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Microscopic Geared Metamachines published in Nature Communications

Top: single gear; Bottom: the second gear from the right has an optical metamaterial that react to laserlight and makes the gear move. All gears are made in silica directly on a chip. Each gear is about 0.016 mm in diameter. (Image by G. Wang)

Microscopic Geared Metamachines
Gan Wang, Marcel Rey, Antonio Ciarlo, Mohanmmad Mahdi Shanei, Kunli Xiong, Giuseppe Pesce, Mikael Käll and Giovanni Volpe
Nature Communications 16, 7767 (2025)
doi: 10.1038/s41467-025-62869-6
arXiv: 2409.17284

The miniaturization of mechanical machines is critical for advancing nanotechnology and reducing device footprints. Traditional efforts to downsize gears and micromotors have faced limitations at around 0.1 mm for over thirty years due to the complexities of constructing drives and coupling systems at such scales. Here, we present an alternative approach utilizing optical metasurfaces to locally drive microscopic machines, which can then be fabricated using standard lithography techniques and seamlessly integrated on the chip, achieving sizes down to tens of micrometers with movements precise to the sub-micrometer scale. As a proof of principle, we demonstrate the construction of microscopic gear trains powered by a single driving gear with a metasurface activated by a plane light wave. Additionally, we develop a versatile pinion and rack micromachine capable of transducing rotational motion, performing periodic motion, and controlling microscopic mirrors for light deflection. Our on-chip fabrication process allows for straightforward parallelization and integration. Using light as a widely available and easily controllable energy source, these miniaturized metamachines offer precise control and movement, unlocking new possibilities for micro- and nanoscale systems.

After the article was published, it was reported by many media outlets, University of Gothenburg, New Scientist, Optics.org, Phys.org, ScienceDaily, Discover Magazine, among others.

Featured in:
GU: Light powered motor fits inside a strand of hair.
New Scientist: Microscopic gears powered by light could be used to make tiny machines and video on Youtube.
Optics.org: University of Gothenburg makes micron-scale light-powered gears
Phys.org: New light-powered gear fits inside a strand of hair
ScienceDaily: Scientists build micromotors smaller than a human hair
Discover Magazine: The Smallest Motors in History Can Fit Inside a Strand of Hair
@DrBenMiles: Scientists Create Micro Machines Powered by Light

Presentation by M. Granfors at BNMI 2025, Gothenburg, 20 August 2025

DeepTrack2 Logo. (Image by J. Pineda)
DeepTrack2: physics-based microscopy simulations for deep learning
Mirja Granfors, Alex Lech, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, and Giovanni Volpe
BNMI 2025, 19-22 August 2025, Gothenburg, Sweden
Date: 20 August 2025
Time: 15:00 – 15:15
Place:  Wallenberg Conference Centre

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Roadmap for animate matter published on Journal of Physics: Condensed Matter

The three properties of animacy. The three polar plots sketch our jointly perceived level of development for each principle of animacy (i.e. activity, adaptiveness and autonomy) for each system discussed in this roadmap. The polar coordinate represents the various systems, while the radial coordinate represents the level of development (from low to high) that each system shows in the principle of each polar plot. Ideally, within a generation, all systems will fill these polar plots to show high levels in each of the three attributes of animacy. For now, only biological materials (not represented here) can be considered fully animated. (Image from the manuscript, adapted.)
Roadmap for animate matter
Giorgio Volpe, Nuno A M Araújo, Maria Guix, Mark Miodownik, Nicolas Martin, Laura Alvarez, Juliane Simmchen, Roberto Di Leonardo, Nicola Pellicciotta, Quentin Martinet, Jérémie Palacci, Wai Kit Ng, Dhruv Saxena, Riccardo Sapienza, Sara Nadine, João F Mano, Reza Mahdavi, Caroline Beck Adiels, Joe Forth, Christian Santangelo, Stefano Palagi, Ji Min Seok, Victoria A Webster-Wood, Shuhong Wang, Lining Yao, Amirreza Aghakhani, Thomas Barois, Hamid Kellay, Corentin Coulais, Martin van Hecke, Christopher J Pierce, Tianyu Wang, Baxi Chong, Daniel I Goldman, Andreagiovanni Reina, Vito Trianni, Giovanni Volpe, Richard Beckett, Sean P Nair, Rachel Armstrong
Journal of Physics: Condensed Matter 37, 333501 (2025)
arXiv: 2407.10623
doi: 10.1088/1361-648X/adebd3

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts in the circular economy, health and climate resilience within a generation. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field to encourage cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

Jun Yi Chen joins the Soft Matter Lab

(Photo by A. Ciarlo
Jun Yi Chen, master student in Chemistry at the University of Münster, started his Erasmus internship at the Physics Department of Gothenburg University on 11 August 2025.

Jun Yi holds a bachelor’s degree in Chemistry from the University of Münster.

During his internship at the Soft Matter Lab, he will investigate the interactions of polymer-coated silica microparticles under various stimuli using optical tweezers.

Mirja Granfors received the Best Early-Career Researcher Presentation Award at ETAI 2025, San Diego

(Photo by M. Granfors.)

Mirja Granfors received the Best Early Career Researcher Presentation Award at Emerging Topics in Artificial Intelligence (ETAI) 2025 held in San Diego, from 3 to 7 August 2025.

The award, which includes a certificate, a cash prize of $300, and a T-shirt, is presented by the organizers of the conference in collaboration with SPIE Optics + Photonics.

Mirja was awarded the prize for her presentation titled “DeepTrack2: physics-based microscopy simulations for deep learning”. Below is the full abstract of her presentation:

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Hari Prakash received the Best Early-Career Researcher Presentation Award at ETAI 2025, San Diego

Hari Prakash received the Best Early Career Researcher Presentation Award at Emerging Topics in Artificial Intelligence (ETAI) 2025 held in San Diego, from 3 to 7 August 2025.

The award, which includes a certificate, a cash prize of $300, and a T-shirt, is presented by the organisers of the conference in collaboration with SPIE Optics + Photonics.

Hari was awarded the prize for his presentation titled “Inchworm-Inspired Soft Robot with Groove-Guided Locomotion”. Below is the full abstract of her presentation:

Soft robots require directional control to navigate complex terrains. However, achieving such control often requires multiple actuators, which increases mechanical complexity, complicates control systems, and raises energy consumption. Here, we introduce an inchworm-inspired soft robot whose locomotion direction is controlled passively by patterned substrates. The robot employs a single rolled dielectric elastomer actuator, while groove patterns on a 3D-printed substrate guide its alignment and trajectory. Through systematic experiments, we demonstrate that varying groove angles enables precise control of locomotion direction without the need for complex actuation strategies. This groove-guided approach reduces energy consumption, simplifies robot design, and expands the applicability of bio-inspired soft robots in fields such as search and rescue, pipe inspection, and planetary exploration.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 3-7 August 2025

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 3-7 August 2025, with the presentations listed below.

Giovanni Volpe, who serves as Symposium Chair for the SPIE Optics+Photonics Congress in 2025, is a coauthor of the following invited presentations:

Giovanni Volpe will also be the reference presenter of the following Poster contributions:

Presentation by M. Granfors at SPIE-ETAI, San Diego, 7 August 2025

GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)
Global graph features unveiled by unsupervised geometric deep learning
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana Pereira, Carlo Manzo, and Giovanni Volpe
Date: 7 August 2025
Time: 2:45 PM – 3:00 PM
Place: Conv. Ctr. Room 4

Graphs are used to model complex relationships, such as interactions between particles or connections between brain regions. The structural complexity and variability of graphs pose challenges to their efficient analysis and classification. Here, we propose GAUDI (Graph Autoencoder Uncovering Descriptive Information), a graph autoencoder that addresses these challenges. GAUDI is trained in an unsupervised manner to capture the most critical parameters of graphs in the latent space, thereby enabling the extraction of essential parameters characterizing the graphs. We demonstrate the performance of GAUDI across diverse graph data originating from complex systems, including the estimation of the parameters of Watts-Strogatz graphs, the classification of protein assembly structures from single-molecule localization microscopy data, the analysis of collective behaviors, and correlations between brain connections and age. This approach offers a robust framework for efficiently analyzing and interpreting complex graph data, facilitating the extraction of meaningful patterns and insights across a wide range of applications.

Invited Presentation by B. Zufiria-Gerbolés at SPIE-ETAI, San Diego, 7 August 2025

Memory capacity in aging. A Brain reservoir computing architecture with uniform random signals applied to all nodes. (Image from the article.)
Computational memory capacity predicts aging and cognitive decline
Blanca Zufiria-Gerbolés, Mite Mijalkov, Ludvig Storm, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira
SPIE-ETAI, San Diego, CA, USA, 3 – 7 August 2025
Date: 7 August 2025
Time: 11:15 AM – 11:45 AM PDT
Place: Conv. Ctr. Room 4

Using reservoir computing and diffusion-weighted imaging, we explored changes in brain connectivity patterns and their impact on cognition during aging. We found that whole-brain networks perform optimally at low densities, with performance decreasing as network density increases, particularly in regions with weaker connections. This decline was strongly associated with age and cognitive performance. Our results suggest that a core network of anatomical hubs is essential for optimal brain function, while peripheral connections are more vulnerable to aging. This study highlights the potential of reservoir computing for understanding age-related cognitive decline.

Reference
Mite Mijalkov, Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira, Computational memory capacity predicts aging and cognitive decline
Nature Communications 16, 2748 (2025)

Presentation by M.Selin at SPIE-OTOM, San Diego, 6 August 2025

Illustration of adsorption process of a polymer coated particle. A single particle is brought to a liquid-liquid interface using an optical tweezers and once the polymer shell makes contact with the interface the particle immediately jumps into the interface. (Image by M. Selin.)
Mapping the adsorption dynamics of core-shell particles at liquid-liquid interfaces with optical tweezers
Martin Selin, Maret Ickler, Gerardo Campos-Villalobos, Fabrizio Camerin, Nicolas Vogel, Antonio Ciarlo, Giovanni Volpe, and Marcel Rey
Date: 6 August 2025
Time: 4:30 PM – 4:45 PM PDT
Place: Conv. Ctr. Room 3

Colloidal systems are integral to industries such as food and cosmetics, where liquid-liquid interfaces—like oils dispersed in water—are common. Whether colloidal particles adsorb to these interfaces depends on multiple factors such as particle surface chemistry, pH and salinity.

Here, we investigate how core–shell particles breach a liquid-liquid interface by using optical tweezers to gently push the particles into dodecane-water interfaces formed by microbubbles. Our core–shell particles feature a silica core and a PDMAEMA shell and by varying the amount of monomer added during synthesis the size of the shell can be tuned. Using the tweezers we measure the extent of the polymer shell. Importantly, we find that uncoated silica particles do not adsorb in pure water, whereas polymer coated particles absorb rapidly once the polymer layer contacts the interface, also when the core itself remains microns away. The longer the polymer the greater the distance from which the particle absorbs.

We also observe similar adsorption other polymer shells like PNIPAM and PVP, indicating that the presence of a polymer coating, rather than its specific chemical composition, is the key factor governing adsorption. At low and high pH the polymer shell contracts, also the binding energy becomes weaker making the absorption slower. In very acidic conditions the binding is so weak that the optical tweezers can pull particles out from the interface, allowing us to directly observe individual polymers detaching. These findings provide new insight into how polymer coatings dictate particle-interface interactions, paving the way for improved control of colloidal behavior.