nanoparticles to create dynamic supracolloids whose components, on drying, reorganize by entropy into three-dimensional structured materials. Cryogenic electron tomography reveals the kinetic pathways, whereas Monte Carlo simulations combined with a kinetic model provide design rules to form the supracolloids and control the kinetic pathways. This approach may be useful to fabricate hierarchical hybrid materials for distinct technological applications.

An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB13 Crystal

Colloidal suspensions of two species have the ability to form binary crystals under certain conditions. The hunt for these functional materials and the countless investigations on their formation process are justified by the plethora of synergetic and collective properties these binary superlattices show. Among the many crystal structures observed over the past decades, the highly exotic colloidal icosahedral AB₁₃ crystal was predicted to be stable in binary hard-sphere mixtures nearly 30 years ago, yet the kinetic pathway of how homogeneous nucleation occurs in this system is still unknown. Here we investigate binary nucleation of the AB₁₃ crystal from a binary fluid phase of nearly hard spheres. We calculate the nucleation barrier and nucleation rate as a function of supersaturation and draw a comparison with nucleation of single-component and other binary crystals. To follow the nucleation process, we employ a neural network to identify the AB₁₃ phase from the binary fluid phase and the competing fcc crystal with single-particle resolution and significant accuracy in the case of bulk phases. We show that AB₁₃ crystal nucleation proceeds via a coassembly process where large spheres and icosahedral small-sphere clusters simultaneously attach to the nucleus. Our results lend strong support for a classical pathway that is well-described by classical nucleation theory, even though the binary fluid phase is highly structured and exhibits local regions of high bond orientational order.

Chains of cubic colloids at fluid-fluid interfaces

Inspired by recent experimental observations of spontaneous chain formation of cubic particles adsorbed at a fluid-fluid interface, we theoretically investigate whether capillary interactions can be responsible for this self-assembly process. We calculate adsorption energies, equilibrium particle orientations, and interfacial deformations, not only for a variety of contact angles but also for single cubes as well as an infinite 2D lattice of cubes at the interface. This allows us to construct a ground-state phase diagram as a function of areal density for several contact angles, and upon combining the capillary energy of a 2D lattice with a simple expression for the entropy of a 2D fluid we also construct temperature-density or size-density phase diagrams that exhibit large two-phase regions and triple points. We identify several regimes with stable chainlike structures, in line with the experimental observations.

Mode-splitting in a microring resonator for self-referenced biosensing

Self-referenced biosensing based on mode-splitting on a microring resonator is experimentally demonstrated. A Bragg grating integrated on the surface of the ring provides coupling between the clockwise and counterclockwise travelling modes of the pristine ring resonator lifting their degeneracy. The amount of mode-splitting is directly related to the reflectivity of the grating and it is only affected by structurally modifying the grating. Environmental perturbations to the surroundings of the gratings, such as temperature and bulk refractive index variations, have a minor effect on the amount of mode-splitting. This principle allows the realization of a self-referenced sensing scheme based on the detection of variations of the mode-splitting induced by structural changes to the grating. In this work, a polymethyl methacrylate (PMMA) Bragg grating is integrated onto a ring resonator in Al₂O₃. It is shown both theoretically and experimentally that the amount of splitting of a resonance varies minimally under temperature or bulk refractive index perturbations. However, the structural change of attaching a layer of biomolecules inside the grating does affect its reflectivity and the amount of mode splitting present. This result represents the first proof-of-concept demonstration of an integrated mode-splitting biosensor insensitive to temperature and refractive index variations of the liquid matrix where the molecules to be detected are embedded. The reported results pave the road towards the realization of truly self-referenced biosensors.

Classifying Crystals of Rounded Tetrahedra and Determining Their Order Parameters Using Dimensionality Reduction

Using simulations we study the phase behavior of a family of hard spherotetrahedra, a shape that interpolates between tetrahedra and spheres. We identify 13 close-packed structures, some with packings that are significantly denser than previously reported. Twelve of these are crystals with unit cells of N = 2 or N = 4 particles, but in the shape regime of slightly rounded tetrahedra we find that the densest structure is a quasicrystal approximant with a unit cell of N = 82 particles. All 13 structures are also stable below close packing, together with an additional 14th plastic crystal phase at the sphere side of the phase diagram, and upon sufficient dilution to packing fractions below 50-60% all structures melt. Interestingly, however, upon compressing the fluid phase, self-assembly takes place spontaneously only at the tetrahedron and the sphere side of the family but not in an intermediate regime of tetrahedra with rounded edges. We describe the local environment of each particle by a set of l-fold bond orientational order parameters q̅l, which we use in an extensive principal component analysis. We find that the total packing fraction as well as several particular linear combinations of q̅l rather than individual q̅l's are optimally distinctive, specifically the differences q̅4 - q̅6 for separating tetragonal from hexagonal structures and q̅4-q̅8 for distinguishing tetragonal structures. We argue that these characteristic combinations are also useful as reliable order parameters in nucleation studies, enhanced sampling techniques, or inverse-design methods involving odd-shaped particles in general.

Helicoidal dynamics of biaxial curved rods in twist-bend nematic phases unveiled by unsupervised machine learning techniques

Uniaxial rods in a nematic phase diffuse preferentially in the direction parallel to the nematic director n[over ̂]. The nematic director field n[over ̂](r) of a chiral twist-bend nematic (N_{TB}) phase of achiral banana-shaped particles, recently discovered experimentally, displays a heliconical twist of given handedness and periodicity. Using simulations, we investigate the long-time macroscopic diffusion in N_{TB} phases, and find that the predilection of curved rods to diffuse in the direction of the twisting n[over ̂](r) yields a fascinating chiral dynamics along helices, even though achiral curved rods display Brownian motion with a nontrivial rototranslational coupling. We devise a machine learning protocol to characterize the helicoidal particle trajectories, finding that their pitch and radius are determined by the pitch and conical angle of the N_{TB} phase thereby connecting its structural and dynamical properties.

Shaping colloidal bananas to reveal biaxial, splay-bend nematic, and smectic phases

Understanding the impact of curvature on the self-assembly of elongated microscopic building blocks, such as molecules and proteins, is key to engineering functional materials with predesigned structure. We develop model “banana-shaped” colloidal particles with tunable dimensions and curvature, whose structure and dynamics are accessible at the particle level. By heating initially straight rods made of SU-8 photoresist, we induce a controllable shape deformation that causes the rods to buckle into banana-shaped particles. We elucidate the phase behavior of differently curved colloidal bananas using confocal microscopy. Although highly curved bananas only form isotropic phases, less curved bananas exhibit very rich phase behavior, including biaxial nematic phases, polar and antipolar smectic-like phases, and even the long-predicted, elusive splay-bend nematic phase.

A Landau-de Gennes theory for twist-bend and splay-bend nematic phases of colloidal suspensions of bent rods

We develop a phenomenological Landau-de Gennes (LdG) theory for lyotropic colloidal suspensions of bent rods using a Q-tensor expansion of the chemical-potential dependent grand potential. In addition, we introduce a bend flexoelectric term, coupling the polarization and the divergence of the Q-tensor, to study the stability of uniaxial (N), twist-bend (N_TB), and splay-bend (N_SB) nematic phases of colloidal bent rods. We first show that a mapping can be found between the LdG theory and the Oseen-Frank theory. By breaking the degeneracy between the splay and bend elastic constants, we find that the LdG theory predicts either an N-N_TB-N_SB or an N-N_SB-N_TB phase sequence upon increasing the particle concentration. Finally, we employ our theory to study the first-order N-N_TB phase transition, for which we find that K₃₃ as well as its renormalized version K₃₃ ^eff remain positive at the transition, whereas K₃₃ ^eff vanishes at the nematic spinodal. We connect these findings to recent simulation results.

High antisite defect concentrations in hard-sphere colloidal Laves phases

Binary mixtures of hard spheres can spontaneously self-assemble into binary crystals. Computer simulations have been especially useful in mapping out the phase behaviour of these mixtures, under the assumption that the stoichiometry of the binary crystal is ideal. Here we show that for a size ratio of q = 0.82 this assumption is not valid near the coexistence region between the fluid and the stable binary crystal, the MgZn2 Laves phase. Instead we find a surprisingly high number of antisite defects: up to 2% of the large spheres are replaced by small spheres in equilibrium. We demonstrate that the defect concentration can be estimated using simple approximations, providing an easy way to identify systems where antisite defects play an important role. Our results shed new light on the self-assembly of colloidal Laves phases, and demonstrate the importance of antisite defects in binary crystals.

High incidence of HCV in HIV-negative men who have sex with men using pre-exposure prophylaxis

BACKGROUND & AIMS

HCV has emerged as a sexually transmitted infection (STI) among HIV-positive men who have sex with men (MSM). We evaluated HCV incidence and its risk factors among HIV-negative MSM using HIV pre-exposure prophylaxis (PrEP).

METHODS

Participants of the Amsterdam PrEP project were tested for HCV antibodies or HCV-RNA every 6 months. Participants used daily or event-driven PrEP and could switch regimens during follow-up. We calculated incidence rates (IRs) for overall HCV infection and separately for primary and re-infection. A univariable Bayesian exponential survival model was used to identify risk factors associated with incident HCV infection. The HCV NS5B gene fragment (709 bp) was sequenced and compared to HCV isolates from HIV-positive MSM and other risk groups (n = 419) using phylogenetic analysis.

RESULTS

Among 350 participants contributing 653.6 person-years (PYs), we detected 15 HCV infections in 14 participants (IR = 2.30/100PY). There were 8 primary infections (IR = 1.27/100PY) and 7 re-infections (IR = 27.8/100PY). IR was 2.71/100PY in daily and 1.15/100PY in event-driven PrEP users. Factors associated with incident HCV infection were higher number of receptive condomless anal sex acts with casual partners (posterior hazard ratio [HR] 1.57 per ln increase; 95% credibility interval [CrI] 1.09-2.20), anal STI (posterior HR 2.93; 95% CrI 1.24-7.13), injecting drug use (posterior HR 4.69; 95% CrI 1.61-12.09) and sharing straws when snorting drugs (posterior HR 2.62; 95% CrI 1.09-6.02). We identified robust MSM-specific HCV clusters of subtypes 1a, 4d, 2b and 3a, which included MSM with and without HIV.

CONCLUSIONS

HIV-negative MSM using PrEP are at risk of incident HCV infection, while identified risk factors are similar to those in HIV-positive MSM. Regular HCV testing is needed, especially for those with a previous HCV infection and those reporting risk factors.

LAY SUMMARY

We report that hepatitis C virus infections are frequently acquired among HIV-negative men who have sex with men (MSM) using pre-exposure prophylaxis to prevent HIV infection. New infections occurred more frequently in those reporting receptive anal sex without using condoms, having an anal sexually transmitted infection, injecting drugs, and sharing straws when snorting drugs. The viruses found in HIV-negative men using pre-exposure prophylaxis are genetically similar to those in HIV-positive men, but not in other hepatitis C risk groups, suggesting that (sexual) transmission is occurring between HIV-positive MSM and HIV-negative MSM using pre-exposure prophylaxis.

CLINICAL TRIAL NUMBER

Dutch trial registration number NTR5411.

Tuning the Glass Transition: Enhanced Crystallization of the Laves Phases in Nearly Hard Spheres

Colloidal crystals with a diamond and pyrochlore structure display wide photonic band gaps at low refractive index contrasts. However, these low-coordinated and open structures are notoriously difficult to self-assemble from colloids interacting with simple pair interactions. To circumvent these problems, one can self-assemble both structures in a closely packed MgCu₂ Laves phase from a binary mixture of colloidal spheres and then selectively remove one of the sublattices. Although Laves phases have been proven to be stable in a binary hard-sphere system, they have never been observed to spontaneously crystallize in such a fluid mixture in simulations nor in experiments of micron-sized hard spheres due to slow dynamics. Here we demonstrate, using computer simulations, that softness in the interparticle potential suppresses the degree of 5-fold symmetry in the binary fluid phase and enhances crystallization of Laves phases in nearly hard spheres.

Development of low-loss TiO2 waveguides

TiO₂ channel waveguides were fabricated using a DC sputter deposition process, followed by photolithography and reactive ion etching. A SiO₂ cladding was deposited using evaporation. SEM, TEM and Raman measurements indicate the presence of both an amorphous and a crystalline phase. As the layer thickness increases, poly-crystalline structures start forming. Loss measurements were performed by imaging the scattered light from the top of the channel waveguides and fitting an exponential decay to the intensity profile. Propagation losses of 7.8 ± 0.52 dB/cm at a wavelength of 632.8 nm and 0.68 ± 0.46 dB/cm at a wavelength of 1010 nm were experimentally characterized.

Speeding up Dynamics by Tuning the Noncommensurate Size of Rodlike Particles in a Smectic Phase

Using simulations, we study the diffusion of rodlike guest particles in a smectic environment of rodlike host particles. We find that the dynamics of guest rods across smectic layers changes from a fast nematiclike diffusion to a slow hopping-type dynamics via an intermediate switching regime by varying the length of the guest rods with respect to the smectic layer spacing. We determine the optimal rod length that yields the fastest and the slowest diffusion in a lamellar environment. We show that this behavior can be rationalized by a complex 1D effective periodic potential exhibiting two energy barriers, resulting in a varying preferred mean position of the guest particle in the smectic layer. The interplay of these two barriers controls the dynamics of the guest particles yielding a slow, an intermediate, and a fast diffusion regime depending on the particle length.

Biaxial, Twist-bend, and Splay-bend Nematic Phases of Banana-shaped Particles Revealed by Lifting the "Smectic Blanket"

We perform an extensive computational study on the phase behavior of hard banana-shaped particles, and show that biaxial, twist-bend, and splay-bend nematic phases are metastable with respect to a smectic phase for a system of hard bent spherocylinders. However, if the smectic phase is destabilized-either by polydispersity in the particle length or by curvature in the particle shape-stable biaxial, twist-bend, and splay-bend nematic phases are obtained. This provides a unified and consistent picture on the subtle role of particle shape on the phase behavior of bent rods.

Al2O3 microring resonators for the detection of a cancer biomarker in undiluted urine

Concentrations down to 3 nM of the rhS100A4 protein, associated with human tumor development, have been detected in undiluted urine using an integrated sensor based on microring resonators in the emerging Al₂O₃ photonic platform. The fabricated microrings were designed for operation in the C-band (λ = 1565 nm) and exhibited a high-quality factor in air of 3.2 × 10⁵. The bulk refractive index sensitivity of the devices was ~100 nm/RIU (for TM polarization) with a limit of detection of ~10^-6 RIU. A surface functionalization protocol was developed to allow for the selective binding of the monoclonal antibodies designed to capture the target biomarker to the surface of the Al₂O₃ microrings. The detection of rhS100A4 proteins at clinically relevant concentrations in urine is a big milestone towards the use of biosensors for the screening and early diagnosis of different cancers. Biosensors based on this microring technology can lead to portable, multiplexed and easy-to-use point of care devices.

Interparticle torques suppress motility-induced phase separation for rodlike particles

To study the role of torque in motility-induced phase separation (MIPS), we simulate a system of self-propelled particles whose shape varies smoothly from isotropic (disks/spheres) to weakly elongated (rods). We construct the phase diagrams of 2D active disks, 3D active spheres, and 2D/3D active rods of aspect ratio l/σ = 2. A stability analysis of the homogeneous isotropic phase allows us to predict the onset of MIPS based on the effective swimming speed and rotational diffusion of the particles. Both methods find suppression of MIPS as the particle shape is elongated. We propose a suppression mechanism based on the duration of collisions and argue that this mechanism can explain both the suppression of MIPS found here for rodlike particles and the enhancement of MIPS found for particles with Vicsek interactions.

Equilibrium configurations and capillary interactions of Janus dumbbells and spherocylinders at fluid-fluid interfaces

We numerically investigate the adsorption of a variety of Janus particles (dumbbells, elongated dumbbells and spherocylinders) at a fluid-fluid interface by using a numerical method that takes into account the interfacial deformations. We first determine the equilibrium configuration of a single adsorbed particle, and we find that the overall shape of the induced deformation field has a strong hexapolar mode while non-Janus particles of the same shape do not induce any interfacial deformation. We then calculate the capillary interactions between two Janus spherocylinders adsorbed at an interface. The hexapolar deformation field induces capillary attractions for laterally aligned Janus spherocylinders and repulsions for laterally anti-aligned ones. We also experimentally synthesize micrometer-sized charged Janus dumbbells and let them adsorb at a water-decane interface. After several hours we observe the formation of aggregates of dumbbells predominantly induced by interactions that appear to be capillary in nature. Our Janus dumbbells attach laterally and are all aligned, as predicted by our numerical calculations.