High-resolution display of “The Kiss” on Retina E-Paper vs. iPhone 15: Photographs comparing the display of “The Kiss” on an iPhone 15 and Retina E-paper. The surface area of the Retina E-paper is ~ 1/4000 times smaller than the iPhone 15. (Image by the Authors of the manuscript.)Video‐rate tunable colour electronic paper with human resolution
Ade Satria Saloka Santosa, Yu-Wei Chang, Andreas B. Dahlin, Lars Osterlund, Giovanni Volpe, Kunli Xiong
Nature 646, 1089-1095 (2025)
arXiv: 2502.03580
doi: 10.1038/s41586-025-09642-3
As demand for immersive experiences grows, displays are moving closer to the eye with smaller sizes and higher resolutions. However, shrinking pixel emitters reduce intensity, making them harder to perceive. Electronic Papers utilize ambient light for visibility, maintaining optical contrast regardless of pixel size, but cannot achieve high resolution. We show electrically tunable meta-pixels down to ~560 nm in size (>45,000 PPI) consisting of WO3 nanodiscs, allowing one-to-one pixel-photodetector mapping on the retina when the display size matches the pupil diameter, which we call Retina Electronic Paper. Our technology also supports video display (25 Hz), high reflectance (~80%), and optical contrast (~50%), which will help create the ultimate virtual reality display.
Cover of the PhD thesis. (Image by M. Selin.)Martin Selin defended his PhD thesis on October 8th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.
Title: Advanced and Autonomous Applications of Optical Tweezers
Abstract: Optical tweezers have become a central tool, using lasers to manipulate and probe objects with exceptional precision enabling single-molecule, single-cell, and single-particle studies. However, this precision comes at the cost of throughput.
By developing a fully autonomous system we can adress this limitation of optical tweezers. The system is capable of perfoming multiple different experiments independently and of operating for over 10 hours continously. Using the same system we also investigate particle adsorption into liquid-liquid interfaces revealing never before seen dynamics.
These developments help optical tweezers by bridging the gap between single-molecule, cell or particle studies and ensemble measurements, enabling the application of deep learning for advanced modeling and unlocking the potential of optical tweezers for large, data-driven studies.
Prakhar Dutta. (Photo by A. Ciarlo.)Prakhar Dutta started his PhD at the Physics Department of the University of Gothenburg on the 1st of October 2025.
Prakhar has a Master’s degree in Biomedical Engineering from RWTH Aachen University in Germany.
During the course of his PhD, as part of the SPM 4.0 MSCA Doctoral Network he will focus on the development of deep learning based packages for processing of Atomic Force Microscopy data and on the development of a photonic force microscope.
Massimiliano Passaretti (left) and Yu-Wei Chang (right) at NEME 2025. (Photo courtesy of Clarion Hotel Draken.)Graph theory and deep learning pipelines
Yu-Wei Chang, Massimiliano Passaretti NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 12:45 – 14:00 Place: Clarion Hotel Draken
This workshop begins with a practical introduction to graph theory, then guides participants through BRAPH 2 to build connectomes, compute graph measures, and run group comparisons, followed by a hands-on deep-learning pipeline. It demonstrates a unified GUI/command-line workflow, a unique architecture of BRAPH 2, helping participants move smoothly from the GUI to scripts. This workshop also guides participants to reproduce multiplex and deep-learning results on their computers from the BRAPH 2 bioRxiv preprint.
From images to graphs, this plenary shows how parcellations and tractography become connectomes and how network analysis reveals brain-network signatures. (Image by Y.-W. Chang.)Network analysis of neuroimaging data, and deep learning pipelines
Yu-Wei Chang NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 09:00 – 09:45 Place: Clarion Hotel Draken
This plenary presents a practical framework for analysing neuroimaging data with network science and deep learning. It moves from modality-specific preprocessing to graph construction (single-layer and multiplex), then covers core graph measures, group inference, and brain-surface visualization, highlighting recent work from Associate Professor Joana B. Pereira’s group (Department of Clinical Neuroscience, Karolinska Institutet). It also introduces deep-learning pipelines for neuroimaging data: reservoir-computing memory capacity analysis, GapNet for handling missing data, and a robust feature-attribution method combined with SNP (single nucleotide polymorphism) information. The plenary concludes with the BRAPH 2 framework, which supports these pipelines and extends to other ongoing projects (e.g., light-sheet microscopy, Raman spectroscopy).
Alex Lech at the BNMI poster session. (Photo by M. Granfors)DeepTrack2: Microscopy Simulations for Deep Learning
Alex Lech, Mirja Granfors, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, Giovanni Volpe BNMI 2025, 19-22 August 2025, Gothenburg, Sweden Date: 20 August 2025 Time: 15:15-19:00 Place: Wallenberg Conference Centre
DeepTrack2 is a flexible and scalable Python library designed for simulating microscopy data to generate high-quality synthetic datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, allowing users to simulate realistic experimental conditions with ease. Its modular architecture enables users to customize experimental setups, simulate a variety of objects, and incorporate optical aberrations, realistic experimental noise, and other user-defined effects, making it suitable for various research applications. DeepTrack2 is designed to be an accessible tool for researchers in fields that utilize image analysis and deep learning, as it removes the need for labor-intensive manual annotation through simulations. This helps accelerate the development of AI-driven methods for experiments by providing large volumes of data that is often required by deep learning models. DeepTrack2 has already been used for a number of applications in cell tracking, classifications tasks, segmentations and holographic reconstruction. Its flexible and scalable nature enables researchers to simulate a wide array of experimental conditions and scenarios with full control of features and parameters.
DeepTrack2 is available on GitHub, with extensive documentation, tutorials, and an active community for support and collaboration at https://github.com/DeepTrackAI/DeepTrack2.
Quantitative Digital Microscopy with Deep Learning.
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt & Giovanni Volpe.
Applied Physics Reviews, volume 8, article number 011310 (2021).
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.
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.
