Author: Agnese Callegari

Presentation by A. Callegari at SPIE-OTOM, San Diego, 22 August 2024

One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)
Active Matter Experiments with Toy Robots
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 18 – 22 August 2024
Date: 22 August 2024
Time: 3:00 PM – 3:15 PM
Place: Conv. Ctr. Room 6D

Active matter is based on concepts of nonequilibrium thermodynamics applied to the most diverse disciplines. Active Brownian particles, unlike their passive counterparts, self-propel and give rise to complex behaviors distinctive of active matter. As the field is relatively recent, active matter still lacks curricular inclusion. Here, we propose macroscopic experiments using Hexbugs, a commercial toy robot, demonstrating effects peculiar of active systems, such as the setting into motion of passive objects via active particles, the sorting of active particles based on their mobility and chirality. Additionally, we provide a demonstration of Casimir-like attraction between planar objects mediated by active particles.

Reference
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe, Playing with Active Matter, arXiv: 2209.04168

Poster by A. Callegari at SPIE-OTOM, San Diego, 19 August 2024

Trajectory of a hexagonal cluster formed by a transparent particle (blu circle) and six light-absorbing particles (red circles) in a traveling sinusoidal optical pattern, in the absence of thermal noise. The direction of the motion of the optical pattern is given by the arrow. The trajectory’s duration is 30 s. (Image by A. Bergsten.)
Chiral active molecules formation via non-reciprocal interactions
Agnese Callegari, Niphredil Klint, John Klint, Alfred Bergsten, Alex Lech, and Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 18 – 22 August 2024
Date: 19 August 2024
Time: 5:30 PM – 7:00 PM
Place: Conv. Ctr. Exhibit Hall A

In 2019, Schmidt et al. demonstrated light-induced assembly of active colloidal molecules. They used two types of colloidal particles in a water-lutidine mixture: one transparent and one slightly absorbing light. In their experiment, this determined a non-reciprocal interaction between light-absorbing and transparent particles and promoted active molecule formation controlled by light. Beyond experimental details, we here explore the effects of this non-reciprocal interaction solely, showing its role in active molecule formation and self-propulsion. Simulation allows for the study of complex light profiles, enabling precise control over assembly and propulsion properties, relevant for targeted microscopic delivery.

Poster by A. Callegari at SPIE-OTOM, San Diego, 19 August 2024

Simplified sketch of the neural network used for the simulations of intracavity optical trapping. (Image by A. Callegari.)
Neural networks for intracavity optical trapping
Agnese Callegari, Mathias Samuelsson, Antonio Ciarlo, Giuseppe Pesce, David Bronte Ciriza, Alessandro Magazzù, Onofrio M. Maragò, Antonio Sasso, and Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 18 – 22 August 2024
Date: 19 August 2024
Time: 5:30 PM – 7:00 PM
Place: Conv. Ctr. Exhibit Hall A

Intracavity optical tweezers have been proven successful for trapping microscopic particles at very low average power intensity – much lower than the one in standard optical tweezers. This feature makes them particularly promising for the study of biological samples. The modelling of such systems, though, requires time-consuming numerical simulations that affect its usability and predictive power. With the help of machine learning, we can overcome the numerical bottleneck – the calculation of optical forces, torques, and losses – and reproduce, in simulation, the results in the literature and generalize to the case of counterpropagating-beams intracavity optical trapping.

Poster by A. Callegari at SPIE-OTOM, San Diego, 19 August 2024

Schematic of the scattering of a light ray on a Janus particle. (Image by A. Callegari.)
Janus particles in a travelling optical landscape
Agnese Callegari, Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 18 – 22 August 2024
Date: 19 August 2024
Time: 5:30 PM – 7:00 PM
Place: Conv. Ctr. Exhibit Hall A

Janus particles possess dual properties that makes them very versatile for soft and active matter applications. Modeling their interaction with light, including optical force and torque, presents challenges. We present here a model of spherical, metal-coated Janus particles in the geometric optics approximation. Via an extension of the Optical Tweezers Geometrical Optics (OTGO) toolbox, we calculate optical forces, torques, and absorption. Through numerical simulation, we demonstrate control over Janus particle dynamics in traveling-wave optical landscapes by adjusting speed and periodicity.

Presentation by M. Selin at SPIE-ETAI, San Diego, 19 August 2024

3d Visualization of the full Minitweezers 2.0 system. (Illustration by M. Selin.)
Integrating real-time deep learning for automation of optical tweezers experiments
Martin Selin
SPIE-ETAI, San Diego, CA, USA, 18 – 22 August 2024
Date: 19 August 2024
Time: 4:10 PM – 4:25 PM
Place: Conv. Ctr. Room 6D

The perhaps most widely used tool for measuring forces and manipulating particles at the micro and nano-scale are optical tweezers which have given them widespread adoption in physics, chemistry and biology. Despite advancements in computer interaction driven by large-scale generative AI models, experimental sciences—and optical tweezers in particular—remain predominantly manual and knowledge-intensive, owing to the specificity of methods and instruments. Here, we demonstrate how integrating the components of optical tweezers—laser, motor, microfluidics, and camera—into a single software simplifies otherwise challenging experiments by enabling automation through the integration of real-time analysis with deep learning. We highlight this through a DNA pulling experiment, showcasing automated single molecule force spectroscopy and intelligent bond detection, and an investigation into core-shell particle behavior under varying pH and salinity, where deep learning compensates for experimental drift. We conclude that automating experimental procedures increases reliability and throughput, while also opening up the possibility for new types of experiments.

Presentation by A. Callegari at SPIE-ETAI, San Diego, 19 August 2024

Focused rays scattered by an ellipsoidal particles (left). Optical torque along y calculated in the x-y plane using ray scattering with a grid of 1600 rays (up, right) and using a trained neural network (down, right). (Image by the Authors of the manuscript.)
Optical forces and torques in the geometrical optics approximation calculated with neural networks
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria Antonia Iatì, Giovanni Volpe, and Onofrio M. Maragò
SPIE-ETAI, San Diego, CA, USA, 18 – 22 August 2024
Date: 19 August 2024
Time: 1:55 PM – 2:10 PM
Place: Conv. Ctr. Room 6D

Optical tweezers manipulate microscopic objects with light by exchanging momentum and angular momentum between particle and light, generating optical forces and torques. Understanding and predicting them is essential for designing and interpreting experiments. Here, we focus on geometrical optics and optical forces and torques in this regime, and we employ neural networks to calculate them. Using an optically trapped spherical particle as a benchmark, we show that neural networks are faster and more accurate than the calculation with geometrical optics. We demonstrate the effectiveness of our approach in studying the dynamics of systems that are computationally “hard” for traditional computation.

Reference
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò, Faster and more accurate geometrical-optics optical force calculation using neural networks, ACS Photonics 10, 234–241 (2023)

Invited Presentation by M. Selin at SPIE-OTOM, San Diego, 18 August 2024

3d Visualization of the full Minitweezers 2.0 system. (Illustration by M. Selin.)
From stretching DNA to probing polymer stiffness: expanding experimental reach with automated optical tweezers
Martin Selin
SPIE-OTOM, San Diego, CA, USA, 18 – 22 August 2024
Date: 18 August 2024
Time: 12:15 PM – 12:45 PM
Place: Conv. Ctr. Room 6D

Optical tweezers have become ubiquitous tools in science with use in disciplines ranging from biology to physics, chemistry, and material sciences with thousands of users around the world and a continuously growing number of applications. Here we show how a specially designed instrument, called miniTweezers2.0, can be made both highly versatile and user friendly. We demonstrate the system on three different experiments, which thanks to the close integration of the various parts of the tweezers into a single software are performed fully autonomously. The first experiment involves DNA stretching, a fundamental single molecule force spectroscopy experiment. The second experiment involved the stretching of red blood cells, which can be used to gauge the membrane stiffness of the cells. Lastly, we investigate the interaction between core-shell particles in various environments. Showing how the soft polymer layer extends, or contracts depending on pH and salinity. Our work show potential of automated and versatile optical tweezers systems in advancing our understanding of nano and micro-scale systems.

Keynote Presentation by G. Volpe at SPIE-MNM, San Diego, 18 August 2024

(Image by A. Argun)
Deep Learning for Imaging and Microscopy
Giovanni Volpe
SPIE-MNM, San Diego, CA, USA, 18 – 22 August 2024
Date: 18 August 2024
Time: 10:25 AM – 11:00 AM
Place: Conv. Ctr. Room 6F

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 18-22 August 2024

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 18-22 August 2024, with the presentations listed below.

Giovanni Volpe is also panelist in the panel discussion:

  • Towards the Utilization of AI
    21 August 2024 • 3:45 PM – 4:45 PM PDT | Conv. Ctr. Room 2

Crystallization and topology-induced dynamical heterogeneities in soft granular clusters published in Physical Review of Research

Scheme of the microfluidic system for the production of clusters of a soft granular medium, and Snapshots of the cluster at different times corresponding to different sections of the channel. (Image by the Authors of the manuscript.)
Crystallization and topology-induced dynamical heterogeneities in soft granular clusters
Michal Bogdan, Jesus Pineda, Mihir Durve, Leon Jurkiewicz, Sauro Succi, Giovanni Volpe, Jan Guzowski
Physical Review of Research, 6, L032031 (2024)
DOI: 10.1103/PhysRevResearch.6.L032031
arXiv: 2302.05363

Soft-granular media, such as dense emulsions, foams or tissues, exhibit either fluid- or solidlike properties depending on the applied external stresses. Whereas bulk rheology of such materials has been thoroughly investigated, the internal structural mechanics of finite soft-granular structures with free interfaces is still poorly understood. Here, we report the spontaneous crystallization and melting inside a model soft granular cluster—a densely packed aggregate of N~30-40 droplets engulfed by a fluid film—subject to a varying external flow. We develop machine learning tools to track the internal rearrangements in the quasi-two-dimensional cluster as it transits a sequence of constrictions. As the cluster relaxes from a state of strong mechanical deformations, we find differences in the dynamics of the grains within the interior of the cluster and those at its rim, with the latter experiencing larger deformations and less frequent rearrangements, effectively acting as an elastic membrane around a fluidlike core. We conclude that the observed structural-dynamical heterogeneity results from an interplay of the topological constrains, due to the presence of a closed interface, and the internal solid-fluid transitions. We discuss the universality of such behavior in various types of finite soft granular structures, including biological tissues.