News
Nanoalignment by Critical Casimir Torques featured in the Editors’ Highlights of Nature Communications
The editors have included our work in their Editors’ Highlights webpage, which showcases the 50 best papers recently published in this area. You can view the feature on the Editors’ Highlights page (https://www.nature.com/ncomms/editorshighlights) as well as on the journal homepage (https://www.nature.com/ncomms/).
Nanoalignment by Critical Casimir Torques published in Nature Communications
Gan Wang, Piotr Nowakowski, Nima Farahmand Bafi, Benjamin Midtvedt, Falko Schmidt, Agnese Callegari, Ruggero Verre, Mikael Käll, S. Dietrich, Svyatoslav Kondrat, Giovanni Volpe
Nature Communications, 15, 5086 (2024)
DOI: 10.1038/s41467-024-49220-1
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.
Plenary Talk by G. Volpe at ENO-CANCOA, Cartagena, Colombia, 13 June 2024
Giovanni Volpe
Encuentro Nacional de Óptica y la Conferencia Andina y del Caribe en Óptica y sus Aplicaciones(ENO-CANCOA)
Cartagena, Colombia, 13 June 2024
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, currently at version 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.
Jason Lewis joins the Soft Matter Lab
Jason received his Ph.D. degree in Complexity Science from the University of Warwick, UK, with a thesis titled “Topological models of swarming”, which studied the dynamics of bird flocks, specifically under topological constraints, via theory and numerical simulation.
After his PhD, he undertook a postdoc in the group of Joakim Stenhammar at Lund University, Sweden, where he investigated chemotaxis and the collective behaviour of microswimmers, known as active turbulence, in addition to other projects at the interface of machine learning and active matter.
His research focuses on the theory and simulation of active matter systems at all scales, specifically on modelling the structure and dynamics of self-organising groups of motile robots.
Harshith Bachimanchi won best early-career researcher presentation award at AIM 2024, La Ràpita, Spain
The award, consisting of a certificate, and a cash prize of 500 €, is sponsored by Journal of Physics: Photonics (JPhys Photonics) from IOP Publishing.
Harshith received the prize for his presentation on “Bringing microplankton to focus: Holography and deep learning”, where he demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. He exemplified this by showing detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.
Mirja Granfors won best early-career researcher presentation award at AIM 2024, La Ràpita, Spain
The award, consisting of a certificate, and a cash prize of 250 €, is sponsored by Nanophotonics.
Mirja was awarded the prize for her presentation titled “Global graph features unveiled by unsupervised geometric deep learning”. In her presentation, she introduced a novel graph autoencoder designed to capture complex relationships modelled by graphs. She demonstrated the performance of the network across a spectrum of datasets, including the classification of protein assembly structures from single-molecule localization microscopy data, as well as the analysis of collective behavior and correlations between brain connections and age.
Deep learning for optical tweezers published in Nanophotonics
Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M. Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe and Mattias Goksör
Nanophotonics, 13(17), 3017-3035 (2024)
doi: 10.1515/nanoph-2024-0013
arXiv: 2401.02321
Optical tweezers exploit light–matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and analysis of the trapped objects, often outperforming classical methods thanks to the higher computational speed and versatility of deep learning. In this perspective, we show how cutting-edge deep learning approaches can remarkably improve optical tweezers, and explore the exciting, new future possibilities enabled by this dynamic synergy. Furthermore, we offer guidelines on integrating deep learning with optical trapping and optical manipulation in a reliable and trustworthy way.
Alex Lech defended his Master Thesis on 16 May 2024. Congrats!
Title: Simulation of light-absorbing microparticles in an optical landscape
Abstract:
Simulating the dynamics of active particles play a key role in understanding the many behaviours active matter can exhibit. Experimental studies are more costly than simulations in this regard, as there is much work that needs to be performed with setups and observation time. Computer simulations are a powerful and cost-effective alternative to experiments. One topic of study within active matter is light-absorbing microparticles which are commonly made of silica with a light-absorbing metallic compound such as iron oxide or gold. One such microparticle is the Janus particle, a silica particle with a hemispherical coating of gold as the absorbing compound. When illuminated with a laser, the coating absorbs the light and heats up rapidly, generating a temperature gradient which allows the Janus particle to exhibit self-propulsion and clustering with other Janus particles due to thermophoresis and Brownian motion.
In this thesis, I introduce a simulation model which simulates light-absorbing microparticles with a desired distribution of iron oxide in an optical landscape. In particular, I will consider the case of an optical landscape characterized by a periodical sinusoidal intensity profile of a given spatial periodicity.
The results show that for a hemispherical distribution (Janus particle) there is self-propulsion originating at the side of the cap, with super-diffusive characteristics. When the laser periodicity is similar to the particle radius, it becomes confined between two high intensity peaks. A particle with uniform distribution diffuses with Brownian motion, with no self-propulsion. Clustering behaviour arises when multiple particles are in close proximity to each other, as observed in experiments.
The agreement with experimental results opens up for the opportunity to simulate other light-absorbing particles with different distributions of absorbing compounds.
Supervisor: Agnese Callegari
Examiner: Giovanni Volpe
Opponent: John Klint, Niphredil Klint
Place: von Bahr
Time: 16 May, 2023, 15:45
Kunli Xiong appointed at Uppsala University
He will start his new appointment on May 6th 2024. His research will focus on nanooptics technology for electronic paper, optical neural networks, and intelligent microparticles.