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Stefano Palagi

Publications and source records attributed to Stefano Palagi.

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Patterned Substrates Unlock Self-Electrophoretic Phenomenon in Active Janus Microswimmers

Inert colloids half-coated with platinum (Pt) are a standard model of chemically powered active particles, yet the microscopic origins of their propulsion in hydrogen peroxide (H2O2) remain difficult to dissect experimentally. Whereas self-diffusiophoresis was the prevailing theory, self-electrophoresis has been more recently suggested as the main mechanism of propulsion. According to the latter mechanism, the pole-to-equator Pt-thickness gradient produced by directional metal deposition is sufficient to create anodic and cathodic regions on the metal cap and thereby generate an electric field sustained by H2O2 decomposition. Enhancing self-propulsion performance of such particles thus requires precise control over the Pt thickness distribution, which is currently not achievable with standard methods (e.g. evaporation or sputtering). Here, we propose a method to fabricate Janus active particles by assembling silica microspheres on patterned substrates containing spherical grooves whose depth and spacing set the degree of particle coating while simultaneously suppressing proximity-led defects (Pt bridges). The resulting particles exhibit a tunable platinum-thickness contrast, as verified by Focused-Ion-Beam cross-sections. In 2.5% H2O2, our results suggest that this control can significantly increase propulsion efficiency, while providing evidence indirectly supporting the hypothesis that self-electrophoresis is the dominant mechanism. These results demonstrate that our patterned-substrate route can enhance control over the catalyst deposition and enable novel Janus morphologies, allowing for more precise engineering of active colloids.

cond-mat.soft

Roadmap for Animate Matter

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts across various sectors. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field encouraging cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

cond-mat.mtrl-sci

Gait learning for soft microrobots controlled by light fields

Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing conditions. Albeit, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semi-synthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot's locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on light-controlled soft microrobots and probabilistic learning control.

cs.RO