Presentation by P. Polimeno at OSA-OMA-2021

Gain-Assisted Plasmonic/Dielectric Nanoshells in Optical Tweezers: Non-Linear Optomechanics and Thermal Effects.
Paolo Polimeno, Francesco Patti, Melissa Infusino, Jonathan Sànchez, Maria Iati, Rosalba Saija, Giovanni Volpe, Onofrio Maragò, Alessandro Veltri
Submitted as OSA-OMA-2021, AF1D.D Contribution
Date: 16 April
Time: 13:15 CEST

Short Abstract
We study theoretically the optomechanics of a dyed dielectric/metallic nanoshell in stationary Optical Tweezers. We consider the thermophoretic effects due to the interaction between the incident radiation and the nanoparticle metallic component.

Presentation by A. Callegari at OSA-OMA-2021

Simulation of clustering of Janus partices in an optical potential due to hydrodynamic fluxes.
Clustering of Janus Particles Under the Effect of Optical Forces Driven by Hydrodynamic Fluxes
Agnese Callegari, S. Masoumeh Mousavi, Iryna Kasianiuk, Denis Kasyanyuk, Sabareesh K P Velu, Luca Biancofiore, Giovanni Volpe
Submitted as: OSA-OMA-2021, AM1D.3 Contribution
Date: 12 April
Time: 15 CEST

Short Abstract
Hydrodynamic fluxes generated by Janus particles in an optical potential drive reversible clustering of colloids.

Extended Abstract

Self-organization entails the emergence of complex patterns and structures from relatively simple constituting building blocks. Phenomena such as flocking of birds and growth of bacterial colonies are examples of self-organization in nature. Also artificial microscopic systems feature similar forms of organization with the emergence of clusters, sometimes referred to as “living crystals”. In the past two decades, studies on self-organization focused on systems made of complex colloids with anisotropic surface, such as Janus particles. Depending on their surface material properties, Janus particles have been used in different fields for various applications such as self-assembly, microrheology and emulsion stabilization. Under certain conditions, Janus particles have the ability of self-propelling and behave as active Brownian particles; these active Janus particles might be used in future biomedical nano-devices for diagnostics, drug delivery and microsurgery. Studies on clustering of Janus particles have been performed by Palacci et al., who have shown the formation of living crystals in systems of light-activated Janus particles (Fe2O3-TPM) in hydrogen peroxide solution. Similarly, Buttinoni et al. demonstrated the clustering of light-activated Janus particles (carbon-SiO2) in a water-lutidine binary mixture. Other research groups have shown self-assembly and controlled crystal formations in a mixed system of light-activated Janus particles and passive colloids. In all these studies, a necessary ingredient for the clustering is the active nature of the particles. In systems of passive colloidal particles, crystallization was observed at the bottom of an attractive optical potential, close to the hard boundary during electrophoretic deposition, and in the presence of an external temperature gradient.

Here, we investigate the behavior of a system composed of Janus particles (silica microspheres half-coated with gold) close to a planar surface in the presence of an optical potential, and we experimentally demonstrate reversible clustering triggered by the presence of the optical field. Experimental results are compared and validated by numerical simulations, where the key ingredient for clustering is the presence of an attractive potential of hydrodynamic nature. In fact, the temperature gradient generated by the light absorption at the metallic patches on the Janus particles induces a local force field tangential to the surface of the Janus particle, which causes the fluid to slip at the surface of the particle. Because of the proximity of a planar surface, the flow pattern around the Janus particle is squeezed and results in a flow with a horizontal incoming radial component (parallel to the planar boundary) and outgoing vertical components (directed upwards from the wall). This thermophoretically-induced flow field affects the motion of other neighboring particles, so that a second nearby particle experiences an attractive hydrodynamic drag force toward the particle originating the flux. Clustering is confirmed also in mixtures of Janus particles and passive colloids (silica microspheres), where the hydrodynamic flux due to the Janus particles causes the clustering of the particles in the hybrid system and the formation of living crystals. As a further confirmation that the presence of Janus particles in the optical potential is crucial for the clustering, we show that a system with only non-Janus particles does not give rise to any clustering. We show experimentally that the clustering process is reversible, since the cluster starts to disassemble as soon as the optical potential is switched off.

Beyond their fundamental interest, the reported results are potentially relevant for various applications in the fields of self-assembly, targeted drug-delivery and bioremediation. For example, the possibility of forming clusters at a controllable distance from the minimum of a potential well offers a new route towards self-assembly near a target. Future work will be devoted to understanding how the clustering behavior can be controlled or altered by using more complex optical potentials.

Invited talk by L. Pérez at SPIE Photonics West OPTO

Stable, unstable and saddle points in a speckle optical potential.
FORMA: expanding applications of optical tweezers
Laura Pérez García
Invited talk at SPIE Photonics West OPTO
6 March 2021
Online

In this presentation, Laura Pérez will talk about FORMA  (force reconstruction via maximum-likelihood-estimator analysis) which addresses the need of measuring the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise. Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields. Here, after a brief introduction to FORMA, I will present its use, advantages, and limitations. I will conclude with some recent work where we exploit Bayesian inference to expand the scope of application of FORMA.

References:
Laura Pérez García, Jaime Donlucas Pérez, Giorgio Volpe, Alejandro V. Arzola & Giovanni Volpe, High-Performance Reconstruction of Microscopic Force Fields from Brownian Trajectories, Nature Communications 9, 5166 (2018)

Time: 6 March 2021
Place: Online
Link: FORMA: expanding applications of optical tweezers at SPIE Photonics West OPTO

Presentation by F. Schmidt on QED Casimir vs Critical Casimir at MPI Stuttgart, 11 February 2021

Schematic of the experiment with a suspended metallic flake-like particle on a gold-coated substrate.
QED Casimir vs Critical Casimir Forces: Trapping and Releasing of metal flake particles

Falko Schmidt, Agnese Callegari, Giovanni Volpe
(online at) MPI Stuttgart, Germany
11 February 2021, 14:30-16.00

We propose a mechanism for restoration of collapsed structures using critical Casimir forces by investigating the diffusion of metal flake-like particles. By tuning temperature near-criticality and employing selective self-assembled monolayers the resulting repulsive critical Casimir force is large enough to lift off particle and enable transitions previously impeded by QED Casimir attraction.

Online seminar by G. Volpe at Indian Institute of Science Education and Research (IISER), Pune, India

Deep learning for microscopy and optical trapping
Giovanni Volpe
21 January 2021, 16:30 CEST
Online
Invited seminar for Indian Institute of Science Education and Research (IISER), Pune, India

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.

Lecture by G. Volpe: Graph Theory Concepts, 25 November 2020

On 25 November 2020, Giovanni Volpe gave an online lecture on Graph Theory Concepts, in the scope of Karolinska Institute graduate course 3064: Imaging in Neuroscience: With a focus on structural MRI methods

The lecture is published online on youtube.

Link:
Imaging in Neuroscience: Graph Theory Concepts

Keynote talk by G. Volpe at the Online Conference Motile Active Matter, 26 October 2020

Active Matter Meets Machine Learning: Opportunities and Challenges
Giovanni Volpe
26 October 2020, 13:45 CEST
Keynote talk (Online) at the Online Conference Motile Active Matter, Jülich Förschungszentrum, 26 October 2020

Abstract: Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis [1, 2]; providing new data-driven models; improving navigation and search strategies [3, 4]; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.

References
[1] S. Helgadottir, A. Argun, G. Volpe, Digital video microscopy enhanced by deep learning. Optica 6, 506–513 (2019)
[2] S. Bo, F. Schmidt, R. Eichhorn, G. Volpe, Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102(R) (2019)
[3] G. Volpe, G. Volpe, The topography of the environment alters the optimal search strategy for active particles. Proc. Natl. Acad. Sci. 114, 11350–11355 (2017)
[4] S. Colabrese, K. Gustavsson, A. Celani, L. Biferale, Flow navigation by smart microswimmers via reinforcement learning. Phys. Rev. Lett. 118, 158004 (2017).

Online seminar by G. Volpe at DiSTAP, Singapore-MIT Alliance for Research and Technology (SMART) Centre

Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
22 October 2020, 14:00 CEST
Invited Seminar (Online) at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore & Boston (MA)

Abstract: 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 introduce a software, DeepTrack 2.0, 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 2.0 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.

References:
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe, “Quantitative Digital Microscopy with Deep Learning”, arXiv:2010.08260 (2020)

Presentation by F. Schmidt on Career Transition from Research to Entrepreneurship, 14 October 2020

Falko Schmidt, founder of Lucero Bio AB.

From basic research to founding a startup company: A personal journey to the unknown

Falko Schmidt
Career Seminar, Online, University of Gothenburg, Sweden
14 October 2020, 11:30-13:00
26 November 2020, 11:30-13:00

I am currently a PhD student at the Physics Department of the University of Gothenburg and will defend at the beginning of 2021. For a couple of years I have been playing with different ideas for a startup already, involving technical solutions developed during my time as PhD and was looking for their applications in the real world. After a couple of failures I have recently founded my own startup company Lucero AB. We will develop automated optical manipulation solutions for single cell analysis with applications in research on longevity, viral diseases and in the pharmaceutical industry. During this seminar I will share my insights on how to find ideas, how to validate them and the process towards creating a startup company.

This seminar is a recurring seminar and it is part of the Career Development and Entrepreneurship series initiated by the Faculty of Science. It will take place during the autumn 2020 and spring 2021.

Please check the links below for the planned dates:

14 October: Career seminars for PhD students at the Dept of Biological and Environmental Sciences and the Dept of Marine Sciences

26 November: Career seminars for PhD students at the Dept of Mathematical Sciences (GU and Chalmers)

Additional dates will be added later.