David Bronte Ciriza nominated for a Student Paper Prize at the Biophotonics Congress

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).

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: 17:00 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.

Round Table Discussion on: Optics, Spectroscopy, Micro and Nanofabrication, and Nanotribology

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.

Optical tweezers in a dusty universe published in The European Physical Journal Plus

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.

Round Table Discussion on: Living Active Matter

As part of the experimental training, a second round-table discussion took place yesterday, 18 March 2021. The event centered around a discussion on the topic of « Living Active Matter » and featured four invited guests, all physicists, who have studied different topics and length scales relevant to living systems. The invited panel was composed of Aidan Brown from University of Edinburgh, Salima Rafai who works at CNRS in Grenoble, Eric Clément from PMMH-ESPCI in Paris and Benjamin Friedrich from TU Dresden, and was conducted by six of the students attending the training: Audrey Nsamela, David Bronte, Jérémie Bertrand, Ojus Satish Bagal, Daniela Peréz Guerrero and Dana Hassan, who first introduced each guest and then asked selected questions. From molecules and cells to tissues, organisms and populations, each guest had a particular expertise which made for a wide-ranging and interesting discussion.

A question on the evolutionary role of self-propulsion was met with an answer from Dr Brown, who, as obvious as it may seem, pointed out that organisms become “active” when whatever they need to survive is not in their immediate surroundings and must be found elsewhere. When Dr Rafai suggested that the micro-swimmers she studied were not converting their energy to motion optimally, Dr Brown pointed out that biological systems are optimized only in the sense that they are versatile and can adapt to a large number of situations or physical parameters, which is not something that can be captured by a single experiment. This goes to show that, when given the same set of facts, physicists and biologists will often interpret their observations differently, and that discussions between the two disciplines can be fruitful.

Dr Friedrich pointed out that the inherent complexity of biology was such that you could sometimes make progress by just looking and writing down how the processes unfold. He went on to explain that one of the bigger challenges biologists face is that many of these processes occurr below the resolution limit of the microscopes (the “diffraction barrier”) and can therefore not be observed by regular optical microscopes. Several panelists are excited about the coming of newly-designed, ground-breaking microscopes; devices that would use entangled photons to break the diffraction barrier. These new technologies could help not only the field of biology, but also encourage physicists and chemists to collaborate and create models of previously undiscovered mechanisms at the smaller scales. Deep learning is another tool that was alluded to by Dr Rafai as something to look forward to for image reconstruction.

Overall, we found the discussion very productive and we would like to thank once more the panelists for their insights and willingness to participate!

David Bronte Ciriza visits the Soft Matter Lab, Gothenburg, Sweden

From the 2nd to the 9th of November 2020, David Bronte Ciriza and Alessandro Magazzù (Post Doc at the CNR-IPCF Messina) have visited the Soft Matter Lab at the University of Gothenburg, Sweden. During their visit, they have been working on a project related to the use of neural networks to improve optical forces calculations. This visit has also served as networking opportunity with the researchers at the Gothenburg University, including the ones that participate in the ActiveMatter network.

Alessandro Magazzù and David Bronte Ciriza in Gothenburg. Foto by Aukut Argun

David Bronte Ciriza presents his PhD project at the ActiveMatter online meeting, 10 September 2020

On the 10th of September, the first online meeting between all the ESRs and PIs took place. During this meeting, David presented himself and introduced his future work at the CNR by means of this short video. The presentation was followed by time for questions and discussion with other members of the ActiveMatter network.

Are you wondering about what David did before joining the network? Do you want to know a little bit more about his project? Take a look at his presentation video!

David, ESR at the CNR, presents himself and introduces his work on the study of elongated active particles through optical forces.

ActiveMatter PIs+ESRs Online Meeting on 10 September 2020

The ActiveMatter PI+ESRs meeting took place on 10 September 2020. Because of the current travel restrictions and regulations imposed to hinder the spread of the CoViD-19 epidemics, the meeting was held online.

The aim of the meeting was to give an update to all the members on the progress of the ActiveMatter network.

Currently 12 of the 15 Early Stage Researchers (ESRs) have already been recruited and could started their project. During the meeting the ESRs had the opportunity to introduce themselves to the rest of the network and to present their research project.

The presentations of the ESRs have been uploaded on the Youtube channel of the ActiveMatter network and are available online.

Links to the individual presentations:
Liam Ruske, UOXF
Carolina van Baalen, ETH
Audrey Nsamela, ELVESYS
Danne van Roon, FC.ID
Chun-Jen Chen, UKONS
Sandrine Heijnen, UCL
Jesús Manuel Antúnez Dominguez, ELVESYS
David Bronte Ciriza, CNR
Laura Natali, UGOT
Ayten Gülce Bayram, UBIL
Davide Breoni, UDUS
Jérémie Mar Bertrand, EPFL

Pictures
(Screenshot by Caroline Beck Adiels)

(Screenshot by Giorgio Volpe)

(Screenshot by Giorgio Volpe)

(Screenshot by Agnese Callegari)

(Screenshot by Agnese Callegari)

David Bronte Ciriza joins the ActiveMatter ITN

David Bronte Ciriza started his training at CNR-IPCF Messina as one of the Early Stage Researchers (ESRs) of ActiveMatter ITN.

His work focuses on understanding the dynamics of artificial swimmers in optical tweezers, both theoretically and experimentally.

He will conduct his research under the supervision of Dr. Maria Antonia Iatì and Dr. Onofrio Maragò.