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Alejandro Jurado

Publications and source records attributed to Alejandro Jurado.

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Self-propelled particles driven by light

Recent advances in the field of active soft matter promise a lot. Both, experimental advances and theoretical understanding point towards new material classes in reach, for example self-healing materials that might switch their properties from elastic to solid easily or switch their macroscopic shapes. All these materials require an active force to propel parts of themselves on the micrometer scale. While chemical fuels are often used to generate these active forces, applying energy in a simple and continuous way remains unsolved. Here we explore using light as such an energy source. Overall, generating active driven, self-propelled particles is hence not only of great interest but also a general challenge. Moreover, controlling such particles even within living tissue would open new worlds, for example to enable specific drug delivery or the design of micro-robots. One recently proposed method to establish light driven self propelled particles is to create specific shaped and transparent objects, that move when illuminated with homogeneous light. In these particles, the refraction of the light leads to a momentum transfer, which then drives the active movement. Here, we show both in simulation and experiments that the production of such particles is possible and demonstrate the feasibility of this propulsion effect, while investigating different shapes. Our experiments show that breaking the shape-symmetry of the particles creates a refraction-based propulsion under homogeneous illumination. Subsequent simulations reveal that total reflection leads to the largest momentum transfer among all different geometries considered. Overall, our study introduces the proof-of-principle for refraction-propelled particles, which has the potential to benefit many fields of study including cellular behaviour, collective dynamics and the understanding of disease mechanisms.

physics.bio-ph

AdoptODE: Fusion of data and expert knowledge for modeling dynamical systems

Building a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example concerning model dimensions or its relevant constraints. Here we present a novel model-building approach -- adoptODE -- augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. AdoptODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative low-dimensional model, which can fit existing data and predict novel experimental outcomes. We validate our method on five, quite different problem domains. (1) Kolmogorov model: Lotka Volterra model where we show the application of adoptODE to continuous-time Markov processes and the performance of adoptODE when working with noisy data. (2) Particle model: Interactive N-body, is a demonstration of the scalability of adoptODE and that even interactive system with a high number of elements can be reconstructed with high precision. (3) Excitable media (heart dynamics) by the Bueno\-Orovio\-Cherry\-Fenton model: AdoptODE can reconstruct the parameters of a high-dimensional model and fields for diffusion driven system for chaotic behaviour. (4) Fluid dynamics: Rayleigh-B\'enard Convection where a complete unknown field, the temperature, can be extracted from only velocity data. (5) New experimental data of Zebrafish embryogenesis: This is a case where we extend existing models with new variables.

physics.data-an