Author: Agnese Callegari

Keynote Lecture by G. Volpe at ISMC 2022, Poznan, 20 September 2022

An exemplar of Hexbugs, commercially available toy robots that have been used in the experimental demonstration proposed. (Image from arXiv: 2209.04168)
Playing with Active Matter
Giovanni Volpe
Keynote Lecture at ISMC 2022
Poznan, Poland
20 September 2022, 13:30 CEST

In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.

Soft Matter Lab members present at ISMC 2022, Poznan, 19-23 September 2022

The Soft Matter Lab participates to the ISMC 2022 in Poznan, Poland, 19-23 September 2022, with the presentations listed below.

Playing with Active Matter on ArXiv

One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)
Playing with Active Matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
arXiv: 2209.04168

In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.

Invited Talk by G. Volpe at Fluctuations in small complex systems VI, Venice, 9 September 2022

Label-free measurement of biomolecules and their diffusion
Giovanni Volpe
9 September 2022, 16:45 (CEST)
Venice meeting on Fluctuations in small complex systems VI
Istituto Veneto di Scienze, Lettere ed Arti
Palazzo Franchetti, Venezia, 5-9 September 2022

Gideon Jägenstedt joins the Soft Matter Lab

(Photo by A. Argun.)
Gideon Jägenstedt joined the Soft Matter Lab on 29 August 2022.

Gideon is a Master student in the Complex Adaptive Systems at Chalmers University of Technology.

During his time at the Soft Matter Lab, he will work on his Master thesis project on particle representation and graph neural networks.

An anomalous competition: assessment of methods for anomalous diffusion through a community effort

An anomalous competition: assessment of methods for anomalous diffusion through a community effort
Carlo Manzo, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 25 August 2022
Time: 9:00 (PDT)

Deviations from the law of Brownian motion, typically referred to as anomalous diffusion, are ubiquitous in science and associated with non-equilibrium phenomena, flows of energy and information, and transport in living systems. In the last years, the booming of machine learning has boosted the development of new methods to detect and characterize anomalous diffusion from individual trajectories, going beyond classical calculations based on the mean squared displacement. We thus designed the AnDi challenge, an open community effort to objectively assess the performance of conventional and novel methods. We developed a python library for generating simulated datasets according to the most popular theoretical models of diffusion. We evaluated 16 methods over 3 different tasks and 3 different dimensions, involving anomalous exponent inference, model classification, and trajectory segmentation. Our analysis provides the first assessment of methods for anomalous diffusion in a variety of realistic conditions of trajectory length and noise. Furthermore, we compared the prediction provided by these methods for several experimental datasets. The results of this study further highlight the role that anomalous diffusion has in defining the biological function while revealing insight into the current state of the field and providing a benchmark for future developers.

Presenter: Giovanni Volpe

Presentation by A. Ciarlo at SPIE-OTOM, San Diego, 24 August 2022

Periodic feedback effect in counterpropagating intracavity optical tweezers
Antonio Ciarlo, Giuseppe Pesce, Fatemeh Kalantarifard, Parviz Elahi, Agnese Callegari, Giovanni Volpe, Antonio Sasso
Submitted to SPIE-OTOM
Date: 24 August 2022
Time: 14:00 (PDT)

Intracavity optical tweezers are a powerful tool to trap microparticles in water using the nonlinear feedback effect produced by the particle motion when it is trapped inside the laser cavity. In such systems two configurations are possible: a single-beam configuration and counterpropagating one. A removable isolator allows to switch between these configurations by suppressing one of the beams. Trapping a particle in the counterpropagating configuration, the measure of the optical power shows a feedback effect for each beam, that is present also when the two beams are misaligned and the trapped particle periodically jumps between them.

Invited Talk by D. Midtvedt at SPIE-ETAI, San Diego, 24 August 2022

Label-free characterization of biological matter across scales
Daniel Midtvedt, Erik Olsén, Benjamin Midtvedt, Elin K. Esbjörner, Fredrik Skärberg, Berenice Garcia, Caroline B. Adiels, Fredrik Höök, Giovanni Volpe
SPIE-ETAI
Date: 24 August 2022
Time: 09:10 (PDT)

Presentation by A. Callegari at SPIE-ETAI, San Diego, 23 August 2022

Simplified sketch of the neural network used for the simulations of intracavity optical trapping. (Image by A. Callegari.)
Simulating intracavity optical trapping with machine learning
Agnese Callegari, Mathias Samuelsson, Antonio Ciarlo, Giuseppe Pesce, David Bronte Ciriza, Alessandro Magazzù, Onofrio M. Maragò, Antonio Sasso, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 23 August 2022
Time: 13:40 (PDT)

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 modeling 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 – reproduce the results in the literature and generalize to the case of counterpropagating-beams intracavity optical trapping.

Presentation by H. Klein Moberg at SPIE-ETAI, San Diego, 23 August 2022

A convolutional neural network characterizes the properties of very small biomolecules without requiring prior detection. (Image by H. Klein Moberg.)
Seeing the invisible: deep learning optical microscopy for label-free biomolecule screening in the sub-10 kDa regime
Henrik Klein Moberg, Christoph Langhammer, Daniel Midtvedt, Barbora Spackova, Bohdan Yeroshenko, David Albinsson, Joachim Fritzsche, Giovanni Volpe
Submitted to SPIE-ETAI
Date: 23 August 2022
Time: 9:15 (PDT)

We show that a custom ResNet-inspired CNN architecture trained on simulated biomolecule trajectories surpasses the performance of standard algorithms in terms of tracking and determining the molecular weight and hydrodynamic radius of biomolecules in the low-kDa regime in NSM optical microscopy. We show that high accuracy and precision is retained even below the 10-kDa regime, constituting approximately an order of magnitude improvement in limit of detection compared to current state-of-the-art, enabling analysis of hitherto elusive species of biomolecules such as cytokines (~5-25 kDa) important for cancer research and the protein hormone insulin (~5.6 kDa), potentially opening up entirely new avenues of biological research.