Raman Tweezers are used to detect tires and road wear particles in water. We analyze samples collected from a brake test platform, highlighting the presence of car tires and brake particles debris with sub-micrometric dimensions. (Featuring work from Dr Pietro G. Gucciardi, Prof Giovanni Volpe, and Dr Fabienne Lagarde).Raman tweezers for tire and road wear micro- and nanoparticles analysis
R. Gillibert, A. Magazzù, A. Callegari, D. Bronte-Ciriza, A. Foti, M. G. Donato, O. M. Maragò, G. Volpe, M. L. de La Chapelle, F. Lagarde and P. G. Gucciardi.
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 TRWPs 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.
A screenshot taken during the round table discussion of 20 September 2021.
On the 20th of September, the last round table of the Initial Training on Theoretical Methods took place. The discussion was let by the ESRs David, Sandrine, Liam, Carolina, Danne, and Laura. We were excited by the presence of an inspiring panel composed of Felix Ritort, Roberto Cerbino, Kirsty Wan, Fabio Giavazzi, Bernhard Mehlig, and François Nédélec.
The quote “If a system is in equilibrium, it’s probably death” ignited a lively and dynamic discussion around the topic of this final round table: “The universality of active matter: From biology to man-made models.”
Several topics were discussed ranging from active matter length scales and entropy production, to the equipartition theorem and universality. The session left us pondering about the definition of active matter: From single cells to the galaxy, where does the definition of active matter end? Our panelists conclude that it all depend on the question we ask ourselves. The round table was closed with a highlight of the most interesting avenues and opportunities in active matter, including the merge information and activity, realization of in vivo systems, as well as the manipulation of soft matter systems. Some inspiring words from one of the panelists let us realize: “We are the future of active matter.”
A screenshot taken during the round table discussion of 9 September 2021.
Today the second round table of the Initial Training on Theoretical Methods took place, entitled “Theoretical aspects of collective behavior”. The round table was hosted by ESRs David, Jesus, Ojus, Carolina, Alireza, Dana, and Umar. The inspiring group of speakers included Margarida Telo da Gama, Fernando Peruani, Nicoletta Gnan, and Claudio Maggi.
Many matters were discussed, ranging from the limits of collective behavior and the role of communication in emergence, to the compatibility between experiments and theory of collective behavior. Examples can be found in both natural and artificial environments, even combinations with varying degrees of active motion. This adds to the challenge of defining valuable, even if not accurate, models. At the core, collective behavior highlights how the system can be much more than just the sum of individual entities.
Comparison between the Geometrical Optics (GO) method and the Neural Network (NN) for the optical forces and torques calculation. The NN improvement in speed and accuracy could help to study the motion of active particles in optical landscapes. Image by D. Bronte Ciriza.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:ELS-XIX (2021) Date: 14 July Time: 12:50 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: 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. In all these fields, numerical simulations are of great help for validating theories, for the planning of experiments, and in the interpretation of the results. However, the calculation of the forces 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 show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation in the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is first demonstrated for the case of a spherical particle with 3 degrees of freedom and later expanded to 9 degrees of freedom by including all the relevant parameters involved in the optical forces calculation. Machine learning is proved 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.
New ActiveMatter logos: color and BW version. (Image by ActiveMatter ESRs)With a joint effort of the ESR students, a new logo for the ActiveMatter website was designed. The idea started as a handdrawing on a piece of paper and was quickly adapted to a better version with drawing softwares. More than 15 logos were suggested and submitted to a vote. The competition was fierce but we all came to agree on one of them and we are happy to present you the new official logo of the ITN ActiveMatter !
A screenshot of the talk at Liceo Archimede, Messina. Image by D. Bronte Ciriza.On the 5th of May 2021, David gave an outreach talk to last year’s high school students at Liceo Archimede in Messina. The talk, titled “Le macchine che imparano”, discussed the past, present, and future of artificial intelligence and provided the students with the basic tools to create their own machine learning models. Besides, aiming to bring science closer to the local community, an example of how machine learning is currently being used by researchers in Messina was explained.
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.David Bronte Ciriza has been nominated by the Optical Society of America for a Student Paper Prize for Optical Manipulation and its Applications among three other finalists. He will present his work on Machine Learning to Enhance the Calculation of Optical Forces in the Geometrical Optics Approximation at the 2021 OSA Biophotonics Congress: Optics in Life Sciences.
This work, which is the fruit of a collaboration between the groups at the CNR-IPCF and the University of Gothenburg, shows the potential of machine learning for faster and more accurate optical forces calculations. The extended abstract can be found here.
Based on the oral presentations of the finalists, the jury will select the winner. David will present on April 16th at 5:00 pm (CEST).
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: 17:00 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 calculation of the optical forces 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.
On Tuesday 23 March the fourth round table of the initial training on experimental methods for active matter took place. The topic of the round table was “Optics, Spectroscopy, Micro and Nanofabrication, and Nanotribology”, and the discussion was led by Ayten Gülce Bayram , David Bronte Ciriza, Dana Hassan, Carolina van Baalen and Jesús Manuel Antúnez Domínguez. The panelists included Maria Grazia Donato, Pietro Gucciardi, Antonino Foti, Shivaprakash Ramakrishna, and Felix Holzner.
The importance of the topic of the round table to the field of active matter was motivated by the panelists from different perspectives. The discussion ranged from the main differences and challenges that come along with working on the micro- and nanoscale, to how changing the dimensions of your system allows one to change the properties of a system’s response, as well as the challenges involved in bringing a product idea to the market. The main conclusion was that the nanoscale is exciting, but the smaller you get, the greater the challenge.
Pictorial representation of space tweezers, space applications of optical tweezers. Interplanetary or planetary dust can be collected and investigated directly in situ (open space or extraterrestrial surfaces). The inset represents a closeup of a grain of interplanetary dust trapped by a single-beam optical tweezers. (Image by Alessandro Magazzù)Optical tweezers in a dusty universe P. Polimeno, A. Magazzù, M. A. Iatì, R. Saija, L. Folco, D. Bronte Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C. Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini, V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi & O. M. Maragò
Eur. Phys. J. Plus 136, 339 (2021)
doi: 10.1140/epjp/s13360-021-01316-z
Abstract: Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate, and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications of this contactless technique have been developed in many fields. Despite this progress, optical trapping applications to planetary exploration are still to be developed. Here we describe how optical tweezers can be used to trap and characterize extraterrestrial particulate matter. In particular, we exploit light scattering theory in the T-matrix formalism to calculate radiation pressure and optical trapping properties of a variety of complex particles of astrophysical interest. Our results open perspectives in the investigation of extraterrestrial particles on our planet, in controlled laboratory experiments, aiming for space tweezers applications: optical tweezers used to trap and characterize dust particles in space or on planetary bodies surface.
