Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Brente Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
Tire and Road Wear Particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ~ 5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of < 5 μm MPs. Here we show that Raman Tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TWRPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman Tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.
The Soft Matter Lab is involved in six presentations at the OSA Biophotonic Congress: Optics in the Life Sciences 2021, topical meeting of Optical Manipulation and its Applications.
Moreover, three of the presentations were selected as finalists for the best student paper in the topical meeting of Optical Manipulation and its Applications.
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 toOSA-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.
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.
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
Hydrodynamic fluxes generated by Janus particles in an optical potential drive reversible clustering of colloids.
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
Agnese Callegari, Alessandro Magazzù, Andrea Gambassi & Giovanni Volpe
The European Physical Journal Plus (EPJP), 136, 213 (2021)
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
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)
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 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)
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
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
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 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)
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.