Marcel Rey,
DINAMO 2023
Date: 13 June 2023
Time: 19:00 (CET)
Soft microgels are ideal model systems due to their ability to deform and adapt their shape upon external stimuli. Here, we use optical tweezers to measure the diffusion of soft core-shell microgels. We report an anomalous, subdiffusive behaviour, which may be linked to the multiple length scales present within core-shell microgels.
Agnese Callegari
PIERS 2023, Prague, Czech Republic
3 July 2023, 09:40
Casimir forces in quantum electrodynamics emerge between microscopic metallic objects because of the confinement of the vacuum electromagnetic fluctuations occurring even at zero temperature. Their generalization at finite temperature and in material media are referred to as Casimir–Lifshitz forces. These forces are typically attractive, leading to the widespread problem of stiction between the metallic parts of micro- and nanodevices. Recently, repulsive Casimir forces have been experimentally realized but their reliance on specialized materials prevents their dynamic control and thus limits their further applicability. Here, we experimentally demonstrate that repulsive critical Casimir forces, which emerge in a critical binary liquid mixture upon approaching the critical temperature, can be used to actively control microscopic and nanoscopic objects with nanometer precision. We demonstrate this by using critical Casimir forces to prevent the stiction caused by the Casimir–Lifshitz forces. We study a microscopic gold flake above a flat gold-coated substrate immersed in a critical mixture. Far from the critical temperature, stiction occurs because of dominant Casimir–Lifshitz forces. Upon approaching the critical temperature, however, we observe the emergence of repulsive critical Casimir forces that are sufficiently strong to counteract stiction. Our method provides a novel way of controlling the distances of micro- and nanostructures using tunable critical Casimir forces to counteract forces such as the Casimir–Lifshitz force, thereby preventing stiction and device failure. Due to the simplicity of our design the concept can be adapted to already existing MEMS and NEMS. Moreover, this path opens new possibilities for the dynamic control of MEMS and NEMS where the temperature of the system could be controlled via light illumination, enabling faster transitions and higher selectivity for a new generation of the micromembranes that are found ubiquitously in MEMS and NEMS devices.
References
Falko Schmidt, Agnese Callegari, Abdallah Daddi-Moussa-Ider, Battulga Munkhbat, Ruggero Verre, Timur Shegai, Mikael Käll, Hartmut Löwen, Andrea Gambassi and Giovanni Volpe, Tunable critical Casimir forces counteract Casimir-Lifshitz attraction,
Nature Physics 19, 271-278 (2023)
Giovanni Volpe
PSL Soft and Active Matter Days 2023, Paris, France
Date: 3 July 2023
Martin Selin
30 June 2023, 13:00 CEST
Optical tweezers are powerful tools for manipulating and studying the mechanical properties of single biomolecules, such as DNA. However, conducting such experiments manually is both time-consuming and labor-intensive limiting the amount of data collectable. In this work, we present a method to automate optical tweezers with the use of deep learning applying it to DNA pulling experiments.
A typical DNA pulling experiment can be divided into three main steps, each of which we have automated. The first is positioning of a bead in a micropipette(or secondary optical trap), second is connecting DNA of a another optically trapped bead with the bead in the micropipette and lastly the stretching of the DNA by moving the trapped bead while monitoring the force.
We have used deep learning, in particular a unet, to track beads and identify important features in the sample such as the micropipette. Combining this with realtime feedback allows the system to both trap beads and carefully position trap beads.
We demonstrate the viability of our method by stretching lambda DNA, showing human like reliability in performing the experiments. We expect our method to find use in the study of small biomolecules enabling more and faster data collection as well as longer running experiments.
Jesús Pineda
Characterizing dynamic processes in living systems provides essential information for advancing our understanding of life processes in health and diseases and for developing new technologies and treatments. In the past two decades, optical microscopy has undergone significant developments, enabling us to study the motion of cells, organelles, and individual molecules with unprecedented detail at various scales in space and time. However, analyzing the dynamic processes that occur in complex and crowded environments remains a challenge. This work introduces MAGIK, a deep-learning framework for the analysis of biological system dynamics from time-lapse microscopy. MAGIK models the movement and interactions of particles through a directed graph where nodes represent detections and edges connect spatiotemporally close nodes. The framework utilizes an attention-based graph neural network (GNN) to process the graph and modulate the strength of associations between its elements, enabling MAGIK to derive insights into the dynamics of the systems. MAGIK provides a key enabling technology to estimate any dynamic aspect of the particles, from reconstructing their trajectories to inferring local and global dynamics. We demonstrate the flexibility and reliability of the framework by applying it to real and simulated data corresponding to a broad range of biological experiments.
Date: 30 June 2023
Time: 12:30
Event: XVII International Congress of the Spanish Biophysical Society
Giovanni Volpe
XVII Congress of the Spanish Biophysical Society, Castelldefels, Spain, 30 June 2023
Date: 30 June 2023
Time: 11:00
Giovanni Volpe
Active Matter at Surfaces and in Complex Environments, Dresden, Germany
Date: 20 June 2023
Time: 15:30
Harshith Bachimanchi
30 June 2023, 12:40 CEST
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by 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.
Giovanni Volpe
DINAMO 2023, Svolvaer, Lofoten Islands, Norway
Date: 15 June 2023
Time: 08:30
Gan Wang, Marcel Rey, Mahdi Shanei, Kunli Xiong, Einstom Engay, Mikael Käll, and Giovanni Volpe
Date: 13 June 2023
Time: 21:00 (CEST）
On-chip micromotors hold significant application potential in various fields, including cells, microfluidic manipulation, and the micro integration of machines. .The driving mechanism plays a crucial role in the design of micromotors. Currently, various methods such as static electricity, light, magnetism, chemical energy, and mechanical conduction are utilized for this purpose. Optics, in particular, offers distinct advantages including precise control, addressability, non-contact operation, and compatibility with diverse liquid environments. However, existing optically actuated on-chip motors necessitate high energy input, resulting in phototoxicity concerns and hindrances to large-scale manipulation. Furthermore, achieving precise control over speed and direction remains challenging, along with difficulties in establishing coupling among multiple devices.
