The news highlights that the approach used in the featured paper will make possible for students in the primary and secondary school system to demonstrate complex active motion principles in the classroom, at an affordable budget.
In fact, experiments at the microscale often require very expensive equipment. The commercially available toys called Hexbugs used in the publication provide a macroscopic analogue of active matter at the microscale and have the advantage of being affordable for experimentation in the classroom.
About Scilight: Scilight showcase the most interesting research across the physical sciences published in AIP Publishing Journals.
Reference:
Hannah Daniel, Using Hexbugs to model active matter, Scilight 2024, 431101 (2024)
doi: 10.1063/10.0032401
Playing with Active Matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
Americal Journal of Physics 92, 847–858 (2024)
doi: 10.1119/5.0125111
arXiv: 2209.04168
In the past 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. Active particles, contrary to Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots (the Hexbugs). We show how they can be easily modified to perform regular and chiral active Brownian motion and demonstrate through experiments fundamental signatures of active systems such as how energy and momentum are harvested from an active bath, how obstacles can sort active particles by chirality, and how active fluctuations induce attraction between planar objects (a Casimir-like effect). These demonstrations enable hands-on experimentation with active matter and showcase widely used analysis methods.
In recent years, the rapid growth of artificial intelligence, particularly deep learning, has transformed fields from natural sciences to technology. While deep learning is often viewed as a glorified form of curve fitting, its advancement to multi-layered, deep neural networks has resulted in unprecedented performance improvements, often surprising experts. As AI models grow larger and more complex, many wonder whether AI will eventually take over the world and what role remains for physicists and, more broadly, humans.
A critical, yet underappreciated fact is that these AI systems rely heavily on vast amounts of training data, most of which are generated and annotated by humans. This dependency raises an intriguing issue: what happens when human-generated data is no longer available, or when AI begins to train on AI-generated data? The phenomenon of AI poisoning, where the quality of AI outputs declines due to self-referencing, demonstrates the limitations of current AI models. For example, in image recognition tasks, such as those involving the MNIST dataset, AI tends to gravitate towards ‘safe’ or average outputs, diminishing originality and accuracy.
In this context, the unique role of humans becomes clear. Physicists, with their capacity for originality, deep understanding of physical phenomena, and the ability to exploit fundamental symmetries in nature, bring invaluable perspectives to the development of AI. By incorporating physics-informed training architectures and embracing the human drive for meaning and discovery, we can guide the future of AI in truly innovative directions. The message is clear: physicists must remain original, pursue their passions, and continue searching for the hidden laws that govern the world and society.
Deep Learning for Microscopy
Giovanni Volpe Date: 26 September 2024 Place: ESPCI/Sorbonne, Paris, France
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.
Critical Casimir levitation of colloids above a bull’s-eye pattern
Piotr Nowakowski, Nima Farahmand Bafi, Giovanni Volpe, Svyatoslav Kondrat, S. Dietrich
arXiv: 2409.08366
Critical Casimir forces emerge among particles or surfaces immersed in a near-critical fluid, with the sign of the force determined by surface properties and with its strength tunable by minute temperature changes. Here, we show how such forces can be used to trap a colloidal particle and levitate it above a substrate with a bull’s-eye pattern consisting of a ring with surface properties opposite to the rest of the substrate. Using the Derjaguin approximation and mean-field calculations, we find a rich behavior of spherical colloids at such a patterned surface, including sedimentation towards the ring and levitation above the ring (ring levitation) or above the bull’s-eye’s center (point levitation). Within the Derjaguin approximation, we calculate a levitation diagram for point levitation showing the depth of the trapping potential and the height at which the colloid levitates, both depending on the pattern properties, the colloid size, and the solution temperature. Our calculations reveal that the parameter space associated with point levitation shrinks if the system is driven away from a critical point, while, surprisingly, the trapping force becomes stronger. We discuss the application of critical Casimir levitation for sorting colloids by size and for determining the thermodynamic distance to criticality. Our results show that critical Casimir forces provide rich opportunities for controlling the behavior of colloidal particles at patterned surfaces.
Erik Olsén started his postdoc at the Physics Department of the University of Gothenburg on 26th August 2024. His research is funded by a Swedish research council internation postdoc fellowship with grant nr 2024-00439.
Erik received a PhD degree 2023 in physics from Chalmers University of Technology, Sweden. In his thesis he focused on optical particle characterisation of nanoparticles and submicron particles, with an emphasis on label-free characterisation methods.
The Soft Matter Lab will administrate the postdoc grant while Erik will be in the lab of Sabrina Leslie at University of British Columbia (UBC). At UBC, Erik will combine different image modalities with confined lens induced confinement (CLiC) to characterise different types of biological nanoparticles.