Category: Article

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

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.

Quantitative evaluation of methods to analyze motion changes in single-particle experiments published on Nature Communications

Rationale for the challenge organization. The interactions of biomolecules in complex environments, such as the cell membrane, regulate physiological processes in living systems. These interactions produce changes in molecular motion that can be used as a proxy to measure interaction parameters. Time-lapse single-molecule imaging allows us to visualize these processes with high spatiotemporal resolution and, in combination with single-particle tracking methods, provide trajectories of individual molecules. (Image by the Authors of the manuscript.)
Quantitative evaluation of methods to analyze motion changes in single-particle experiments
Gorka Muñoz-Gil, Harshith Bachimanchi, Jesús Pineda, Benjamin Midtvedt, Gabriel Fernández-Fernández, Borja Requena, Yusef Ahsini, Solomon Asghar, Jaeyong Bae, Francisco J. Barrantes, Steen W. B. Bender, Clément Cabriel, J. Alberto Conejero, Marc Escoto, Xiaochen Feng, Rasched Haidari, Nikos S. Hatzakis, Zihan Huang, Ignacio Izeddin, Hawoong Jeong, Yuan Jiang, Jacob Kæstel-Hansen, Judith Miné-Hattab, Ran Ni, Junwoo Park, Xiang Qu, Lucas A. Saavedra, Hao Sha, Nataliya Sokolovska, Yongbing Zhang, Giorgio Volpe, Maciej Lewenstein, Ralf Metzler, Diego Krapf, Giovanni Volpe, Carlo Manzo
Nature Communications 16, 6749 (2025)
arXiv: 2311.18100
doi: https://doi.org/10.1038/s41467-025-61949-x

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we report the results of a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we implemented a software library that simulates realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition constitutes the first assessment of these methods, providing insights into the current limitations of the field, fostering the development of new approaches, and guiding researchers to identify optimal tools for analyzing their experiments.

Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis on ArXiv

In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. (Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)
From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
ArXiv: 2507.15772

Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection. Raman spectroscopy provides a powerful, non-invasive means to quantify these biomolecules through their molecular vibrational signatures. However, traditional Raman analysis relies on customized data-processing workflows that require fluorescence background removal and prior identification of Raman peaks of interest-introducing potential biases and inconsistencies. Here, we introduce DIVA (Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis), a fully automated workflow based on a variational autoencoder. Unlike conventional approaches, DIVA processes native Raman spectra-including fluorescence backgrounds-without manual preprocessing, identifying and quantifying significant spectral features in an unbiased manner. We applied DIVA to detect a range of plant stresses, including abiotic (shading, high light intensity, high temperature) and biotic stressors (bacterial infections). By integrating deep learning with vibrational spectroscopy, DIVA paves the way for AI-driven plant health assessment, fostering more resilient and sustainable agricultural practices.

An in vivo mimetic liver-lobule-chip (LLoC) for stem cell maturation, and zonation of hepatocyte-like cells on chip published in Lab on a Chip

The image shows a liver-lobule-chip (LLoC) with 21 artificial lobules mimicking liver microarchitecture. Its PDMS design supports diffusion-based perfusion, shear stress, and nutrient gradients and enables iPSC-derived hepatic maturation and spatially organized, zonated function in 3D. (Image by C. Beck Adiels)
An in vivo mimetic liver-lobule-chip (LLoC) for stem cell maturation, and zonation of hepatocyte-like cells on chip
Philip Dalsbecker, Siiri Suominen, Muhammad Asim Faridi, Reza Mahdavi, Julia Johansson, Charlotte Hamngren Blomqvist, Mattias Goksör, Katriina Aalto-Setälä, Leena E. Viiri and Caroline B. Adiels
Lab on a Chip 25, 4328 – 4344 (2025)
doi: 10.1039/D4LC00509K

In vitro cell culture models play a crucial role in preclinical drug discovery. To achieve optimal culturing environments and establish physiologically relevant organ-specific conditions, it is imperative to replicate in vivo scenarios when working with primary or induced pluripotent cell types. However, current approaches to recreating in vivo conditions and generating relevant 3D cell cultures still fall short. In this study, we validate a liver-lobule-chip (LLoC) containing 21 artificial liver lobules, each representing the smallest functional unit of the human liver. The LLoC facilitates diffusion-based perfusion via sinusoid-mimetic structures, providing physiologically relevant shear stress exposure and radial nutrient concentration gradients within each lobule. We demonstrate the feasibility of long term cultures (up to 14 days) of viable and functional HepG2 cells in a 3D discoid tissue structure, serving as initial proof of concept. Thereafter, we successfully differentiate sensitive, human induced pluripotent stem cell (iPSC)-derived cells into hepatocyte-like cells over a period of 20 days on-chip, exhibiting advancements in maturity compared to traditional 2D cultures. Further, hepatocyte-like cells cultured in the LLoC exhibit zonated protein expression profiles, indicating the presence of metabolic gradients characteristic of liver lobules. Our results highlight the suitability of the LLoC for long-term discoid tissue cultures, specifically for iPSCs, and their differentiation in a perfused environment. We envision the LLoC as a starting point for more advanced in vitro models, allowing for the combination of multiple liver cell types to create a comprehensive liver model for disease-onchip studies. Ultimately, when combined with stem cell technology, the LLoC offers a promising and robust on-chip liver model that serves as a viable alternative to primary hepatocyte cultures—ideally suited for preclinical drug screening and personalized medicine applications.

BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences on bioRxiv

BRAPH 2 Genesis enables swift creation of custom, reproducible software distributions—tackling the growing complexity of neuroscience by streamlining analysis across diverse data types and workflows. (Image by B. Zufiria-Gerbolés and Y.-W. Chang.)
BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences
Yu-Wei Chang, Blanca Zufiria-Gerbolés, Pablo Emiliano Gómez-Ruiz, Anna Canal-Garcia, Hang Zhao, Mite Mijalkov, Joana Braga Pereira, Giovanni Volpe
bioRxiv: 10.1101/2025.04.11.648455

As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.

Computational memory capacity predicts aging and cognitive decline published in Nature Communications

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
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
Nature Communications 16, 2748 (2025)
doi: 10.1038/s41467-025-57995-0

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles published in Nanoscale

Propagation of scattered light through a scattering microscope, illustrating typical nanoparticles studied. (Image by B. García Rodriguez.)
Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles
Berenice Garcia Rodriguez, Erik Olsén, Fredrik Skärberg, Giovanni Volpe, Fredrik Höök, Daniel Sundås Midtvedt
Nanoscale, 17, 8336-8362 (2025)
arXiv: 2409.11810
doi: 10.1039/D4NR03860F

In order to relate nanoparticle properties to function, fast and detailed particle characterization, is needed. The ability to characterize nanoparticle samples using optical microscopy techniques has drastically improved over the past few decades; consequently, there are now numerous microscopy methods available for detailed characterization of particles with nanometric size. However, there is currently no “one size fits all” solution to the problem of nanoparticle characterization. Instead, since the available techniques have different detection limits and deliver related but different quantitative information, the measurement and analysis approaches need to be selected and adapted for the sample at hand. In this tutorial, we review the optical theory of single particle scattering and how it relates to the differences and similarities in the quantitative particle information obtained from commonly used microscopy techniques, with an emphasis on nanometric (submicron) sized dielectric particles. Particular emphasis is placed on how the optical signal relates to mass, size, structure, and material properties of the detected particles and to its combination with diffusivity-based particle sizing. We also discuss emerging opportunities in the wake of new technology development, with the ambition to guide the choice of measurement strategy based on various challenges related to different types of nanoparticle samples and associated analytical demands.

Diffusion models for super-resolution microscopy: a tutorial published in Journal of Physics: Photonics

Super-resolution by diffusion models: low-resolution images of microtubules (left) are transformed to high-resolution (right) by diffusion model. Dataset courtesy: BioSR Dataset. (Image by H. Bachimamchi.)
Diffusion models for super-resolution microscopy: a tutorial
Harshith Bachimanchi, Giovanni Volpe
Journal of Physics: Photonics 7, 013001 (2025)
doi: 10.1088/2515-7647/ada101
arXiv: 2409.16488

Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions in the context of super-resolution microscopy. We provide the necessary theoretical background, the essential mathematical derivations, and a detailed Python code implementation using PyTorch. We discuss the metrics to quantitatively evaluate the model, illustrate the model performance at different noise levels of the input low-resolution images, and briefly discuss how to adapt the tutorial for other applications. The code provided in this tutorial is also available as a Python notebook in the supplementary information.

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning published in ACS Sensors

Schematic illustration of the plasmonic H2 sensing principle, where the sorption of hydrogen into hydride-forming metal nanoparticles induces a change in their localized surface plasmon resonance frequency, which leads to a color change that is resolved in a spectroscopic measurement in the visible light spectral range. (Image by the Authors of the manuscript.)
Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning
Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček, Pernilla Ekborg-Tanner, Sara Nilsson, Giovanni Volpe, Paul Erhart, Christoph Langhammer
ACS Sensors 10, 376–386 (2025)
arXiv: 2312.15372
doi: 10.1021/acssensors.4c02616

Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Moreover, LEMAS provides a measure for the uncertainty of the predictions that are pivotal for safety-critical sensor applications. Our results advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.