Deep learning for optical tweezers
Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M. Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe and Mattias Goksör
Nanophotonics, 13(17), 3017-3035 (2024)
doi: 10.1515/nanoph-2024-0013
arXiv: 2401.02321
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. In this perspective, we show how cutting-edge deep learning approaches can remarkably improve optical tweezers, and explore 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.
Roadmap for optical tweezers
Giovanni Volpe, Onofrio M Maragò, Halina Rubinsztein-Dunlop, Giuseppe Pesce, Alexander B Stilgoe, Giorgio Volpe, Georgiy Tkachenko, Viet Giang Truong, Síle Nic Chormaic, Fatemeh Kalantarifard, Parviz Elahi, Mikael Käll, Agnese Callegari, Manuel I Marqués, Antonio A R Neves, Wendel L Moreira, Adriana Fontes, Carlos L Cesar, Rosalba Saija, Abir Saidi, Paul Beck, Jörg S Eismann, Peter Banzer, Thales F D Fernandes, Francesco Pedaci, Warwick P Bowen, Rahul Vaippully, Muruga Lokesh, Basudev Roy, Gregor Thalhammer-Thurner, Monika Ritsch-Marte, Laura Pérez García, Alejandro V Arzola, Isaac Pérez Castillo, Aykut Argun, Till M Muenker, Bart E Vos, Timo Betz, Ilaria Cristiani, Paolo Minzioni, Peter J Reece, Fan Wang, David McGloin, Justus C Ndukaife, Romain Quidant, Reece P Roberts, Cyril Laplane, Thomas Volz, Reuven Gordon, Dag Hanstorp, Javier Tello Marmolejo, Graham D Bruce, Kishan Dholakia, Tongcang Li, Oto Brzobohatý, Stephen H Simpson, Pavel Zemánek, Felix Ritort, Yael Roichman, Valeriia Bobkova, Raphael Wittkowski, Cornelia Denz, G V Pavan Kumar, Antonino Foti, Maria Grazia Donato, Pietro G Gucciardi, Lucia Gardini, Giulio Bianchi, Anatolii V Kashchuk, Marco Capitanio, Lynn Paterson, Philip H Jones, Kirstine Berg-Sørensen, Younes F Barooji, Lene B Oddershede, Pegah Pouladian, Daryl Preece, Caroline Beck Adiels, Anna Chiara De Luca, Alessandro Magazzù, David Bronte Ciriza, Maria Antonia Iatì, Grover A Swartzlander Jr
Journal of Physics: Photonics 2(2), 022501 (2023)
arXiv: 2206.13789
doi: 110.1088/2515-7647/acb57b
Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.
Faster and more accurate geometrical-optics optical force calculation using neural networks
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
ACS Photonics 10, 234–241 (2023)
doi: 10.1021/acsphotonics.2c01565
arXiv: 2209.04032
Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.
During the ActiveMatter meeting in Lisbon, Laura Natali and David Bronte Ciriza proposed a two hours activity on the fundamentals of effective communication. The activity was structured in an interactive way, and it began with a open discussion about the importance of communication, especially in science.
Then, the ESRs briefly described their research in a popular science style, so addressed to a broader public. The first hour concluded with a presentation about rules to keep in mind while communicating both in oral and written form.
Afterwards, a few examples among the written texts were selected and discussed with all the participants. The aim was to exchange feedback and suggestions on how to make the communication more effective. The feedback was the inspiration for everyone to review their communication example, and the final versions are being uploaded on the official twitter account @ActiveMatterITN.
Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Bronte Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
Environmental Science: Nano 9, 145 – 161 (2022)
ChemRxiv: https://doi.org/10.33774/chemrxiv-2021-h59n1
doi: https://doi.org/10.1039/D1EN00553G
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.
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.
Alessandro Magazzù and David Bronte Ciriza are visiting the Soft Matter Lab from 2 to 9 November 2020.
Alessandro is currently Post Doc at the IPCF-CNR Messina, Italy, and David is a PhD student at the same institution, and he is one of the ESRs (Early Stage Researchers) of the ActiveMatter MSCA-ITN-ETN.
They will be working on a neural network approach to the calculation of optical forces and torques on dielectric particles in the geometrical optics approximation.