arXiv subjects

Giovanni Volpe

Publications and source records attributed to Giovanni Volpe.

At least 37 records · Page 2Linked to original sources

arXiv2024

Transverse optical gradient force in untethered rotating metaspinners

Nanostructured dielectric metasurfaces offer unprecedented opportunities to control light-matter momentum exchange, and thereby the forces and torques that light can exert on matter. Here we introduce optical metasurfaces as components of ultracompact untethered microscopic metaspinners capable of efficient light-induced rotation in a liquid environment. Illuminated by weakly focused light, a metaspinner generates torque via photon recoil through the metasurfaces ability to bend light towards high angles despite their sub-wavelength thickness, thereby creating orbital angular momentum. We find that a metaspinner is subject to an anomalous transverse lateral optical gradient force that acts in concert with the classical gradient force. Consequently, when two or more metaspinners are trapped together in a laser beam, they collectively orbit the optical axis in the opposite direction to their spinning motion, in stark contrast to rotors coupled through hydrodynamic or mechanical interactions. The metaspinners delineated herein not only serve to illustrate the vast possibilities of utilizing optical metasurfaces for fundamental exploration of optical torques, but they also represent potential building-blocks of artificial active matter systems, light-driven micromachinery, and general-purpose optomechanical devices.

physics.optics↗

arXiv2024

Roadmap on machine learning glassy dynamics

Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This perspective article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present recent successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand the fundamental mechanisms at play in glass-forming liquids. To foster a collaborative community effort, this article also introduces the ``GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. We propose critical metrics to compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our theoretical understanding of glassy liquids.

cond-mat.soft↗

arXiv2024

Microscopic Geared Mechanisms

The miniaturization of mechanical machines is critical for advancing nanotechnology and reducing device footprints. Traditional efforts to downsize gears and micromotors have faced limitations at around 0.1 mm for over thirty years due to the complexities of constructing drives and coupling systems at such scales. Here, we present an alternative approach utilizing optical metasurfaces to locally drive microscopic machines, which can then be fabricated using standard lithography techniques and seamlessly integrated on the chip, achieving sizes down to tens of micrometers with movements precise to the sub-micrometer scale. As a proof of principle, we demonstrate the construction of microscopic gear trains powered by a single driving gear with a metasurface activated by a plane light wave. Additionally, we develop a versatile pinion and rack micromachine capable of transducing rotational motion, performing periodic motion, and controlling microscopic mirrors for light deflection. Our on-chip fabrication process allows for straightforward parallelization and integration. Using light as a widely available and easily controllable energy source, these miniaturized metamachines offer precise control and movement, unlocking new possibilities for micro- and nanoscale systems.

physics.optics↗

arXiv2024

Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial

Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models (DDPMs) from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions. We provide the theoretical background, mathematical derivations, and a detailed Python code implementation using PyTorch, along with techniques to enhance model performance.

eess.IV↗

arXiv2024

Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles

In order to relate nanoparticle properties to function, fast and detailed particle characterization, is needed. The ability to characterize nanoparticle samples using optical microscopy techniques has drastically improved over the past few decades; consequently, there are now numerous microscopy methods available for detailed characterization of particles with nanometric size. However, there is currently no ``one size fits all'' solution to the problem of nanoparticle characterization. Instead, since the available techniques have different detection limits and deliver related but different quantitative information, the measurement and analysis approaches need to be selected and adapted for the sample at hand. In this tutorial, we review the optical theory of single particle scattering and how it relates to the differences and similarities in the quantitative particle information obtained from commonly used microscopy techniques, with an emphasis on nanometric (submicron) sized dielectric particles. Particular emphasis is placed on how the optical signal relates to mass, size, structure, and material properties of the detected particles and to its combination with diffusivity-based particle sizing. We also discuss emerging opportunities in the wake of new technology development, with the ambition to guide the choice of measurement strategy based on various challenges related to different types of nanoparticle samples and associated analytical demands.

physics.optics↗

arXiv2024

Critical Casimir levitation of colloids above a bull's-eye pattern

Critical Casimir forces emerge among particles or surfaces immersed in a near-critical fluid, with the sign of the force determined by surface properties and with its strength tunable by minute temperature changes. Here, we show how such forces can be used to trap a colloidal particle and levitate it above a substrate with a bull's-eye pattern consisting of a ring with surface properties opposite to the rest of the substrate. Using the Derjaguin approximation and mean-field calculations, we find a rich behavior of spherical colloids at such a patterned surface, including sedimentation towards the ring and levitation above the ring (ring levitation) or above the bull's-eye's center (point levitation). Within the Derjaguin approximation, we calculate a levitation diagram for point levitation showing the depth of the trapping potential and the height at which the colloid levitates, both depending on the pattern properties, the colloid size, and the solution temperature. Our calculations reveal that the parameter space associated with point levitation shrinks if the system is driven away from a critical point, while, surprisingly, the trapping force becomes stronger. We discuss the application of critical Casimir levitation for sorting colloids by size and for determining the thermodynamic distance to criticality. Our results show that critical Casimir forces provide rich opportunities for controlling the behavior of colloidal particles at patterned surfaces.

cond-mat.soft↗

arXiv2024

Programmable self-assembly of core-shell ellipsoids at liquid interfaces

Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly behaviour due to quadrupolar capillary interactions induced by meniscus deformation. These interactions cause particles to attract each other in either tip-to-tip or side-to-side configurations. However, controlling their interfacial self-assembly is challenging because it is difficult to predict which of these two states will be preferred. In this study, we demonstrate that introducing a soft shell around hard ellipsoidal particles provides a means to control the self-assembly process, allowing us to switch the preferred configuration between these states. We study their interfacial self-assembly and find that pure ellipsoids without a shell consistently form a "chain-like" side-to-side assembly, regardless of aspect ratio. In contrast, core-shell ellipsoids transition from "flower-like" tip-to-tip to "chain-like" side-to-side arrangements as their aspect ratios increase. The critical aspect ratio for transitioning between these structures increases with shell-to-core ratios. Our experimental findings are corroborated by theoretical calculations and Monte Carlo simulations, which map out the phase diagram of thermodynamically preferred self-assembly structures for core-shell ellipsoids as a function of aspect ratio and shell-to-core ratios. This study shows how to program the self-assembly of anisotropic particles by tuning their physicochemical properties, allowing the deterministic realization of distinct structural configurations.

cond-mat.soft↗

arXiv2024

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↗

arXiv2024

Tutorial for the growth and development of Myxococcus xanthus as a Model System at the Intersection of Biology and Physics

Myxococcus xanthus is a unicellular organism whose cells possess the ability to move and communicate, leading to the emergence of complex collective properties and behaviours. This has made it an ideal model system to study the emergence of collective behaviours in interdisciplinary research efforts lying at the intersection of biology and physics, especially in the growing field of active matter research. Often, challenges arise when setting up reliable and reproducible culturing protocols. This tutorial provides a clear and comprehensive guide on the culture, growth, development, and experimental sample preparation of \textit{M. xanthus}. Additionally, it includes some representative examples of experiments that can be conducted using these samples, namely motility assays, fruiting body formation, predation, and elasticotaxis.

physics.bio-ph↗

arXiv2024

Crystallization and topology-induced dynamical heterogeneities in soft granular clusters

Soft-granular media, such as dense emulsions, foams or tissues, exhibit either fluid- or solid-like properties depending on the applied external stresses. Whereas bulk rheology of such materials has been thoroughly investigated, the internal structural mechanics of finite soft-granular structures with free interfaces is still poorly understood. Here, we report the spontaneous `crystallization' and `melting' inside a model soft granular cluster -- a densely packed aggregate of $N\sim 30-40$ droplets engulfed by a fluid film -- subject to a varying external flow. We develop new machine learning tools to track the internal rearrangements in the quasi-2D cluster as it transits a sequence of constrictions. As the cluster relaxes from a state of strong mechanical deformations, we find differences in the dynamics of the grains within the interior of the cluster and those at its rim, with the latter experiencing larger deformations and less frequent rearrangements, effectively acting as an elastic membrane around a fluid-like core. We conclude that the observed structural-dynamical heterogeneity results from an interplay of the topological constrains, due to the presence of a closed interface, and the internal solid-fluid transitions. We discuss universality of such behavior in various types of finite soft granular structures, including biological tissues.

cond-mat.soft↗

arXiv2024

Nanoalignment by Critical Casimir Torques

The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.

cond-mat.soft↗

arXiv2024

Deep Learning for Optical Tweezers

Optical tweezers exploit light--matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and analysis of the trapped objects, often outperforming classical methods thanks to the higher computational speed and versatility of deep learning. Here, we review how deep learning has already remarkably improved optical tweezers, while exploring the exciting, new future possibilities enabled by this dynamic synergy. Furthermore, we offer guidelines on integrating deep learning with optical trapping and optical manipulation in a reliable and trustworthy way.

physics.optics↗

arXiv2023

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning

The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble Model for Accelerated Sensing (LEMAS) accelerates the response of a state-of-the-art optical plasmonic hydrogen sensor by up to a factor of 40 in an oxygen-free inert gas environment, by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Furthermore, it eliminates the pressure dependence of the response intrinsic to metal hydride-based sensors, while leveraging their ability to operate in oxygen-starved environments that are proposed to be used for inert gas encapsulation systems of hydrogen installations. Moreover LEMAS provides a measure for the uncertainty of the predictions that is pivotal for safety-critical sensor applications. Our results thus advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.

physics.comp-ph↗

arXiv2023

Destructive effect of fluctuations on the performance of a Brownian gyrator

The Brownian gyrator (BG) is often called a minimal model of a nano-engine performing a rotational motion, judging solely upon the fact that in non-equilibrium conditions its torque, angular momentum ${\cal L}$ and angular velocity $\cal W$ have non-zero mean values. For a time-discretized model, which is most adapted for the analysis of an essentially discrete-time data garnered in experiments or numerical simulations, we calculate the previously unknown probability density functions (PDFs) of ${\cal L}$ and $\cal W$. For finite time-step $δt$, the PDF of ${\cal L}$ has exponential tails and all moments are therefore well-defined, but the noise-to-signal ratio can attain big values for small $δt$. Conversely, the PDF of ${\cal W}$ exhibits heavy power-law tails and its mean ${\cal W}$ is the only existing moment. The BG is therefore not an engine in the common sense: it does not exhibit regular rotations on each run and its fluctuations are not only a minor nuisance -- on contrary, their effect is completely destructive for the performance. Our theoretical predictions are confirmed by numerical simulations and experimental data. We discuss some plausible improvements

cond-mat.stat-mech↗

arXiv2023

Deep-learning-powered data analysis in plankton ecology

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

physics.bio-ph↗

arXiv2023

Dual-angle interferometric scattering microscopy for optical multiparametric particle characterization

Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that hydrodynamic radius relates to the outer particle radius while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by optically differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single particle level.

physics.optics↗

arXiv2023

Optically Driven Janus Micro Engine with Full Orbital Motion Control

Microengines have shown promise for a variety of applications in nanotechnology, microfluidics and nanomedicine, including targeted drug delivery, microscale pumping, and environmental remediation. However, achieving precise control over their dynamics remains a significant challenge. In this study, we introduce a microengine that exploits both optical and thermal effects to achieve a high degree of controllability. We find that in the presence of a strongly focused light beam, a gold-silica Janus particle becomes confined at the stationary point where the optical and thermal forces balance. By using circularly polarized light, we can transfer angular momentum to the particle breaking the symmetry between the two forces and resulting in a tangential force that drives directed orbital motion. We can simultaneously control the velocity and direction of rotation of the particle changing the ellipticity of the incoming light beam, while tuning the radius of the orbit with laser power. Our experimental results are validated using a geometrical optics phenomenological model that considers the optical force, the absorption of optical power, and the resulting heating of the particle. The demonstrated enhanced flexibility in the control of microengines opens up new possibilities for their utilization in a wide range of applications, encompassing microscale transport, sensing, and actuation.

physics.optics↗

arXiv2023

Environmental Memory Boosts Group Formation of Clueless Individuals

The formation of groups of interacting individuals improves performance and fitness in many decentralised systems, from micro-organisms to social insects, from robotic swarms to artificial intelligence algorithms. Often, group formation and high-level coordination in these systems emerge from individuals with limited information-processing capabilities implementing low-level rules of communication to signal to each other. Here, we show that, even in a community of clueless individuals incapable of processing information and communicating, a dynamic environment can coordinate group formation by transiently storing memory of the earlier passage of individuals. Our results identify a new mechanism of indirect coordination via shared memory that is primarily promoted and reinforced by dynamic environmental factors, thus overshadowing the need for any form of explicit signalling between individuals. We expect this pathway to group formation to be relevant for understanding and controlling self-organisation and collective decision making in both living and artificial active matter in real-life environments.

cond-mat.soft↗