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 asOSA-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.
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.
Schematic of a bistable potential generated with a double-beam optical tweezers.
Optical Tweezers: A Comprehensive Tutorial from Calibration to Applications
Jan Gieseler, Juan Ruben Gomez-Solano, Alessandro Magazzù, Isaac Pérez Castillo, Laura Pérez García, Marta Gironella-Torrent, Xavier Viader-Godoy, Felix Ritort, Giuseppe Pesce, Alejandro V. Arzola, Karen Volke-Sepulveda & Giovanni Volpe
Advances in Optics and Photonics, 13(1), 74-241 (2021)
doi: https://doi.org/10.1364/AOP.394888
arXiv: 2004.05246
Since their invention in 1986 by Arthur Ashkin and colleagues, optical tweezers have become an essential tool in several fields of physics, spectroscopy, biology, nanotechnology, and thermodynamics. In this Tutorial, we provide a primer on how to calibrate optical tweezers and how to use them for advanced applications. After a brief general introduction on optical tweezers, we focus on describing and comparing the various available calibration techniques. Then, we discuss some cutting-edge applications of optical tweezers in a liquid medium, namely to study single-molecule and single-cell mechanics, microrheology, colloidal interactions, statistical physics, and transport phenomena. Finally, we consider optical tweezers in vacuum, where the absence of a viscous medium offers vastly different dynamics and presents new challenges. We conclude with some perspectives for the field and the future application of optical tweezers. This Tutorial provides both a step-by-step guide ideal for non-specialists entering the field and a comprehensive manual of advanced techniques useful for expert practitioners. All the examples are complemented by the sample data and software necessary to reproduce them.
Particle tracking and characterization in terms of radius and refractive index.
Quantitative Digital Microscopy with Deep Learning
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe
Applied Physics Reviews 8, 011310 (2021)
doi: 10.1063/5.0034891
arXiv: 2010.08260
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.
Measuring the dynamics of colloids interacting with critical Casimir interaction via blinking optical tweezers: graphical representation of the optical traps.
Optical trapping and critical Casimir forces
Agnese Callegari, Alessandro Magazzù, Andrea Gambassi & Giovanni Volpe
The European Physical Journal Plus (EPJP), 136, 213 (2021)
doi: 10.1140/epjp/s13360-020-01020-4
arXiv: 2008.01537
Critical Casimir forces emerge between objects, such as colloidal particles, whenever their surfaces spatially confine the fluctuations of the order parameter of a critical liquid used as a solvent. These forces act at short but microscopically large distances between these objects, reaching often hundreds of nanometers. Keeping colloids at such distances is a major experimental challenge, which can be addressed by the means of optical tweezers. Here, we review how optical tweezers have been successfully used to quantitatively study critical Casimir forces acting on particles in suspensions. As we will see, the use of optical tweezers to experimentally study critical Casimir forces can play a crucial role in developing nano-technologies, representing an innovative way to realize self-assembled devices at the nano- and microscale.
Nils Jacobson defended his Master thesis in MPCAS at the Chalmers University of Technology on 16 February 2021. Congrats!
Screenshot of Nils Jacobson’s Master Thesis defence.Title: Vascular Bifurcation Detection in Cerebral CT Angiography Using CNN and Frangi Filters
Segmentation and feature extraction are important tools for analysing and visualizing information in medical image data, particularly in vascular image data which relates to widely spread vascular diseases. Vessel segmentation is extensively featured in research, recently adapting trends in deep learning image processing. This paper aims to develop a vessel bifurcation detection method to support a seed point based segmentation approach. The suggested approach is a combination of classification, with a convolutional neural network (DenseNet), local vessel segmentation, with Frangi filters, and 3D morphological skeletonization. A small data set is produced for network training and evaluation. Results indicate a high classification accuracy which filters problematic samples for the Frangi filter. Thus the combination is able to suggest quality branch seed points under most circumstances. Next step would be to expand the data set to enable further optimization and more rigid evaluation. In any case a combination of a high performance classifier followed by qualitative assessment of local samples show potential.
Name of the master programme: MPCAS – Complex Adaptive Systems Supervisor: Jonna Hellström and Giovanni Volpe Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg Opponent: Eva Škvor
Place: Online via Zoom Time: 16 February, 2021, 16:00
Giovanni Volpe is part of the committee of the conference Optical Manipulation and its Applications (OMA), which is part of the OSA Biophotonics Congress: Optics in the Life Sciences.
Optical Manipulation encompasses all areas of manipulation and measurement using light, from optical manipulation of microparticles to photoactivated materials and optogenetics, emphasizing new and developing application areas in biophysics and biomedicine.
The categories of topics in the conference are Optical Manipulation in Biophysics and Biomedicine, Optical Manipulation Fundamentals, Optical Manipulation Applications, and Alternative Manipulation Techniques.
The conference will be held online 12-16 April 2021.
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.
Falko Schmidt defended his PhD Thesis in Physics on Friday, 15 January 2021. Congrats!
The disputation took place at 9 a.m., in PJ salen, Fysikgården.
Falko Schmidt’s opponent, Peer Fischer, gave an introductory presentation with title “Microswimmers and motile active matter”.
From Falko Schmidt’s PhD Thesis.Title: Active Matter in a Critical State: From passive building blocks to active molecules, engines and droplets
The motion of microscopic objects is strongly affected by their surrounding environment. In quiescent liquids, motion is reduced to random fluctuations known as Brownian motion. Nevertheless, microorganisms have been able to develop mechanisms to generate active motion. This has inspired researchers to understand and artificially replicate active motion. Now, the field of active matter has developed into a multi-disciplinary field, with researchers developing artificial microswimmers, producing miniaturized versions of heat engines and showing that individual colloids self-assemble into larger microstructures. This thesis taps into the development of artificial microscopic and nanoscopic systems and demonstrates that passive building blocks such as colloids are transformed into active molecules, engines and active droplets that display a rich set of motions. This is achieved by combining optical manipulation with a phase-separating environment consisting of a critical binary mixture. I first show how simple absorbing particles are transformed into fast rotating microengines using optical tweezers, and how this principle can be scaled down to nanoscopic particles. Transitioning then from single particles to self-assembled modular swimmers, such colloidal molecules exhibit diverse behaviour such as propulsion, orbital rotation and spinning, and whose formation process I can control with periodic illumination. To characterize the molecules dynamics better, I introduce a machine-learning algorithm to determine the anomalous exponent of trajectories and to identify changes in a trajectory’s behaviour. Towards understanding the behaviour of larger microstructures, I then investigate the interaction of colloidal molecules with their phase-separating environment and observe a two-fold coupling between the induced liquid droplets and their immersed colloids. With the help of simulations I gain a better physical picture and can further analyse the molecules’ and droplets’ emergence and growth dynamics. At last, I show that fluctuation-induced forces can solve current limitations in microfabrication due to stiction, enabling a further development of the field towards smaller and more stable nanostructures required for nowadays adaptive functional materials. The insights gained from this research mark the path towards a new generation of design principles, e.g., for the construction of flexible micromotors, tunable micromembranes and drug delivery in health care applications.
