Tag: Agnese Callegari

Presentation by D. Bronte Ciriza at OSA-OMA-2021

Optical forces calculated on a sphere with the geometrical optics (left column) and the machine learning (center column) approaches. The difference between both approaches is shown in the column on the right, illustrating the removal of artefacts with the machine learning method.

Machine learning to enhance the calculation of optical forces in the geometrical optics approximation
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to OSA-OMA-2021, AF2D.2 Contribution
Date: 16 April
Time: 17 CEST

Short Abstract: We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation.

Extended Abstract:

Light can exert forces by exchanging momentum with particles. Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. Optical tweezers, which are instruments that, by tightly focusing a laser beam, are capable of confining particles in three dimensions, have become a common tool for manipulation of micro- and nano- particles, as well as a force and torque transducer with sensing capabilities at the femtonewton level. Optical tweezers have also been successfully employed to explore novel phenomena, including protein folding and molecular motors, or the optical forces and Brownian motion of 1D and 2D materials.

Numerical simulations play a fundamental role in the planning of experiments and in the interpretation of the results. In some basic cases for optical tweezers, the optical trap can be approximated by a harmonic potential. However, there are many situations where this approximation is insufficient, for example in the case of a particle escaping an optical trap, or for particles that are moving on an optical landscape but are not trapped. In these cases, a more complex treatment of the light-matter interaction is required for a more accurate calculation of the forces. This calculation is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way. Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis.

In this work, we explore the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is typically the case in experiments with micrometer-size particles. The optical field is described by a collection of N light rays and the momentum exchange between the rays and the particle is calculated employing the tools of geometrical optics. The limitation of considering a discrete N number of light rays introduces artifacts in the force calculation. We show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation. This is first demonstrated by training a neural network for the case of a spherical particle with 3 degrees of freedom accounting for the position of the particle. We show how the neural network improves the prediction of the force with respect to the initial training data that has been generated through the geometrical optics approach.
Starting from these results for 3 degrees of freedom, the work has been expanded to 9 degrees of freedom by including all the relevant parameters for the optical forces calculation considering also different refractive indexes, shapes, sizes, positions and orientations of the particle besides different numerical apertures of the objective that focuses the light.

This work proves machine learning as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.

Presentation by A. Callegari at OSA-OMA-2021

Simulation of clustering of Janus partices in an optical potential due to hydrodynamic fluxes.
Clustering of Janus Particles Under the Effect of Optical Forces Driven by Hydrodynamic Fluxes
Agnese Callegari, S. Masoumeh Mousavi, Iryna Kasianiuk, Denis Kasyanyuk, Sabareesh K P Velu, Luca Biancofiore, Giovanni Volpe
Submitted as: OSA-OMA-2021, AM1D.3 Contribution
Date: 12 April
Time: 15 CEST

Short Abstract
Hydrodynamic fluxes generated by Janus particles in an optical potential drive reversible clustering of colloids.

Extended Abstract

Self-organization entails the emergence of complex patterns and structures from relatively simple constituting building blocks. Phenomena such as flocking of birds and growth of bacterial colonies are examples of self-organization in nature. Also artificial microscopic systems feature similar forms of organization with the emergence of clusters, sometimes referred to as “living crystals”. In the past two decades, studies on self-organization focused on systems made of complex colloids with anisotropic surface, such as Janus particles. Depending on their surface material properties, Janus particles have been used in different fields for various applications such as self-assembly, microrheology and emulsion stabilization. Under certain conditions, Janus particles have the ability of self-propelling and behave as active Brownian particles; these active Janus particles might be used in future biomedical nano-devices for diagnostics, drug delivery and microsurgery. Studies on clustering of Janus particles have been performed by Palacci et al., who have shown the formation of living crystals in systems of light-activated Janus particles (Fe2O3-TPM) in hydrogen peroxide solution. Similarly, Buttinoni et al. demonstrated the clustering of light-activated Janus particles (carbon-SiO2) in a water-lutidine binary mixture. Other research groups have shown self-assembly and controlled crystal formations in a mixed system of light-activated Janus particles and passive colloids. In all these studies, a necessary ingredient for the clustering is the active nature of the particles. In systems of passive colloidal particles, crystallization was observed at the bottom of an attractive optical potential, close to the hard boundary during electrophoretic deposition, and in the presence of an external temperature gradient.

Here, we investigate the behavior of a system composed of Janus particles (silica microspheres half-coated with gold) close to a planar surface in the presence of an optical potential, and we experimentally demonstrate reversible clustering triggered by the presence of the optical field. Experimental results are compared and validated by numerical simulations, where the key ingredient for clustering is the presence of an attractive potential of hydrodynamic nature. In fact, the temperature gradient generated by the light absorption at the metallic patches on the Janus particles induces a local force field tangential to the surface of the Janus particle, which causes the fluid to slip at the surface of the particle. Because of the proximity of a planar surface, the flow pattern around the Janus particle is squeezed and results in a flow with a horizontal incoming radial component (parallel to the planar boundary) and outgoing vertical components (directed upwards from the wall). This thermophoretically-induced flow field affects the motion of other neighboring particles, so that a second nearby particle experiences an attractive hydrodynamic drag force toward the particle originating the flux. Clustering is confirmed also in mixtures of Janus particles and passive colloids (silica microspheres), where the hydrodynamic flux due to the Janus particles causes the clustering of the particles in the hybrid system and the formation of living crystals. As a further confirmation that the presence of Janus particles in the optical potential is crucial for the clustering, we show that a system with only non-Janus particles does not give rise to any clustering. We show experimentally that the clustering process is reversible, since the cluster starts to disassemble as soon as the optical potential is switched off.

Beyond their fundamental interest, the reported results are potentially relevant for various applications in the fields of self-assembly, targeted drug-delivery and bioremediation. For example, the possibility of forming clusters at a controllable distance from the minimum of a potential well offers a new route towards self-assembly near a target. Future work will be devoted to understanding how the clustering behavior can be controlled or altered by using more complex optical potentials.

Optical trapping and critical Casimir forces published in EPJP

Measuring the dynamics of colloids interacting with critical Casimir interaction via blinking optical tweezers: graphical representation of the optical traps.

Optical trapping and critical Casimir forces
Agnese Callegari, Alessandro Magazzù, Andrea Gambassi & Giovanni Volpe
The European Physical Journal Plus (EPJP), 136, 213 (2021)
doi: 10.1140/epjp/s13360-020-01020-4
arXiv: 2008.01537

Critical Casimir forces emerge between objects, such as colloidal particles, whenever their surfaces spatially confine the fluctuations of the order parameter of a critical liquid used as a solvent. These forces act at short but microscopically large distances between these objects, reaching often hundreds of nanometers. Keeping colloids at such distances is a major experimental challenge, which can be addressed by the means of optical tweezers. Here, we review how optical tweezers have been successfully used to quantitatively study critical Casimir forces acting on particles in suspensions. As we will see, the use of optical tweezers to experimentally study critical Casimir forces can play a crucial role in developing nano-technologies, representing an innovative way to realize self-assembled devices at the nano- and microscale.

Ordering of Binary Colloidal Crystals by Random Potentials published on Soft Matter

Ordering of binary colloidal crystals by random potentials

Ordering of Binary Colloidal Crystals by Random Potentials
André S. Nunes, Sabareesh K. P. Velu, Iryna Kasianiuk, Denys Kasyanyuk, Agnese Callegari, Giorgio Volpe, Margarida M. Telo da Gama, Giovanni Volpe & Nuno A. M. Araújo
Soft Matter 16, 4267-4273 (2020)
doi: https://doi.org/10.1039/D0SM00208A
arXiv: 1903.01579

Structural defects are ubiquitous in condensed matter, and not always a nuisance. For example, they underlie phenomena such as Anderson localization and hyperuniformity, and they are now being exploited to engineer novel materials. Here, we show experimentally that the density of structural defects in a 2D binary colloidal crystal can be engineered with a random potential. We generate the random potential using an optical speckle pattern, whose induced forces act strongly on one species of particles (strong particles) and weakly on the other (weak particles). Thus, the strong particles are more attracted to the randomly distributed local minima of the optical potential, leaving a trail of defects in the crystalline structure of the colloidal crystal. While, as expected, the crystalline ordering initially decreases with an increasing fraction of strong particles, the crystalline order is surprisingly recovered for sufficiently large fractions. We confirm our experimental results with particle-based simulations, which permit us to elucidate how this non-monotonic behavior results from the competition between the particle-potential and particle-particle interactions.

Clustering of Janus Particles published in Soft Matter

Clustering of Janus particles in optical potential driven by hydrodynamic fluxes

Clustering of Janus Particles in Optical Potential Driven by Hydrodynamic Fluxes
S. Masoumeh Mousavi, Iryna Kasianiuk, Denis Kasyanyuk, Sabareesh K. P. Velu, Agnese Callegari, Luca Biancofiore & Giovanni Volpe
Soft Matter 15(28), 5748—5759 (2019)
doi: 10.1039/C8SM02282H
arXiv: 1811.01989

Self-organisation is driven by the interactions between the individual components of a system mediated by the environment, and is one of the most important strategies used by many biological systems to develop complex and functional structures. Furthermore, biologically-inspired self-organisation offers opportunities to develop the next generation of materials and devices for electronics, photonics and nanotechnology. In this work, we demonstrate experimentally that a system of Janus particles (silica microspheres half-coated with gold) aggregates into clusters in the presence of a Gaussian optical potential and disaggregates when the optical potential is switched off. We show that the underlying mechanism is the existence of a hydrodynamic flow induced by a temperature gradient generated by the light absorption at the metallic patches on the Janus particles. We also perform simulations, which agree well with the experiments and whose results permit us to clarify the underlying mechanism. The possibility of hydrodynamic-flux-induced reversible clustering may have applications in the fields of drug delivery, cargo transport, bioremediation and biopatterning.

Ordering of binary colloidal crystals by random potentials on ArXiv

Ordering of binary colloidal crystals by random potentials

Ordering of binary colloidal crystals by random potentials
André S. Nunes, Sabareesh K. P. Velu, Iryna Kasianiuk, Denys Kasyanyuk, Agnese Callegari, Giorgio Volpe, Margarida M. Telo da Gama, Giovanni Volpe & Nuno A. M. Araújo
arXiv: 1903.01579

Structural defects are ubiquitous in condensed matter, and not always a nuisance. For example, they underlie phenomena such as Anderson localization and hyperuniformity, and they are now being exploited to engineer novel materials. Here, we show experimentally that the density of structural defects in a 2D binary colloidal crystal can be engineered with a random potential. We generate the random potential using an optical speckle pattern, whose induced forces act strongly on one species of particles (strong particles) and weakly on the other (weak particles). Thus, the strong particles are more attracted to the randomly distributed local minima of the optical potential, leaving a trail of defects in the crystalline structure of the colloidal crystal. While, as expected, the crystalline ordering initially decreases with increasing fraction of strong particles, the crystalline order is surprisingly recovered for sufficiently large fractions. We confirm our experimental results with particle-based simulations, which permit us to elucidate how this non-monotonic behavior results from the competition between the particle-potential and particle-particle interactions.

Controlling Colloidal Dynamics by Critical Casimir Forces published in Soft Matter

Controlling the dynamics of colloidal particles by critical Casimir forces

Controlling the dynamics of colloidal particles by critical Casimir forces
(Back cover article)
Alessandro Magazzù, Agnese Callegari, Juan Pablo Staforelli, Andrea Gambassi, Siegfried Dietrich & Giovanni Volpe
Soft Matter 15(10), 2152—2162 (2019)
doi: 10.1039/C8SM01376D
arXiv: 1806.11403

Critical Casimir forces can play an important role for applications in nano-science and nano-technology, owing to their piconewton strength, nanometric action range, fine tunability as a function of temperature, and exquisite dependence on the surface properties of the involved objects. Here, we investigate the effects of critical Casimir forces on the free dynamics of a pair of colloidal particles dispersed in the bulk of a near-critical binary liquid solvent, using blinking optical tweezers. In particular, we measure the time evolution of the distance between the two colloids to determine their relative diffusion and drift velocity. Furthermore, we show how critical Casimir forces change the dynamic properties of this two-colloid system by studying the temperature dependence of the distribution of the so-called first-passage time, i.e., of the time necessary for the particles to reach for the first time a certain separation, starting from an initially assigned one. These data are in good agreement with theoretical results obtained from Monte Carlo simulations and Langevin dynamics.

Funding:

ERC-founder H2020 European Research Council (ERC) Starting Grant ComplexSwimmers (677511).

Active Matter Influence on Coffee Rings published in Soft Matter

Active Matter Alters the Growth Dynamics of Coffee Rings

Active Matter Alters the Growth Dynamics of Coffee Rings
(Back cover article)
Tuğba Andaç, Pascal Weigmann, Sabareesh K. P. Velu, Erçağ Pinçe, Agnese Callegari, Giorgio Volpe, Giovanni Volpe & Agnese Callegari
Soft Matter 15(7), 1488—1496 (2019)
doi: 10.1039/C8SM01350K
arXiv: 1803.02619

How particles are deposited at the edge of evaporating droplets, i.e. the coffee ring effect, plays a crucial role in phenomena as diverse as thin-film deposition, self-assembly, and biofilm formation. Recently, microorganisms have been shown to passively exploit and alter these deposition dynamics to increase their survival chances under harshening conditions. Here, we show that, as the droplet evaporation rate slows down, bacterial mobility starts playing a major role in determining the growth dynamics of the edge of drying droplets. Such motility-induced dynamics can influence several biophysical phenomena, from the formation of biofilms to the spreading of pathogens in humid environments and on surfaces subject to periodic drying. Analogous dynamics in other active matter systems can be exploited for technological applications in printing, coating, and self-assembly, where the standard coffee-ring effect is often a nuisance.

Talk by A. Callegari at LAOP, Lima, 14 Nov 2018

Active Matter Alters the Growth Dynamics of Coffee Rings
Agnese Callegari, Tugba Andaç, Pascal Weigmann, Sabareesh K. Velu, Erçag Pince, Giorgio Volpe & Giovanni Volpe
LAOP – Latin America Optics & Photonics Congress, Lima, Peru
12-15 November 2018

Abstract: We show that bacterial mobility starts playing a major role in determining the growth dynamics of the edge of drying droplets, as the droplet evaporation rate slows down.