Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis on ChemRxiv

Optical beam focused into the liquid: the tire particles are pushed away from the laser focus.
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
ChemRxiv: https://doi.org/10.33774/chemrxiv-2021-h59n1

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.

Presentation by L. Natali at Spatial Data Science 2020, 11 June 2021

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)
Improving epidemic testing and containment strategies using machine learning. 
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe.
Submitted to SDS2020
Date: 11 June
Time: 16:15 (CEST)

Abstract: 
Containment of epidemic outbreaks entails great societal and economic costs.  Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology, 2 035007 (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Press release on Machine learning can help slow down future pandemics

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)

The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics

The articles was also featured in:
AI ska bromsa framtidens pandemier Metal Supply (23/04/2021)
El papel de la inteligencia artificial para frenar futuras pandemias El Nacional.cat. (16/04/2021)
AI could be critical to preventing future pandemics – study Health Tech World. (16/04/2021)
Machine Learning Slows Down Future Pandemics MedIndia. (15/04/2021)
Machine Learning May Be Key to Avoiding the Next Possible Pandemic News18.com (15/04/2021)
Så kan AI bromsa nästa pandemi – svensk forskningförfinar testningen Computer Sweden (15/04/2021)
AI could prevent future pandemics Electronics360 (14/04/2021)
L’IA peut contribuer à limiter la propagation des infections lors des futures épidémies (étude) Ecofin Telecom. (14/04/2021)
Machine Learning can help slow down future pandemics:Study SocialNews.xyz (14/04/2021)
Machine learning can help slow down future pandemics —ScienceDaily Sortiwa Trending Viral News Portal (14/04/2021)
AI mot smittspridning Sveriges Radio Vetenskapsradion. (14/04/2021)

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.

Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials published on ACS Photonics

Counter-propagating laser beam intensity, represented and projected on the yz plane.
Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials
Paolo Polimeno, Francesco Patti, Melissa Infusino, Jonathan Sánchez, Maria A. Iatì, Rosalba Saija, Giovanni Volpe, Onofrio M. Maragò & Alessandro Veltri
ACS Photonics 7(5), 1262–1270 (2020)
doi: https://doi.org/10.1021/acsphotonics.0c00213

We investigate gain-assisted optical forces on dye-enriched silver nanoshell in the quasi-static limit by means of a theoretical/numerical approach. We demonstrate the onset of nonlinear optical trapping of these resonant nanostructures in a counter-propagating Gaussian beam configuration. We study the optical forces and trapping behavior as a function of wavelength, particle gain level, and laser power. We support the theoretical analysis with Brownian dynamics simulations that show how particle position locking is achieved at high gains in extended optical trapping potentials. Finally, for wavelengths blue-detuned with respect to the plasmon-enhanced resonance, we observe particle channeling by the standing wave antinodes due to gradient force reversal. This work opens perspectives for gain-assisted optomechanics where nonlinear optical forces are finely tuned to efficiently trap, manipulate, channel, and deliver an externally controlled nanophotonic system.

Intracavity Optical Trapping published in Nature Commun.

Intracavity Optical Trapping

Intracavity optical trapping of microscopic particles in a ring-cavity fiber laser
Fatemeh Kalantarifard, Parviz Elahi, Ghaith Makey, Onofrio M. Maragò, F. Ömer Ilday & Giovanni Volpe
Nature Communications 10, 2683 (2019)
doi: 10.1038/s41467-019-10662-7
arXiv: 1808.07831

Standard optical tweezers rely on optical forces arising when a focused laser beam interacts with a microscopic particle: scattering forces, pushing the particle along the beam direction, and gradient forces, attracting it towards the high-intensity focal spot. Importantly, the incoming laser beam is not affected by the particle position because the particle is outside the laser cavity. Here, we demonstrate that intracavity nonlinear feedback forces emerge when the particle is placed inside the optical cavity, resulting in orders-of-magnitude higher confinement along the three axes per unit laser intensity on the sample. This scheme allows trapping at very low numerical apertures and reduces the laser intensity to which the particle is exposed by two orders of magnitude compared to a standard 3D optical tweezers. These results are highly relevant for many applications requiring manipulation of samples that are subject to photodamage, such as in biophysics and nanosciences.

Review on Optical Tweezers published in J. Quant. Spectrosc. Rad. Transf.

Optical tweezers and their applications

Optical tweezers and their applications
Paolo Polimeno, Alessandro Magazzù, Maria Antonia Iata, Francesco Patti, Rosalba  Saija, Cristian Degli Esposti Boschi, Maria Grazia Donato, Pietro G. Gucciardi, Philip H. Jones, Giovanni Volpe & Onofrio M. Maragò
Journal of Quantitative Spectroscopy and Radiative Transfer 218(October 2018), 131—150 (2018)
DOI: 10.1016/j.jqsrt.2018.07.013

Optical tweezers, tools based on strongly focused light, enable optical trapping, manipulation, and characterisation of a wide range of microscopic and nanoscopic materials. In the limiting cases of spherical particles either much smaller or much larger than the trapping wavelength, the force in optical tweezers separates into a conservative gradient force, which is proportional to the light intensity gradient and responsible for trapping, and a non-conservative scattering force, which is proportional to the light intensity and is generally detrimental for trapping, but fundamental for optical manipulation and laser cooling. For non-spherical particles or at intermediate (meso)scales, the situation is more complex and this traditional identification of gradient and scattering force is more elusive. Moreover, shape and composition can have dramatic consequences for optically trapped particle dynamics. Here, after an introduction to the theory and practice of optical forces with a focus on the role of shape and composition, we give an overview of some recent applications to biology, nanotechnology, spectroscopy, stochastic thermodynamics, critical Casimir forces, and active matter.

Guide to Building Optical Tweezers published in JOSA B

A step-by-step guide to the realisation of advanced optical tweezers

A step-by-step guide to the realisation of advanced optical tweezers
Giuseppe Pesce, Giorgio Volpe, Onofrio M. Maragò, Philip H. Jones, Sylvain Gigan, Antonio Sasso & Giovanni Volpe
Journal of the Optical Society of America B 32(5), B84—B98 (2015)
DOI: 10.1364/JOSAB.32.000B84
arXiv: 1501.07894

Since the pioneering work of Arthur Ashkin, optical tweezers (OT) have become an indispensable tool for contactless manipulation of micro- and nanoparticles. Nowadays OT are employed in a myriad of applications demonstrating their importance. While the basic principle of OT is the use of a strongly focused laser beam to trap and manipulate particles, more complex experimental setups are required to perform novel and challenging experiments. With this article, we provide a detailed step-by-step guide for the construction of advanced optical manipulation systems. First, we explain how to build a single-beam OT on a homemade micro- scope and how to calibrate it. Improving on this design, we realize a holographic OT, which can manipulate independently multiple particles and generate more sophisticated wavefronts such as Laguerre–Gaussian beams. Finally, we explain how to implement a speckle OT, which permits one to employ random speckle light fields for deterministic optical manipulation.

Review on Optical Trapping of Nanostructures published in Nature Nanotech.

Optical trapping and manipulation of nanostructures

Optical trapping and manipulation of nanostructures
Onofrio M. Maragò, Philip H. Jones, Pietro Gucciardi, Giovanni Volpe & Andrea Ferrari
Nature Nanotechnology 8(11), 807—819 (2013)
DOI: 10.1038/nnano.2013.208

Optical trapping and manipulation of micrometre-sized particles was first reported in 1970. Since then, it has been successfully implemented in two size ranges: the subnanometre scale, where light–matter mechanical coupling enables cooling of atoms, ions and molecules, and the micrometre scale, where the momentum transfer resulting from light scattering allows manipulation of microscopic objects such as cells. But it has been difficult to apply these techniques to the intermediate — nanoscale — range that includes structures such as quantum dots, nanowires, nanotubes, graphene and two-dimensional crystals, all of crucial importance for nanomaterials-based applications. Recently, however, several new approaches have been developed and demonstrated for trapping plasmonic nanoparticles, semiconductor nanowires and carbon nanostructures. Here we review the state-of-the-art in optical trapping at the nanoscale, with an emphasis on some of the most promising advances, such as controlled manipulation and assembly of individual and multiple nanostructures, force measurement with femtonewton resolution, and biosensors.