Gulliver

We analyze the aggregation of locally amphiphilic copolymers with blocky architecture and uniformly distributed amphiphilic moieties in terms of a mean-field theory. Locally amphiphilic structure is characteristic of many thermoresponsive polymers, both linear and grafted, which endows them with local surface activity. Self-assembly of such copolymers exhibits a rich diversity of morphologies, which are analyzed in the present work in the limit of high surface activity of amphiphilic dimers with solvophilic/polar pendants. Depending on the composition and architecture of the copolymer and also sizes and interaction parameters of the solvophilic/polar pendants, we build morphological diagrams of copolymer solutions. Copolymers with small volume pendants form precipitates with internal voids containing these pendants. For moderate volume pendants, a lamellar structure (or large vesicles) is observed at smaller fractions of amphiphilic monomer units in the chain and micelles are formed at larger fractions of these units. For large polar pendants, copolymers of both types mainly form granular branched compound micelles. The type and sizes of micelles depend on the monomer distribution along the chain, and the blocky architecture favors micellization and formation of spherical particles. Micelles of macromolecules with sufficiently long side chains have adjacent outer layers saturated with the polar groups, and the size of hydrophobic domains of such micelles is independent of the side chain lengths. For shorter side chains, their length determines the size of domains formed by the amphiphilic blocks of the block copolymers and the thickness of discs or layers and the radius of beads formed by the regular copolymers.

• First microfluidic nanoprecipitation of polybutylene oxide nanoparticles • Ψ-geometry channels yield smaller, more uniform nanoparticles than T-geometries • Low flow rate ratios promote rapid mixing and enhance nanoparticle uniformity • High total flow rates improve mixing while maintaining laminar flow conditions • Miniaturized channels (20 µm) improve size control via enhanced diffusion Microfluidic-assisted nanoprecipitation provides precise control over formulation conditions, enabling for the design of nanoparticles with highly tunable properties. This study explores the influence of channel geometry, flow dynamics, and polymer concentration on the size and polydispersity of poly(butylene oxide) (PBO) nanoparticles. PBO is a hydrophobic polymer with a low glass transition temperature ( T g = –71 °C) that typically forms large nanoparticles (>176 nm) via bulk nanoprecipitation, as well as aggregates ranging from 3000–5000 nm. Using a hydrodynamic flow-focusing Ψ-geometry, we demonstrate that higher total flow rates increase convective mixing, reduce mixing times, and produce smaller, more monodisperse PBO nanoparticles. A comparative analysis of Ψ- and T-channel geometries across various dimensions revealed that Ψ-geometries consistently outperformed T-geometries due to their superior mixing efficiency. Decreasing the channel dimensions to 20 µm further improved mixing by shortening diffusion lengths and accelerating solvent–antisolvent interdiffusion. Using the Ψ-geometry, nanoparticles as small as 66 nm were achieved, whereas T-geometries produced significantly larger particles (>500 nm). A linear trend between particle size and total flow was observed, best described by a power-law relationship, linking flow rate—and by extension, Reynolds number—to mixing speed and nanoparticle size. These findings highlight the pivotal role of microfluidic design and flow control in tailoring nanoprecipitation for low- T g , hydrophobic polymers such as PBO. This approach shows promising potential for the encapsulation and delivery of hydrophobic drugs.

Dynamics that are microscopic in space and time, where particles commit to a new position, referred to as excitations, are considered the elementary unit of relaxation in the dynamic facilitation theory of the glass transition. Meanwhile, geometric motifs known as locally favored structures are associated with vitrification in many glassformers. Recent work indicates that the probability of particles being found in both locally favored structures (LFS) and excitations decreases significantly upon supercooling, suggesting an anticorrelation between them [Ortlieb etal., Nat. Commun. 14, 2621 (2023)2041-172310.1038/s41467-023-37793-2]. However, the spatial relationship between excitations and LFS remains unclear. By employing state-of-the-art GPU computer simulations and colloid experiments, we analyze this relationship in model glassformers. We reveal a strong anti-correlation between long-lived LFS and excitations, along with a spatial separation between the two in deeply supercooled liquids. This strong anticorrelation likely arises because well-packed LFS resist local rearrangements, and thus hindering the formation of excitations.

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

Fully developed turbulence is a universal and scale-invariant chaotic state characterized by an energy cascade from large to small scales at which the cascade is eventually arrested by dissipation1–6. Here we show how to harness these seemingly structureless turbulent cascades to generate patterns. Pattern formation entails a process of wavelength selection, which can usually be traced to the linear instability of a homogeneous state7. By contrast, the mechanism we propose here is fully nonlinear. It is triggered by the non-dissipative arrest of turbulent cascades: energy piles up at an intermediate scale, which is neither the system size nor the smallest scales at which energy is usually dissipated. Using a combination of theory and large-scale simulations, we show that the tunable wavelength of these cascade-induced patterns can be set by a non-dissipative transport coefficient called odd viscosity, ubiquitous in chiral fluids ranging from bioactive to quantum systems8–12. Odd viscosity, which acts as a scale-dependent Coriolis-like force, leads to a two-dimensionalization of the flow at small scales, in contrast with rotating fluids in which a two-dimensionalization occurs at large scales4. Apart from odd viscosity fluids, we discuss how cascade-induced patterns can arise in natural systems, including atmospheric flows13–19, stellar plasma such as the solar wind20–22, or the pulverization and coagulation of objects or droplets in which mass rather than energy cascades23–25.

We simulate a two-dimensional array of droplets being compressed between two walls. The droplets are adhesive due to an attractive depletion force. As one wall moves toward the other, the droplet array is compressed and eventually induced to rearrange. The rearrangement occurs via a fracture, where depletion bonds are quickly broken between a subset of droplets. For monodisperse, hexagonally ordered droplet arrays, this fracture is preceded by a maximum force exerted on the walls, which drops rapidly after the fracture occurs. In small droplet arrays a fracture is a single well-defined event, but for larger droplet arrays, competing fractures can be observed. These are fractures nucleated nearly simultaneously in different locations. Finally, we also study the compression of bidisperse droplet arrays. The addition of a second droplet size further disrupts fracture events, showing differences between ideal crystalline arrays, crystalline arrays with a small number of defects, and fully amorphous arrays. These results are in good agreement with previously published experiments.

Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans.

We study the diffusivity of a tagged particle in a binary mixture of Brownian particles with nonreciprocal interactions. Numerical simulations reveal that, for a broad class of interaction potentials, nonreciprocity can significantly increase the long-time diffusion coefficient of tracer particles and that this diffusion enhancement is associated with a breakdown of the Einstein relation. These observations are quantified and confirmed via two different and complementary analytical approaches: (i) a linearized stochastic density field theory, which is particularly accurate in the limit of soft interactions, and (ii) a reduced two-body description, which is exact at leading order in the density of particles. The latter reveals that diffusion enhancement can be attributed to the formation of transiently propelled dimers of particles, whose cohesion and speed are controlled by the nonreciprocal interactions.

The droplet size in emulsions is known to affect the rheological properties and plays a crucial role in the many applications of emulsions. Despite its importance, the underlying mechanisms governing droplet size in emulsification remain poorly understood. We investigate the average drop size and size distribution upon emulsification with a high-shear mixer for model oil-in-water emulsions stabilized by a surfactant. The size distribution is found to be a log-normal distribution, resulting from the repetitive random breakup of drops. High-shear emulsification, the usual way of making emulsions, is therefore found to be very different from turbulent emulsification given by the Kolmogorov-Hinze theory for which power-law distributions of the drop size are expected. In agreement with this, the mean droplet size does not follow a scaling with the Reynolds number of the emulsification flow, but rather a capillary number scaling based on the viscosity of the continuous phase.

We propose a computational strategy to quantify the temperature evolution of the timescales and lengthscales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, that requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time.