arXiv subjects

Maxime Lavaud

Publications and source records attributed to Maxime Lavaud.

9 recordsLinked to original sources

arXiv2026

Broadband molecular dynamics simulation of fluid inertial effects in confined Brownian motion

Hydrodynamic memory governs Brownian motion over a broad range of timescales, from acoustic wave propagation at short times to diffusive relaxation at long times. While confinement-induced corrections to Brownian diffusion are well established, how confinement modifies the full hydrodynamic response remains less explored. In this Letter, we use molecular-dynamics simulations of a neutrally buoyant colloidal particle in an explicit solvent to resolve the velocity autocorrelation function across a broad hydrodynamic spectrum. In the bulk, the simulations recover compressibility, added mass, the hydrodynamic long-time tail, and Stokes-Einstein diffusion without adjustable parameters. Near a rigid wall, the velocity correlations become anisotropic, their algebraic tails are modified, and the diffusion coefficients are reduced. Most importantly, the short-time dynamics reveals a pronounced enhancement of the effective added mass as the wall is approached. As such, the velocity autocorrelation function appears as a central quantity to bridge the zero-frequency mobility and the high-frequency inertial behaviour of a confined Brownian particle.

cond-mat.soft↗

arXiv2026

A label-free method to quantify early-stage amyloid aggregation via intrinsic phenylalanine fluorescence

The aggregation of amyloid-forming peptides is a dynamic, complex process that underlies their diverse biological activities, from physiological functions to disease-associated dysfunctions. While the structure of fibrillar end-products is well-characterized for most amyloids, the heterogeneous and often transient oligomers, likely key in cytotoxicity, remain poorly investigated, especially for peptides with low-yield aromatic residues. Here, by exploiting and developing flow induced dispersion analysis in both peak and front modes, we demonstrate that intrinsic phenylalanine fluorescence can be harnessed to quantify the conversion of diffusing monomers into non-diffusing oligomers and fibrils. This approach is validated using PSM$\alpha$3, a tryptophan- and tyrosine-free peptide from S. aureus, known for its fast amyloid fibrillation and key roles in bacterial virulence. We indeed characterize its low-molecular-weight oligomers, and size evolution from 2 to 10 nm over time. Importantly, to further validate the robustness of our front-mode approach, we apply it to hIAPP, a tyrosine-containing, tryptophan-free peptide, with critical outcome in type 2 diabetes. Our results overcome the limitations of traditional biochemical and biophysical amyloid assays by extending analysis from large oligomers and fibrils to small heterogeneous oligomers, under near-physiological conditions. This study thus offers a new analytical framework, thereby filling a critical gap in amyloid research, to probe the early stages of aggregation, key in the design of alternative therapeutics for amyloid-diseases.

cond-mat.soft↗

arXiv2025

The Hitchhiker's Guide to Differential Dynamic Microscopy

Over nearly two decades, Differential Dynamic Microscopy (DDM) has become a standard technique for extracting dynamic correlation functions from time-lapse microscopy data, with applications spanning colloidal suspensions, polymer solutions, active fluids, and biological systems. In its most common implementation, DDM analyzes image sequences acquired with a conventional microscope equipped with a digital camera, yielding time- and wavevector-resolved information analogous to that obtained in multi-angle Dynamic Light Scattering (DLS). With a widening array of applications and a growing, heterogeneous user base, lowering the technical barrier to performing DDM has become a central objective. In this tutorial article, we provide a step-by-step guide to conducting DDM experiments -- from planning and acquisition to data analysis -- and introduce the open-source software package fastDDM, designed to efficiently process large image datasets. fastDDM employs optimized, parallel algorithms that reduce analysis times by up to four orders of magnitude on typical datasets (e.g., 10,000 frames), thereby enabling high-throughput workflows and making DDM more broadly accessible across disciplines.

cond-mat.soft↗

arXiv2025

Enhanced diffusion over a periodic trap by hydrodynamic coupling to an elastic mode

In many physical systems, degrees of freedom are coupled \emph{via} hydrodynamic forces, even in the absence of Hamiltonian interactions. A particularly important and widespread example concerns the transport of microscopic particles in fluids near deformable boundaries. In such a situation, the influence of elastohydrodynamic couplings on Brownian motion remains to be understood. Unfortunately, the temporal and spatial scales associated with the thermal fluctuations of usual surfaces are often so small that their deformations are difficult to monitor experimentally, together with the much slower and larger particle motion at stake. Here, we propose a minimal model describing the hydrodynamic coupling of a colloidal particle to a fluctuating elastic mode, in presence of an external periodic potential. We demonstrate that the late-time diffusion coefficient of the particle increases with the compliance of the elastic mode. Remarkably, our results reveal that, and quantify how: i) spontaneous microscopic transport in complex environnements can be affected by soft boundaries -- a situation with numerous practical implications in nanoscale and biological physics; ii) the effects of fast and tiny surface deformations are imprinted over the long-term and large-distance colloidal mobility -- and are hence measurable in practice.

cond-mat.soft↗

arXiv2024

Bidirectional Mamba state-space model for anomalous diffusion

Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation is tested on the AnDi-2 challenge datasets among others. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture for anomalousdiffusion characterization.

cond-mat.soft↗

arXiv2024

Observation of Brownian elastohydrodynamic forces acting on confined soft colloids

Confined motions in complex environments are ubiquitous in microbiology. These situations invariably involve the intricate coupling between fluid flow, soft boundaries, surface forces and fluctuations. In the present study, such a coupling is investigated using a novel method combining holographic microscopy and advanced statistical inference. Specifically, the Brownian motion of softmicrometric oil droplets near rigid walls is quantitatively analyzed. All the key statistical observables are reconstructed with high precision, allowing for nanoscale resolution of local mobilities and femtonewton inference of conservative or non-conservative forces. Strikingly, the analysis reveals the existence of a novel, transient, but large, soft Brownian force. The latter might be of crucial importance for microbiological and nanophysical transport, target finding or chemical reactions in crowded environments, and hence the whole life machinery.

cond-mat.soft↗

arXiv2022

Numerical simulations of confined Brownian-yet-non-Gaussian motion

Brownian motion is a central scientific paradigm. Recently, due to increasing efforts and interests towards miniaturization and small-scale physics or biology, the effects of confinement on such a motion have become a key topic of investigation. Essentially, when confined near a wall, a particle moves much slower than in the bulk due to friction at the boundaries. The mobility is therefore locally hindered and space-dependent, which in turn leads to the apparition of so-called multiplicative noises, and associated non-Gaussianities which remain difficult to resolve at all times. Here, we exploit simple, optimized and efficient numerical simulations to address Brownian motion in confinement in a broadrange and quantitative way. To do so, we integrate the overdamped Langevin equation governing the thermal dynamics of a negatively-buoyant single spherical colloid within a viscous fluid confined by two rigid walls, including surface charges. From the produced large set of long random trajectories, we perform a complete statistical analysis and extract all the key quantities, such as the probability distributions in displacements and their main moments. In particular, we propose a novel method to compute high-order cumulants by reducing convergence problems, and employ it to efficiently characterize the inherent non-Gaussianity of the confined process.

cond-mat.stat-mech↗

arXiv2022

Non-Gaussian diffusion near surfaces

We study the diffusion of particles confined close to a single wall and in double-wall planar channel geometries where the local diffusivities depend on the distance to the boundaries. Displacement parallel to the walls is Brownian as characterized by its variance, but it is non-Gaussian having a non-zero fourth cumulant. Establishing a link with Taylor dispersion, we calculate the fourth cumulant and the tails of the displacement distribution for general diffusivity tensors along with potentials generated by either the walls or externally, for instance gravity. Experimental and numerical studies of the motion of a colloid in the direction parallel to the wall give measured fourth cumulants which are correctly predicted by our theory. Interestingly, contrary to models of Brownian-yet-non-Gaussian diffusion, the tails of the displacement distribution are shown to be Gaussian rather than exponential. All together, our results provide additional tests and constraints for the inference of force maps and local transport properties near surfaces.

cond-mat.soft↗

arXiv2020

Stochastic Inference of Surface-Induced Effects using Brownian Motion

Brownian motion in confinement and at interfaces is a canonical situation, encountered from fundamental biophysics to nanoscale engineering. Using the Lorenz-Mie framework, we optically record the thermally-induced tridimensional trajectories of individual microparticles, within salty aqueous solutions, in the vicinity of a rigid wall, and in the presence of surface charges. We construct the time-dependent position and displacement probability density functions, and study the non-Gaussian character of the latter which is a direct signature of the hindered mobility near the wall. Based on these distributions, we implement a novel, robust and self-calibrated multifitting method, allowing for the thermal-noise-limited inference of diffusion coefficients spatially-resolved at the nanoscale, equilibrium potentials, and forces at the femtoNewton resolution.

cond-mat.soft↗