arXiv subjects

Renaud Baillou

Publications and source records attributed to Renaud Baillou.

4 recordsLinked to original sources

arXiv2026

Emergent charge crystallization and frustration in a particle anti-spin Ice

Artificial spin ices have transcended their origins in frustrated rare-earth pyrochlores to become a versatile platform for engineering exotic states of matter. Across diverse implementations, from nanomagnets and superconducting vortices to colloids, quantum annealers, liquid crystals, and metamaterials, they are unified by the ice rule, which often leads to degeneracy and constrained disorder by enforcing minimization of the local topological charge. Here, we report the first realization of an "anti-spin ice" in which not only the ice rule does not hold, but its opposite is true as the system seeks to maximize, rather than minimize, spin ice charges. Using fast-rotating, in-plane magnetic fields to generate isotropic attraction between colloidal particles, we invert the conventional paradigm of repulsive interactions in colloidal spin ices. Combining experiments and simulations across standard square and honeycomb lattices as well as novel pentaheptite geometries, we establish rules for order and disorder in the anti-spin ice. With the pentaheptite lattice, we demonstrate that the anti-spin ice system can also exhibit frustration, but of a new kind. This topological charge frustration arises from the lattice connectivity, where networks of unequal, odd-sided polygons suppress charge crystallization at high interaction strength.

cond-mat.soft↗

arXiv2026

Deep learning-enhanced Lagrangian 3D Tracking of motile microorganisms

How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy -- for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior -- and thus opening new avenues for research in complex biological and ecological systems.

physics.bio-ph↗

arXiv2025

Confinement controls bacterial spreading at all scales

Navigation of microorganisms is controlled by internal processes ultimately sensitive to mechanical or chemical signaling encountered along the path. In many natural environments, such as porous soils or physiological ducts, motile species alternate between bulk and surface motion displaying in each case, distinct kinematics. This inherent complexity is key to many practical biological and ecological issues involving spreading and contamination, essential for understanding the spatiotemporal structuring of populations in their environment. However grasping the interplay between geometrical confinement and kinematics driven by internal biological responses remains poorly understood from a physical and biological standpoint. Here, we address this question through experimental and theoretical analysis in the heuristic situation of two parallel confining surfaces. We track wild-type E. coli - a model peritrichous flagellated bacterium - in 3D over extended periods of time. We obtain the first experimental measurements of the emerging diffusivity and bulk/surface residence times as a function of confinement height and the specific chiral kinematics at surfaces. All experimental results are quantitatively reproduced, without parametric adjustment, by a non-Markovian stochastic (BV) model that incorporates the internal biochemical memory carried by a phosphorylated protein switching the motor rotation. By matching the results with a Markovian (memoryless) companion model, we derive an analytical expression for the diffusivity and demonstrate how confining walls influence microbial long-range dispersion. This approach also provides a general conceptual basis for understanding how microorganisms navigate complex environments, in which their movement alternates between bulk and surfaces.

physics.bio-ph↗

arXiv2023

Non-equilibrium cluster-cluster aggregation in the presence of anchoring sites

Non-equilibrium cluster-cluster aggregation of particles diffusing in or at the cell membrane has been hypothesized to lead to domains of finite size in different biological contexts such as lipid rafts, cell adhesion complexes, or postsynaptic domains in neurons. In this scenario, the desorption of particles balances a continuous flux to the membrane, imposing a cut-off on possible aggregate sizes and giving rise to a stationary size distribution. Here, we investigate the case of non-equilibrium cluster-cluster aggregation in two dimensions where diffusing particles and/or clusters remain fixed in space at specific anchoring sites, which should be particularly relevant for synapses but may also be present in other biological or physical systems. Using an effective mean-field description of the concentration field around anchored clusters, we derive an expression for their average size as a function of parameters such as the anchoring site density. We furthermore propose and solve appropriate rate equations that allow us to predict the size distributions of both diffusing and fixed clusters. We confirm our results with particle-based simulations, and discuss potential implications for biological and physical systems.

cond-mat.soft↗