Agnese Callegari

Nanoalignment by Critical Casimir Torques on ArXiv

Artist rendition of a disk-shaped microparticle trapped above a circular uncoated pattern within a thin gold layer coated on a glass surface. *(Image by the Authors of the manuscript.)*

Nanoalignment by Critical Casimir Torques
Gan Wang, Piotr Nowakowski, Nima Farahmand Bafi, Benjamin Midtvedt, Falko Schmidt, Ruggero Verre, Mikael Käll, S. Dietrich, Svyatoslav Kondrat, Giovanni Volpe
arXiv: 2401.06260

The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.

Colloquium by G. Volpe at the Mini-Symposium with Giovanni Volpe and Pawel Sikorski, Lund, 11 January 2024

Deep Learning for Imaging and Microscopy
Giovanni Volpe
Mini-Symposium with Giovanni Volpe and Pawel Sikorski, Lund, Sweden, 11 January 2024
Date: 11 January 2024
Time: 15:15

Fredrik Skärberg presented his half-time seminar on 10 January 2024

Fredrik Skärberg (right) and opponent Prof. Rebecka Jörnsten (left). *(Photo by A. Ciarlo)*

Fredrik Skärberg completed the first half of his doctoral studies and he defended his half-time on the 10th of January 2024.

The presentation, with title: “Holographic characterization of biological nanoparticles using deep learning”, was held in hybrid format, with part of the audience in the Nexus room and the rest connected through zoom. The half-time consisted in a presentation about his past and planned projects and it was followed by a discussion and questions proposed by his opponent Prof. Rebecka Jörnsten.

The presentation started with a short background to characterization of biological particles inside cells and an introduction to the papers included in the half-time.

It continued with images and videos of various particle types inside cells, both tracked and characterized, followed by a description of the LodeSTAR-model.

In the last section, he outlined the proposed continuation of his PhD, with an ongoing project for monitoring lipid droplets during long timescales and a neural network for 3D rotation parameter estimation of rotating biological samples.

PhD Student: Fredrik Skärberg
Supervisor: Daniel Midtvedt
Co-supervisors: Giovanni Volpe, Fredrik Höök

Fredrik Skärberg and audience in Nexus. *(Photo by A. Ciarlo.)*

Norma Caridad Palmero Cruz joins the Soft Matter Lab

Norma Caridad Palmero Cruz starts her PhD at the Physics Department at the University of Gothenburg on 8th January 2024.

Norma has a Master degree in Physics from the University of Havana, Cuba.

In her PhD, Norma will focus on on the study of biological systems using optical tweezers and light sheets techniques.

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning on ArXiv

Schematic illustration of the plasmonic H₂ sensing principle, where the sorption of hydrogen into hydride-forming metal nanoparticles induces a change in their localized surface plasmon resonance frequency, which leads to a color change that is resolved in a spectroscopic measurement in the visible light spectral range. *(Image by the Authors of the manuscript.)*

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning
Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček, Pernilla Ekborg-Tanner, Sara Nilsson, Giovanni Volpe, Paul Erhart, Christoph Langhammer
arXiv: 2312.15372

The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble Model for Accelerated Sensing (LEMAS) accelerates the response of a state-of-the-art optical plasmonic hydrogen sensor by up to a factor of 40 in an oxygen-free inert gas environment, by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Furthermore, it eliminates the pressure dependence of the response intrinsic to metal hydride-based sensors, while leveraging their ability to operate in oxygen-starved environments that are proposed to be used for inert gas encapsulation systems of hydrogen installations. Moreover LEMAS provides a measure for the uncertainty of the predictions that is pivotal for safety-critical sensor applications. Our results thus advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.

Symposium on AI, Neuroscience, and Aging featured on ANSA.it

The Symposium on AI, Neuroscience, and Aging has been featured on ANSA.it news, in an article with title: Simposio italo-svedese a Stoccolma sull’IA e la neuroscienza (Italian).

ANSA (an acronym standing for Agenzia Nazionale Stampa Associata) is the leading news agency in Italy and one of the top ranking in the world.

Optimal calibration of optical tweezers with arbitrary integration time and sampling frequencies – A general framework published in Biomedical Optics Express

Different sampling methods for the trajectory of a particle. *(Adapted from the manuscript.)*

Optimal calibration of optical tweezers with arbitrary integration time and sampling frequencies — A general framework
Laura Pérez-García, Martin Selin, Antonio Ciarlo, Alessandro Magazzù, Giuseppe Pesce, Antonio Sasso, Giovanni Volpe, Isaac Pérez Castillo, Alejandro V. Arzola
Biomedical Optics Express, 14, 6442-6469 (2023)
doi: 10.1364/BOE.495468
arXiv: 2305.07245

Optical tweezers (OT) have become an essential technique in several fields of physics, chemistry, and biology as precise micromanipulation tools and microscopic force transducers. Quantitative measurements require the accurate calibration of the trap stiffness of the optical trap and the diffusion constant of the optically trapped particle. This is typically done by statistical estimators constructed from the position signal of the particle, which is recorded by a digital camera or a quadrant photodiode. The finite integration time and sampling frequency of the detector need to be properly taken into account. Here, we present a general approach based on the joint probability density function of the sampled trajectory that corrects exactly the biases due to the detector’s finite integration time and limited sampling frequency, providing theoretical formulas for the most widely employed calibration methods: equipartition, mean squared displacement, autocorrelation, power spectral density, and force reconstruction via maximum-likelihood-estimator analysis (FORMA). Our results, tested with experiments and Monte Carlo simulations, will permit users of OT to confidently estimate the trap stiffness and diffusion constant, extending their use to a broader set of experimental conditions.

Talk by G. Volpe at the Symposium on AI, Neuroscience, and Aging, Stockholm, 27 November 2023

Deep Learning for Imaging and Microscopy
Giovanni Volpe
Symposium on AI, Neuroscience, and Aging, Stockholm, Sweden, 27 November 2023
Date: 27 November 2023
Time: 15:55

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. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user- friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Seminar by C. Reichhardt on 30 November 2023

Complex Dynamics in Active Matter Systems, Frustration Effects, Magnus Forces and Synchronization
Charles Reichhardt
Los Alamos National Laboratory

30 November 2023, 16:30, Nexus

Active matter denotes systems with self-propulsion and arises in biological, soft, robotic, and social settings [1]. Here, we outline some of our group’s recent efforts in active systems, including active matter interacting with ordered and disordered substrates, where various kinds of active clogging and commensuration effects can occur that have connections with frustrated systems and Mott physics. We also discuss chiral active systems with a Magnus force, where we find edge currents similar to those found for topological systems or charged particles in magnetic fields. In the presence of quenched disorder, the chiral active system also shows side jump effects with an active matter Hall angle. Finally, we discuss coupled active matter swarmulators where, in addition to activity, the particles have an internal degree of freedom that can become synchronized or antisynchronized. This system shows a variety of new kinds of motility-induced phase-separated states, including active matter stripes, frustrated states, gels, cluster fluids, and glassy states.

[1] Active Brownian particles in complex and crowded environments, Clemens Bechinger, Roberto Di Leonardo, Hartmut Lowen, Charles Reichhardt Giorgio Volpe, and Giovanni Volpe, Reviews of Modern Physics 88 045006 (2016).

Environmental Memory Boosts Group Formation of Clueless Individuals published in Nature Communications

Non-monotonic size dependence of group formation on environmental crowding. *(Excerpt from Fig. 2 of the manuscript.)*

Environmental Memory Boosts Group Formation of Clueless Individuals
Cristóvão S. Dias, Manish Trivedi, Giovanni Volpe, Nuno A. M. Araújo, Giorgio Volpe
Nature Communications, 14, 7324 (2023)
doi: 10.1038/s41467-023-43099-0
arXiv: 2306.00516

The formation of groups of interacting individuals improves performance and fitness in many decentralised systems, from micro-organisms to social insects, from robotic swarms to artificial intelligence algorithms. Often, group formation and high-level coordination in these systems emerge from individuals with limited information-processing capabilities implementing low-level rules of communication to signal to each other. Here, we show that, even in a community of clueless individuals incapable of processing information and communicating, a dynamic environment can coordinate group formation by transiently storing memory of the earlier passage of individuals. Our results identify a new mechanism of indirect coordination via shared memory that is primarily promoted and reinforced by dynamic environmental factors, thus overshadowing the need for any form of explicit signalling between individuals. We expect this pathway to group formation to be relevant for understanding and controlling self-organisation and collective decision making in both living and artificial active matter in real-life environments.