Showing 1–50 of 95 results for author: Volpe, G

Organic Semiconductor Alignment via Confinement in Vapor-Guided Droplets

Authors: Robert Malinowski, Alessandro Rossi, Lewis M. Cowen, Peter A. Gilhooly-Finn, Michael A. Parkes, Ming-Hao Chang, Yu-Cheng Chiu, Ioannis Papakonstantinou, Matthew O. Blunt, Bob C. Schroeder, Giorgio Volpe

Abstract: Organic semiconductors are lightweight, solution-processable materials with strong potential for printed and flexible electronics, from deformable displays to wearable sensors. Despite significant advances in materials synthesis and manufacturing, controlling molecular and mesoscale alignment during deposition remains a central challenge, as film morphology critically governs charge transport and… ▽ More Organic semiconductors are lightweight, solution-processable materials with strong potential for printed and flexible electronics, from deformable displays to wearable sensors. Despite significant advances in materials synthesis and manufacturing, controlling molecular and mesoscale alignment during deposition remains a central challenge, as film morphology critically governs charge transport and device performance. Here, we demonstrate that flows developing within the intrinsically confined volume of microliter vapor-guided droplets can be harnessed to produce highly aligned organic semiconductor films. As droplets move in response to an external vapor source, internal flows align organic semiconducting nanowires within the droplet prior to deposition, yielding films with pronounced directional order. Organic field-effect transistors fabricated with this approach exhibit approximately 40% enhancement in saturation current relative to spin-coated controls. Beyond improved device performance, the contactless and compact nature of our method enables the deposition and alignment of organic semiconductors on curved and flexible surfaces. More broadly, vapor-guided droplets offer a scalable framework for the confinement-induced alignment of functional soft materials, with potential for integration into existing additive manufacturing platforms for flexible electronics and beyond. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Deep learning-enhanced Lagrangian 3D Tracking of motile microorganisms

Authors: Thierry Darnige, Daniel Midtvedt, Renaud Baillou, Benjamin Perez Estay, Changsong Wu, Alex Le Guen, Giovanni Volpe, Eric Clement

Abstract: How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, poten… ▽ More How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy -- for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior -- and thus opening new avenues for research in complex biological and ecological systems. △ Less

Submitted 21 March, 2026; originally announced March 2026.

Comments: 12 pages, 6 figures

Technological Excellence Requires Human and Social Context

Authors: Karl Palmås, Mats Benner, Monica Billger, Ben Clarke, Raimund Feifel, Julia Fernandez-Rodriguez, Anna Foka, Juliette Griffié, Claes Gustafsson, Kerstin Hamilton, Johan Holmén, Kristina Lindström, Tobias Olofsson, Joana B. Pereira, Marisa Ponti, Julia Ravanis, Sviatlana Shashkova, Emma Sparr, Pontus Strimling, Fredrik Höök, Giovanni Volpe

Abstract: Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation… ▽ More Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation is no longer tenable. We propose a broader understanding of excellence that combines technical rigor with ethical robustness, social intelligibility, and long-term relevance. The rapid emergence of generative and agentic artificial intelligence further underscores this argument. As technological systems increasingly operate through language, interpretation, and normative alignment, expertise traditionally cultivated in the humanities and social sciences becomes integral to the design, governance, and responsible deployment of such systems. Drawing on historical examples and contemporary research practices, this article examines five interconnected domains where the humanities and social sciences, treated as integrated dimensions of research practice, can strengthen technological development: (1) ethical, legal, and social integration in agenda-setting and research design; (2) plural and reflexive foresight practices that shape technological futures; (3) graduate education as a leverage point for cross-disciplinary literacy; (4) visualization and communication as epistemic and civic practices; and (5) institutional frameworks that move beyond rigid distinctions between basic and applied research. Across these dimensions, we propose practical strategies for embedding interdisciplinary collaboration structurally rather than symbolically. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 27 pages, 1 figure

Development of a Cherenkov-Based Time-of-Flight Detector Using Silicon Photomultipliers

Authors: Liliana Congedo, Giuseppe De Robertis, Antonio Di Mauro, Mario Giliberti, Francesco Licciulli, Antonio Liguori, Rocco Liotino, Leonarda Lorusso, Mario Nicola Mazziotta, Eugenio Nappi, Nicola Nicassio, Giuliana Panzarini, Roberta Pillera, Giacomo Volpe

Abstract: The aim of this work is to develop high precision Time-of-Flight (TOF) devices based on high refractive index solid Cherenkov radiators read out by silicon photomultipliers (SiPMs). Cherenkov light is prompt and therefore ideal for reaching the intrinsic timing limits of TOF systems. By utilizing a thin, high-refractive-index radiator a nearly instantaneous signal is generated by particles exceedi… ▽ More The aim of this work is to develop high precision Time-of-Flight (TOF) devices based on high refractive index solid Cherenkov radiators read out by silicon photomultipliers (SiPMs). Cherenkov light is prompt and therefore ideal for reaching the intrinsic timing limits of TOF systems. By utilizing a thin, high-refractive-index radiator a nearly instantaneous signal is generated by particles exceeding the Cherenkov threshold. In order to achieve the ultimate time resolution, we carried out a rigorous optimization of the radiator material and geometry, alongside the efficiency of the optical coupling to the SiPM sensors. The key factors limiting the time resolution were characterized by comprehensive Monte Carlo simulations, subsequently validated against experimental beam test data. We assembled small-scale prototypes instrumented with various Hamamatsu SiPM arrays sensors with pitches ranging from 1.3 to 3 mm coupled with various window materials, such as fused silica and MgF2, featuring various thickness values. The prototypes were successfully tested in beam test campaigns at the CERN-PS T10 beam line. The data were collected with a complete chain of front-end and readout electronics based on either the Petiroc 2A or the Radioroc 2 interfaced to a picoTDC to measure charges and times. By comparing the time measurements with two SiPM arrays we were able to measure a time resolution better than 33.2 ps at the full system level with a charged particle detection efficiency of 100%. Our results demonstrate the expected performance benchmarks for the charged particle detection efficiency and time resolution and highlight the potential of the developed Cherenkov-based TOF detectors for next-generation particle identification systems. △ Less

Submitted 23 February, 2026; originally announced February 2026.

Comments: 29 pages in single column; 19 figures; manuscript prepared for the issue "Silicon Photomultiplier-Based Systems for Particle and Radiation Detection" in MDPI Instruments

Efficient and precise Cherenkov-based charged particle timing using SiPMs

Authors: M. N. Mazziotta, A. Di Mauro, M. Giliberti, A. Liguori, L. Lorusso, E. Nappi, N. Nicassio, G. Panzarini, R. Pillera, G. Volpe

Abstract: Dedicated R&D efforts are currently underway to couple a thin Cherenkov radiator to Silicon Photomultiplier (SiPM) arrays for precise charged particle Time-of-Flight (ToF) measurements. The prompt nature of Cherenkov radiation makes it an ideal candidate for achieving ultimate timing performance in a ToF detector. Using a thin radiator with a high refractive index, such as fused silica, enables th… ▽ More Dedicated R&D efforts are currently underway to couple a thin Cherenkov radiator to Silicon Photomultiplier (SiPM) arrays for precise charged particle Time-of-Flight (ToF) measurements. The prompt nature of Cherenkov radiation makes it an ideal candidate for achieving ultimate timing performance in a ToF detector. Using a thin radiator with a high refractive index, such as fused silica, enables the generation of a fast signal from charged particles that exceed the Cherenkov threshold. A crucial requirement for approaching the target time resolution is the optimization of both the radiator material and thickness, as well as the optical coupling to the SiPM arrays. In this work, we present the main factors that affect the time resolution and the expected performance achieved through a detailed Monte Carlo simulation and the comparison with beam test results. △ Less

Submitted 21 January, 2026; originally announced January 2026.

Comments: 4 pages, 4 figures, Proceeding of "The 12th International Workshop on Ring Imaging Cherenkov Detectors (RICH 2025)" - September 15-19, 2025 in Mainz (Germany), Submitted to NIM A

Beam test studies for a SiPM-based RICH detector prototype for the future ALICE~3 experiment

Authors: A. R. Altamura, L. Congedo, G. De Robertis, D. Di Bari, A. Di Mauro, M. Giliberti, J. O. Guerra-Pulido, F. Licciulli, L. Lorusso, P. Martinengo, M. N. Mazziotta, E. Nappi, N. Nicassio, G. Paic, G. Panzarini, R. Pillera, G. Volpe

Abstract: The ALICE Collaboration is proposing a completely new apparatus, ALICE~3, for the LHC Runs~5 and beyond. In this context, a key subsystem for high-energy charged particle identification will be a proximity-focusing ring-imaging Cherenkov detector using aerogel as radiator and silicon photomultipliers (SiPMs) as photon sensors. We assembled a small-scale prototype instrumented with Hamamatsu S13352… ▽ More The ALICE Collaboration is proposing a completely new apparatus, ALICE~3, for the LHC Runs~5 and beyond. In this context, a key subsystem for high-energy charged particle identification will be a proximity-focusing ring-imaging Cherenkov detector using aerogel as radiator and silicon photomultipliers (SiPMs) as photon sensors. We assembled a small-scale prototype instrumented with Hamamatsu S13352 and S13361-3075AE-08 SiPM arrays, readout by custom boards equipped with front-end Petiroc 2A ASICs. The Cherenkov radiator consisted of a 2 cm thick hydrophobic aerogel tile with a refractive index of 1.03 separated from the SiPM plane by a 23 cm expansion gap. The prototype was successfully tested in a campaign at the CERN PS T10 beam line with the goal of validating the design bRICH specifications in terms to achieve the target separation power. We measured a single photon angular resolution of 3.8~mrad at the Cherenkov angle saturation value of 242~mrad, as well as the expected scaling of the angular resolution with the increasing number of detected photons. We also studied the contribution of uncorrelated and correlated background sources with respect to the signal and proved the effectiveness of time matching between charged tracks and photon hits to achieve efficient suppression of the SiPM dark count rate background. In this paper, the detector concept, the description of the tested prototype layout and the main beam test results are reported. △ Less

Submitted 18 January, 2026; originally announced January 2026.

Comments: 18 pages, 12 figures, Eur. Phys. J. C 85, 578 (2025)

Journal ref: Eur. Phys. J. C 85, 578 (2025)

Test beam performance of a novel RICH detector with timing capabilities for the future ALICE~3 PID system at LHC

Authors: M. N. Mazziotta, L. Congedo, G. De Robertis, A. Di Mauro, F. Licciulli, L. Lorusso, P. Martinengo, E. Nappi, N. Nicassio, G. Panzarini, R. Pillera, G. Volpe

Abstract: The ALICE Collaboration is proposing a completely new apparatus, ALICE 3, for the LHC Run 5 and beyond. A key subsystem for charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector consisting of an aerogel radiator and a photosensitive surface based on Silicon Photomultiplier (SiPM) arrays in a proximity-focusing configuration. A thin high-refractive index slab of transpare… ▽ More The ALICE Collaboration is proposing a completely new apparatus, ALICE 3, for the LHC Run 5 and beyond. A key subsystem for charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector consisting of an aerogel radiator and a photosensitive surface based on Silicon Photomultiplier (SiPM) arrays in a proximity-focusing configuration. A thin high-refractive index slab of transparent material (window), acting as a second Cherenkov radiator, is glued on the entrance face of the SiPM arrays to achieve precise charged particle timing. Requiring time matching between aerogel Cherenkov photon and track hits leads to an improvement of pattern recognition by discarding the uncorrelated SiPM dark count hits. In this work we present the current status of the R\&D performed for the ALICE 3 RICH detector prototype and the expected full scale system performance. A special focus will be given to the beam test results obtained with a small-scale prototype instrumented with various array of Hamamatsu SiPMs with pitches ranging from 1 to 3 mm. The Cherenkov radiator consisted of a 2 cm thick aerogel tile with a refractive index of 1.03 at 400 nm wavelength. For timing measurements SiPM arrays coupled with two different window materials (SiO$_2$ and MgF$_2$) were used. The prototype was successfully tested in beam test campaigns at the CERN PS T10 beam line. The data were collected with a complete chain of front-end and readout electronics based on the Petiroc 2A and Radioroc 2 together with a picoTDC to measure charges and times. We measured a charged particle detection efficiency above 99\% and a single photon angular resolution better than 4.2 mrad at the Cherenkov angle saturation with a time resolution better than 70 ps for charged particles. △ Less

Submitted 18 January, 2026; originally announced January 2026.

Comments: Proceeding of "6th International Workshop on New Photon-Detector (PD24)", Citation M.N. Mazziotta et al 2025 JINST 20 C05038

Journal ref: 2025 JINST 20 C05038

Development of a novel compact and fast SiPM-based RICH detector for the future ALICE 3 PID system at LHC

Authors: M. N. Mazziotta, A. R. Altamura, L. Congedo, G. De Robertis, A. Di Mauro, J. O. Guerra-Pulidoc, F. Licciulli, L. Lorusso, P. Martinengo, E. Nappi, N. Nicassio, G. Paic, G. Panzarini, R. Pillera, G. Volpe

Abstract: A dedicated R\&D is ongoing for the charged particle identification system of the \mbox{ALICE 3} experiment proposed for the LHC Run 5 and beyond. One of the subsystems for the high-energy charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector. The possibility of integrating Cherenkov-based charged particle timing measurements is currently under study. The proposed system… ▽ More A dedicated R\&D is ongoing for the charged particle identification system of the \mbox{ALICE 3} experiment proposed for the LHC Run 5 and beyond. One of the subsystems for the high-energy charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector. The possibility of integrating Cherenkov-based charged particle timing measurements is currently under study. The proposed system is based on a proximity-focusing RICH configuration including an aerogel radiator separated from a SiPM array layer by an expansion gap. A thin high-refractive index window of transparent material, acting as a second Cherenkov radiator, is glued on the SiPM array to enable time-of-flight measurements of charged particles by exploiting the yield of Cherenkov photons in the thin window. We assembled a small-scale prototype instrumented with different Hamamatsu SiPM array sensors with pitches ranging from 1 to 3 mm, readout by custom boards equipped with the front-end Petiroc 2A ASICs to measure charges and times. The primary Cherenkov radiator consisted of a 2 cm thick aerogel tile, while various window materials, including SiO$_2$ and MgF$_2$, were used as secondary Cherenkov radiators. The prototype was successfully tested in a campaign at the CERN PS T10 beam line with pions and protons. This paper summarizes the results achieved in the 2023 test beam campaign. △ Less

Submitted 18 January, 2026; originally announced January 2026.

Comments: Proceeding of "25th international Workshop on Radiation Imaging Detectors (iWoRiD2024)", JINST 20 (2025) C01001

Towards Animate Droplets: Active, Adaptive, and Autonomous

Authors: Joe Forth, Robert Malinowski, Giorgio Volpe

Abstract: Droplets, sub-millilitre liquid volumes with at least one interface, have traditionally served as compartments for storing, transporting, and delivering materials. Beyond familiar applications in food, coatings, and consumer goods, they find cutting-edge use in energy storage, sensing, and tissue engineering. The next frontier is their integration into animate matter, emerging materials defined by… ▽ More Droplets, sub-millilitre liquid volumes with at least one interface, have traditionally served as compartments for storing, transporting, and delivering materials. Beyond familiar applications in food, coatings, and consumer goods, they find cutting-edge use in energy storage, sensing, and tissue engineering. The next frontier is their integration into animate matter, emerging materials defined by their levels of activity, adaptiveness, and autonomy. Easy to produce and dispense or print into complex structures, and with enormous chemical versatility, droplets are ideal building blocks for animate matter. In this Perspective, we outline a roadmap for advancing animacy in droplets and call for a more concerted effort to integrate novel mechanisms for motility, sensing, and decision-making into droplet design. Although research on active droplets spans more than a century, achieving true autonomy, where droplets process multiple stimuli and respond without external control, remains a central challenge. We hope to inspire interdisciplinary collaboration towards applications in consumer goods, microfluidics, adaptive optics, tissue engineering, and soft robotics. △ Less

Submitted 15 December, 2025; originally announced December 2025.

Latent Space-Driven Quantification of Biofilm Formation using Time Resolved Droplet Microfluidics

Authors: Daniela Pérez Guerrero, Jesús Manuel Antúnez Domínguez, Aurélie Vigne, Daniel Midtvedt, Wylie Ahmed, Lisa D. Muiznieks, Giovanni Volpe, Caroline Beck Adiels

Abstract: Bacterial biofilms play a significant role in various fields that impact our daily lives, from detrimental public health hazards to beneficial applications in bioremediation, biodegradation, and wastewater treatment. However, high-resolution tools for studying their dynamic responses to environmental changes and collective cellular behavior remain scarce. To characterize and quantify biofilm devel… ▽ More Bacterial biofilms play a significant role in various fields that impact our daily lives, from detrimental public health hazards to beneficial applications in bioremediation, biodegradation, and wastewater treatment. However, high-resolution tools for studying their dynamic responses to environmental changes and collective cellular behavior remain scarce. To characterize and quantify biofilm development, we present a droplet-based microfluidic platform combined with an image analysis tool for in-situ studies. In this setup, Bacillus subtilis was inoculated in liquid Lysogeny Broth microdroplets, and biofilm formation was examined within emulsions at the water-oil interface. Bacteria were encapsulated in droplets, which were then trapped in compartments, allowing continuous optical access throughout biofilm formation. Droplets, each forming a distinct microenvironment, were generated at high throughput using flow-controlled pressure pumps, ensuring monodispersity. A microfluidic multi-injection valve enabled rapid switching of encapsulation conditions without disrupting droplet generation, allowing side-by-side comparison. Our platform supports fluorescence microscopy imaging and quantitative analysis of droplet content, along with time-lapse bright-field microscopy for dynamic observations. To process high-throughput, complex data, we integrated an automated, unsupervised image analysis tool based on a Variational Autoencoder (VAE). This AI-driven approach efficiently captured biofilm structures in a latent space, enabling detailed pattern recognition and analysis. Our results demonstrate the accurate detection and quantification of biofilms using thresholding and masking applied to latent space representations, enabling the precise measurement of biofilm and aggregate areas. △ Less

Submitted 10 July, 2025; originally announced July 2025.

SmartTrap: Automated Precision Experiments with Optical Tweezers

Authors: Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, Giovanni Volpe

Abstract: There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and increasing reproducibility. For example, optical tweezers are widely employed in single-molecule biophysics, cell biomechanics, and soft matter physics, but they still… ▽ More There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and increasing reproducibility. For example, optical tweezers are widely employed in single-molecule biophysics, cell biomechanics, and soft matter physics, but they still require a human operator, resulting in low throughput and limited repeatability. Here, we present a smart optical tweezers platform, which we name SmartTrap, capable of performing complex experiments completely autonomously. SmartTrap integrates real-time 3D particle tracking using deep learning, custom electronics for precise feedback control, and a microfluidic setup for particle handling. We demonstrate the ability of SmartTrap to operate continuously, acquiring high-precision data over extended periods of time, through a series of experiments. By bridging the gap between manual experimentation and autonomous operation, SmartTrap establishes a robust and open source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics, and colloidal science with reduced experimental overhead and operator bias. △ Less

Submitted 8 May, 2025; originally announced May 2025.

Comments: 23 pages, 12 figures

Global graph features unveiled by unsupervised geometric deep learning

Authors: Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, Giovanni Volpe

Abstract: Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce GAUDI (Graph Autoencoder Uncovering Descriptive Information), a novel unsupervised geometric deep learning framework designed to capture both local details and global structure. GAUDI employs an in… ▽ More Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce GAUDI (Graph Autoencoder Uncovering Descriptive Information), a novel unsupervised geometric deep learning framework designed to capture both local details and global structure. GAUDI employs an innovative hourglass architecture with hierarchical pooling and upsampling layers linked through skip connections, which preserve essential connectivity information throughout the encoding-decoding process. Even though identical or highly similar underlying parameters describing a system's state can lead to significant variability in graph realizations, GAUDI consistently maps them into nearby regions of a structured and continuous latent space, effectively disentangling invariant process-level features from stochastic noise. We demonstrate GAUDI's versatility across multiple applications, including small-world networks modeling, characterization of protein assemblies from super-resolution microscopy, analysis of collective motion in the Vicsek model, and identification of age-related changes in brain connectivity. Comparison with related approaches highlights GAUDI's superior performance in analyzing complex graphs, providing new insights into emergent phenomena across diverse scientific domains. △ Less

Submitted 26 February, 2026; v1 submitted 7 March, 2025; originally announced March 2025.

Comments: 28 pages, 6 figures

U-Net 3+ for Anomalous Diffusion Analysis enhanced with Mixture Estimates (U-AnD-ME) in particle-tracking data

Authors: Solomon Asghar, Ran Ni, Giorgio Volpe

Abstract: Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain critical information about these biolog… ▽ More Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain critical information about these biologically significant processes. However, standard approaches for characterizing anomalous diffusion from these measurements often struggle in cases of practical interest, e.g. due to short, noisy trajectories. Fully exploiting this data therefore requires the development of advanced analysis methods -- a core goal at the heart of the recent international Anomalous Diffusion Challenges. Here, we introduce a novel machine-learning framework, U-net 3+ for Anomalous Diffusion analysis enhanced with Mixture Estimates (U-AnD-ME), that applies a U-Net 3+ based neural network alongside Gaussian mixture models to enable highly accurate characterisation of single-particle tracking data. In the 2024 Anomalous Diffusion Challenge, U-AnD-ME outperformed all other participating methods for the analysis of two-dimensional anomalous diffusion trajectories at both single-trajectory and ensemble levels. Using a large dataset inspired by the Challenge, we further characterize the performance of U-AnD-ME in segmenting trajectories and inferring anomalous diffusion properties. △ Less

Submitted 26 February, 2025; originally announced February 2025.

Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials

Authors: Paolo Polimeno, Francesco Patti, Melissa Infusino, Jonathan Sanchez, Maria A. Iati, Rosalba Saija, Giovanni Volpe, Onofrio M. Marago, Alessandro Veltri

Abstract: We investigate gain-assisted optical forces on dye-enriched silver nanoshell in the quasi-static limit by means of a theoretical/numerical approach. We demonstrate the onset of nonlinear optical trapping of these resonant nanostructures in a counterpropagating Gaussian beam configuration. We study the optical forces and trapping behaviour as a function of wavelength, particle gain level, and laser… ▽ More We investigate gain-assisted optical forces on dye-enriched silver nanoshell in the quasi-static limit by means of a theoretical/numerical approach. We demonstrate the onset of nonlinear optical trapping of these resonant nanostructures in a counterpropagating Gaussian beam configuration. We study the optical forces and trapping behaviour as a function of wavelength, particle gain level, and laser power. We support the theoretical analysis with Brownian dynamics simulations that show how particle position locking is achieved at high gains in extended optical trapping potentials. Finally, for wavelengths blue-detuned with respect to the plasmon-enhanced resonance,we observe particle channeling by the standing wave antinodes due to gradient force reversal. This work opens perspectives for gain-assisted optomechanics where nonlinear optical forces are finely tuned to efficiently trap, manipulate, channel, and deliver externally controlled nanophotonic system. △ Less

Submitted 21 February, 2025; originally announced February 2025.

Comments: 25 pages, 5 figures

Journal ref: ACS Photonics 2020, 7, 5, 1262-1270

Retina electronic paper with video-rate-tunable 45000 pixels per inch

Authors: Ade Satria Saloka Santosa, Yu-Wei Chang, Andreas B. Dahlin, Lars Osterlund, Giovanni Volpe, Kunli Xiong

Abstract: 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… ▽ More 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. △ Less

Submitted 5 February, 2025; originally announced February 2025.

Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics

Authors: Alessia Gentili, Rainer Klages, Giorgio Volpe

Abstract: Living organisms have developed advanced motion strategies for efficient space exploration, serving as inspiration for the movements of microrobots. These real-life strategies often involve anomalous dynamics displaying random movement patterns that deviate from Brownian motion. Despite their biological inspiration, autonomous stochastic navigation strategies of current microrobots remain much les… ▽ More Living organisms have developed advanced motion strategies for efficient space exploration, serving as inspiration for the movements of microrobots. These real-life strategies often involve anomalous dynamics displaying random movement patterns that deviate from Brownian motion. Despite their biological inspiration, autonomous stochastic navigation strategies of current microrobots remain much less versatile than those of their living counterparts. Supported by theoretical reasoning, this work demonstrates superparamagnetic colloidal microrobots with fully customizable stochastic dynamics displaying the entire spectrum of anomalous diffusion, from subdiffusion to superdiffusion, across statistically significant spatial and temporal scales (covering at least two decades). By simultaneously tuning microrobots' step-length distribution and, critically, their velocity autocorrelation function with magnetic fields, fundamental anomalous dynamics are reproduced with tailored properties mimicking Lévy walks and fractional Brownian motion. These findings pave the way for programmable microrobotic systems that replicate optimal stochastic navigation strategies found in nature for applications in medical robotics and environmental remediation. △ Less

Submitted 5 November, 2025; v1 submitted 18 December, 2024; originally announced December 2024.

Journal ref: Small, e06538 (2025)

Enhanced Spatial Clustering of Single-Molecule Localizations with Graph Neural Networks

Authors: Jesús Pineda, Sergi Masó-Orriols, Montse Masoliver, Joan Bertran, Mattias Goksör, Giovanni Volpe, Carlo Manzo

Abstract: Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Mult… ▽ More Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO's transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO's robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data. △ Less

Submitted 11 December, 2025; v1 submitted 29 November, 2024; originally announced December 2024.

Comments: 47 pages, 5 main figures, 3 table, 3 supplementary figures, 9 supplementary tables. This is the author's version of the article published in Nature Communications under CC BY 4.0. The final published version is available at https://doi.org/10.1038/s41467-025-65557-7

Journal ref: Nat Commun 16, 9693 (2025)

Microscopic Geared Mechanisms

Authors: Gan Wang, Marcel Rey, Antonio Ciarlo, Mahdi Shanei, Kunli Xiong, Giuseppe Pesce, Mikael Käll, Giovanni Volpe

Abstract: 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 d… ▽ More 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. △ Less

Submitted 25 September, 2024; originally announced September 2024.

Journal ref: Nature Communications volume 16, Article number: 7767 (2025)

Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles

Authors: Berenice Garcia Rodriguez, Erik Olsén, Fredrik Skärberg, Giovanni Volpe, Fredrik Höök, Daniel Sundås Midtvedt

Abstract: 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 cu… ▽ More 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. △ Less

Submitted 18 September, 2024; originally announced September 2024.

Tutorial for the growth and development of Myxococcus xanthus as a Model System at the Intersection of Biology and Physics

Authors: Jesus Manuel Antúnez Domínguez, Laura Pérez García, Natsuko Rivera-Yoshida, Jasmin Di Franco, David Steiner, Alejandro V. Arzola, Mariana Benítez, Charlotte Hamngren Blomqvist, Roberto Cerbino, Caroline Beck Adiels, Giovanni Volpe

Abstract: Myxococcus xanthus is a unicellular organism whose cells possess the ability to move and communicate, leading to the emergence of complex collective properties and behaviours. This has made it an ideal model system to study the emergence of collective behaviours in interdisciplinary research efforts lying at the intersection of biology and physics, especially in the growing field of active matter… ▽ More Myxococcus xanthus is a unicellular organism whose cells possess the ability to move and communicate, leading to the emergence of complex collective properties and behaviours. This has made it an ideal model system to study the emergence of collective behaviours in interdisciplinary research efforts lying at the intersection of biology and physics, especially in the growing field of active matter research. Often, challenges arise when setting up reliable and reproducible culturing protocols. This tutorial provides a clear and comprehensive guide on the culture, growth, development, and experimental sample preparation of \textit{M. xanthus}. Additionally, it includes some representative examples of experiments that can be conducted using these samples, namely motility assays, fruiting body formation, predation, and elasticotaxis. △ Less

Submitted 26 July, 2024; originally announced July 2024.

Roadmap for Animate Matter

Authors: 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 , et al. (15 additional authors not shown)

Abstract: 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 tra… ▽ More 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 across various sectors. 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 encouraging 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. △ Less

Submitted 10 September, 2024; v1 submitted 15 July, 2024; originally announced July 2024.

Transverse optical gradient force in untethered rotating metaspinners

Authors: Einstom Engay, Mahdi Shanei, Vasilii Mylnikov, Gan Wang, Peter Johansson, Giovanni Volpe, Mikael Käll

Abstract: Nanostructured dielectric metasurfaces offer unprecedented opportunities to control light-matter momentum exchange, and thereby the forces and torques that light can exert on matter. Here we introduce optical metasurfaces as components of ultracompact untethered microscopic metaspinners capable of efficient light-induced rotation in a liquid environment. Illuminated by weakly focused light, a meta… ▽ More Nanostructured dielectric metasurfaces offer unprecedented opportunities to control light-matter momentum exchange, and thereby the forces and torques that light can exert on matter. Here we introduce optical metasurfaces as components of ultracompact untethered microscopic metaspinners capable of efficient light-induced rotation in a liquid environment. Illuminated by weakly focused light, a metaspinner generates torque via photon recoil through the metasurfaces ability to bend light towards high angles despite their sub-wavelength thickness, thereby creating orbital angular momentum. We find that a metaspinner is subject to an anomalous transverse lateral optical gradient force that acts in concert with the classical gradient force. Consequently, when two or more metaspinners are trapped together in a laser beam, they collectively orbit the optical axis in the opposite direction to their spinning motion, in stark contrast to rotors coupled through hydrodynamic or mechanical interactions. The metaspinners delineated herein not only serve to illustrate the vast possibilities of utilizing optical metasurfaces for fundamental exploration of optical torques, but they also represent potential building-blocks of artificial active matter systems, light-driven micromachinery, and general-purpose optomechanical devices. △ Less

Submitted 16 December, 2024; v1 submitted 17 June, 2024; originally announced June 2024.

Comments: 37 pages, 5 figures

Deep Learning for Optical Tweezers

Authors: Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M. Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe, Mattias Goksör

Abstract: Optical tweezers exploit light--matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and anal… ▽ More Optical tweezers exploit light--matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and analysis of the trapped objects, often outperforming classical methods thanks to the higher computational speed and versatility of deep learning. Here, we review how deep learning has already remarkably improved optical tweezers, while exploring the exciting, new future possibilities enabled by this dynamic synergy. Furthermore, we offer guidelines on integrating deep learning with optical trapping and optical manipulation in a reliable and trustworthy way. △ Less

Submitted 4 January, 2024; originally announced January 2024.

Comments: 19 pages, 7 figures, 1 table

MSC Class: 78-02

Efficient Rare Event Sampling with Unsupervised Normalising Flows

Authors: Solomon Asghar, Qing-Xiang Pei, Giorgio Volpe, Ran Ni

Abstract: From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as \emph{rare events}, the study of which is essential for understanding and controlling these systems' properties. Classical computational methods to sample rare events remain prohibitively inefficient, and are a bottleneck fo… ▽ More From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as \emph{rare events}, the study of which is essential for understanding and controlling these systems' properties. Classical computational methods to sample rare events remain prohibitively inefficient, and are a bottleneck for enhanced samplers requiring prior data. Here, we introduce a novel framework, FlowRES that uses unsupervised normalising flow neural networks to enhance Monte Carlo sampling of rare events by generating high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling the transition path ensembles of equilibrium and non-equilibrium systems of Brownian particles exploring increasingly complex potential surfaces. Beyond eliminating requirements for prior data, FlowRES features key advantages over established samplers: no collective variables need defining, its efficiency remains constant even as events become increasingly unlikely, and it can handle systems with multiple routes between states. △ Less

Submitted 2 January, 2024; originally announced January 2024.

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

Authors: Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček, Pernilla Ekborg-Tanner, Sara Nilsson, Giovanni Volpe, Paul Erhart, Christoph Langhammer

Abstract: The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble M… ▽ More The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble Model for Accelerated Sensing (LEMAS) accelerates the response of a state-of-the-art optical plasmonic hydrogen sensor by up to a factor of 40 in an oxygen-free inert gas environment, by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Furthermore, it eliminates the pressure dependence of the response intrinsic to metal hydride-based sensors, while leveraging their ability to operate in oxygen-starved environments that are proposed to be used for inert gas encapsulation systems of hydrogen installations. Moreover LEMAS provides a measure for the uncertainty of the predictions that is pivotal for safety-critical sensor applications. Our results thus advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection. △ Less

Submitted 23 December, 2023; originally announced December 2023.

Comments: 11 pages; 5 figures

Journal ref: ACS Sensors 10, 376 (2025)

Quantitative evaluation of methods to analyze motion changes in single-particle experiments

Authors: 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 , et al. (11 additional authors not shown)

Abstract: 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 perform… ▽ More 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. △ Less

Submitted 12 August, 2025; v1 submitted 29 November, 2023; originally announced November 2023.

Comments: 37 pages, 8 figures. This is the author's version of the article published in Nature Communications under CC BY 4.0. The final published version is available at https://doi.org/10.1038/s41467-025-61949-x

Journal ref: Nat Commun 16, 6749 (2025)

Patchy landscapes promote stability of small groups

Authors: Gianni Jacucci, Davide Breoni, Sandrine Heijnen, José Palomo, Philip Jones, Hartmut Löwen, Giorgio Volpe, Sylvain Gigan

Abstract: Group formation and coordination are fundamental characteristics of living systems, essential for performing tasks and ensuring survival. Interactions between individuals play a key role in group formation, and the impact of resource distributions is a vibrant area of research. Using active particles in a tuneable optical environment as a model system, we demonstrate that heterogeneous energy sour… ▽ More Group formation and coordination are fundamental characteristics of living systems, essential for performing tasks and ensuring survival. Interactions between individuals play a key role in group formation, and the impact of resource distributions is a vibrant area of research. Using active particles in a tuneable optical environment as a model system, we demonstrate that heterogeneous energy source distributions result in smaller, more stable groups with reduced individual exchange between clusters compared to homogeneous conditions. Reduced group sizes can be beneficial to optimise resources in heterogeneous environments and to control information flow within populations. Devoid of biological complications, our system provides insights into the importance of patchy landscapes in ecological dynamics and holds implications for refining swarm intelligence algorithms and enhancing crowd control techniques. △ Less

Submitted 2 October, 2023; originally announced October 2023.

Comments: 12 pages, 9 figures

Deep-learning-powered data analysis in plankton ecology

Authors: Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert, Pierre De Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander, Giovanni Volpe

Abstract: The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimm… ▽ More The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data. △ Less

Submitted 15 September, 2023; originally announced September 2023.

Comments: For the associated GitHub repository, see https://github.com/softmatterlab/Deep-learning-in-plankton-ecology

Dual-angle interferometric scattering microscopy for optical multiparametric particle characterization

Authors: Erik Olsén, Berenice Garcia, Fredrik Skärberg, Petteri Parkkila, Giovanni Volpe, Fredrik Höök, Daniel Midtvedt

Abstract: Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size… ▽ More Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that hydrodynamic radius relates to the outer particle radius while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by optically differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single particle level. △ Less

Submitted 14 September, 2023; originally announced September 2023.

Designing thermoplasmonic polymersomes for photothermal therapy

Authors: Valentino Barbieri, Javier González-Colsa, Diana Matias, Aroa Duro-Castano, Anshu Thapa, Lorena Ruiz-Perez, Pablo Albella, Giorgio Volpe, Giuseppe Battaglia

Abstract: Polymersomes, vesicles self-assembled from amphiphilic polymers, are promising nanocarriers for the targeted intracellular delivery of therapeutics. Integrating inorganic light-absorbing materials with plasmonic properties, such as gold, into their membrane by in situ synthesis is a stepping stone to enable their use for photothermal therapy. Yet, it still needs to be determined whether the in sit… ▽ More Polymersomes, vesicles self-assembled from amphiphilic polymers, are promising nanocarriers for the targeted intracellular delivery of therapeutics. Integrating inorganic light-absorbing materials with plasmonic properties, such as gold, into their membrane by in situ synthesis is a stepping stone to enable their use for photothermal therapy. Yet, it still needs to be determined whether the in situ synthesis of gold can produce polymersomes with thermoplasmonic properties without altering their morphology, stability, and nanocarrier functionality. Here we demonstrate that small gold nanoparticles can be controllably nucleated within biocompatible block-copolymer membranes to design hybrid polymersomes with a noteworthy thermoplasmonic response. The cumulative absorption of individual 2 nm gold nanoparticles can induce temperature increases in 10 K in dilute suspensions of hybrid polymersomes upon laser illumination. Furthermore, we develop a theoretical model to rationalize our observations and predict the thermoplasmonic response of our hybrid polymersomes. We finally demonstrate in vitro photothermal therapy of cancer cells, enhanced by the receptor-mediated endocytosis of our hybrid polymersomes. We envision that our nanotechnological platform can be translated to phenotypic cell targeting to precisely deliver effective photothermal agents. △ Less

Submitted 30 July, 2023; originally announced July 2023.

Optimal face-to-face coupling for fast self-folding kirigami

Authors: Maks Pecnik Bambic, Nuno A. M. Araújo, Benjamin J. Walker, Duncan R. Hewitt, Qing Xiang Pei, Ran Ni, Giorgio Volpe

Abstract: Kirigami-inspired designs can enable self-folding three-dimensional materials from flat, two-dimensional sheets. Hierarchical designs of connected levels increase the diversity of possible target structures, yet they can lead to longer folding times in the presence of fluctuations. Here, we study the effect of rotational coupling between levels on the self-folding of two-level kirigami designs dri… ▽ More Kirigami-inspired designs can enable self-folding three-dimensional materials from flat, two-dimensional sheets. Hierarchical designs of connected levels increase the diversity of possible target structures, yet they can lead to longer folding times in the presence of fluctuations. Here, we study the effect of rotational coupling between levels on the self-folding of two-level kirigami designs driven by thermal noise in a fluid. Naturally present due to hydrodynamic resistance, we find that optimization of this coupling as control parameter can significantly improve a structure's self-folding rate and yield. △ Less

Submitted 9 July, 2023; originally announced July 2023.

Journal ref: Soft Matter (2024)

Optimal calibration of optical tweezers with arbitrary integration time and sampling frequencies -- A general framework

Authors: Laura Pérez-Garcéa, Martin Selin, Antonio Ciarlo, Alessandro Magazzu, Giuseppe Pesce, Antonio Sasso, Giovanni Volpe, Isaac Pérez Castillo, Alejandro V. Arzola

Abstract: Optical tweezers (OT) have become an essential technique in several fields of physics, chemistry, and biology as precise micromanipulation tools and microscopic force transducers. Quantitative measurements require the accurate calibration of the trap stiffness of the optical trap and the diffusion constant of the optically trapped particle. This is typically done by statistical estimators construc… ▽ More Optical tweezers (OT) have become an essential technique in several fields of physics, chemistry, and biology as precise micromanipulation tools and microscopic force transducers. Quantitative measurements require the accurate calibration of the trap stiffness of the optical trap and the diffusion constant of the optically trapped particle. This is typically done by statistical estimators constructed from the position signal of the particle, which is recorded by a digital camera or a quadrant photodiode. The finite integration time and sampling frequency of the detector need to be properly taken into account. Here, we present a general approach based on the joint probability density function of the sampled trajectory that corrects exactly the biases due to the detector's finite integration time and limited sampling frequency, providing theoretical formulas for the most widely employed calibration methods: equipartition, mean squared displacement, autocorrelation, power spectral density, and force reconstruction via maximum-likelihood-estimator analysis (FORMA). Our results, tested with experiments and Monte Carlo simulations, will permit users of OT to confidently estimate the trap stiffness and diffusion constant, extending their use to a broader set of experimental conditions. △ Less

Submitted 12 May, 2023; originally announced May 2023.

Comments: 6 figures

Optically Driven Janus Micro Engine with Full Orbital Motion Control

Authors: David Bronte Ciriza, Agnese Callegari, Maria Grazia Donato, Berk Çiçek, Alessandro Magazzù, Iryna Kasianiuk, Denis Kasianiuk, Falko Schmidt, Antonino Foti, Pietro G. Gucciardi, Giovanni Volpe, Maurizio Lanza, Luca Biancofiore, Onofrio M. Maragò

Abstract: Microengines have shown promise for a variety of applications in nanotechnology, microfluidics and nanomedicine, including targeted drug delivery, microscale pumping, and environmental remediation. However, achieving precise control over their dynamics remains a significant challenge. In this study, we introduce a microengine that exploits both optical and thermal effects to achieve a high degree… ▽ More Microengines have shown promise for a variety of applications in nanotechnology, microfluidics and nanomedicine, including targeted drug delivery, microscale pumping, and environmental remediation. However, achieving precise control over their dynamics remains a significant challenge. In this study, we introduce a microengine that exploits both optical and thermal effects to achieve a high degree of controllability. We find that in the presence of a strongly focused light beam, a gold-silica Janus particle becomes confined at the stationary point where the optical and thermal forces balance. By using circularly polarized light, we can transfer angular momentum to the particle breaking the symmetry between the two forces and resulting in a tangential force that drives directed orbital motion. We can simultaneously control the velocity and direction of rotation of the particle changing the ellipticity of the incoming light beam, while tuning the radius of the orbit with laser power. Our experimental results are validated using a geometrical optics phenomenological model that considers the optical force, the absorption of optical power, and the resulting heating of the particle. The demonstrated enhanced flexibility in the control of microengines opens up new possibilities for their utilization in a wide range of applications, encompassing microscale transport, sensing, and actuation. △ Less

Submitted 12 July, 2023; v1 submitted 11 May, 2023; originally announced May 2023.

Comments: 30 pages, 12 figures

Label-free optical quantification of material composition of suspended virus-gold nanoparticle complexes

Authors: Erik Olsén, Benjamin Midtvedt, Adrián González, Fredrik Eklund, Katarzyna Ranoszek-Soliwoda, Jaroslaw Grobelny, Giovanni Volpe, Malgorzata Krzyzowska, Fredrik Höök, Daniel Midtvedt

Abstract: The interaction between metallic and biological nanoparticles (NPs) is widely used in various biotechnology and biomedical applications. However, detailed characterization of this type of interaction is challenging due to a lack of high-throughput techniques that can quantify both size and composition of suspended NP complexes. Here, we introduce a technique called ``twilight nanoparticle tracking… ▽ More The interaction between metallic and biological nanoparticles (NPs) is widely used in various biotechnology and biomedical applications. However, detailed characterization of this type of interaction is challenging due to a lack of high-throughput techniques that can quantify both size and composition of suspended NP complexes. Here, we introduce a technique called ``twilight nanoparticle tracking analysis'' (tNTA) and demonstrate that it provides a quantitative relationship between the measured optical signal and the composition of suspended dielectric-metal NP complexes. We assess the performance of tNTA by analyzing the selective binding of tannic acid-modified gold nanoparticles (TaAuNPs) to herpes simplex viruses (HSV). Our results show that TaAuNPs bind specifically to HSV without causing substantial changes in the size or refractive index of the virus, suggesting that the binding does not cause virus disruption. Instead, the anti-viral properties of TaAuNPs appear to stem from direct particle binding to the virus. △ Less

Submitted 15 April, 2023; originally announced April 2023.

Roadmap on Deep Learning for Microscopy

Authors: Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C. D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu , et al. (50 additional authors not shown)

Abstract: Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the… ▽ More Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. △ Less

Submitted 7 March, 2023; originally announced March 2023.

Preface: Characterisation of Physical Processes from Anomalous Diffusion Data

Authors: Carlo Manzo, Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Maciej Lewenstein, Ralf Metzler

Abstract: Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge.org ) and published in Journal of Physics A: Mathematical and Theoretical. The list of articles included in the special issue can be accessed at https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-P… ▽ More Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge.org ) and published in Journal of Physics A: Mathematical and Theoretical. The list of articles included in the special issue can be accessed at https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data . △ Less

Submitted 7 January, 2023; v1 submitted 2 January, 2023; originally announced January 2023.

Comments: Preface to the Special Issue "Characterisation of Physical Processes from Anomalous Diffusion Data", Journal of Physics A: Mathematical and Theoretical https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data

ATHENA Detector Proposal -- A Totally Hermetic Electron Nucleus Apparatus proposed for IP6 at the Electron-Ion Collider

Authors: ATHENA Collaboration, J. Adam, L. Adamczyk, N. Agrawal, C. Aidala, W. Akers, M. Alekseev, M. M. Allen, F. Ameli, A. Angerami, P. Antonioli, N. J. Apadula, A. Aprahamian, W. Armstrong, M. Arratia, J. R. Arrington, A. Asaturyan, E. C. Aschenauer, K. Augsten, S. Aune, K. Bailey, C. Baldanza, M. Bansal, F. Barbosa, L. Barion , et al. (415 additional authors not shown)

Abstract: ATHENA has been designed as a general purpose detector capable of delivering the full scientific scope of the Electron-Ion Collider. Careful technology choices provide fine tracking and momentum resolution, high performance electromagnetic and hadronic calorimetry, hadron identification over a wide kinematic range, and near-complete hermeticity. This article describes the detector design and its e… ▽ More ATHENA has been designed as a general purpose detector capable of delivering the full scientific scope of the Electron-Ion Collider. Careful technology choices provide fine tracking and momentum resolution, high performance electromagnetic and hadronic calorimetry, hadron identification over a wide kinematic range, and near-complete hermeticity. This article describes the detector design and its expected performance in the most relevant physics channels. It includes an evaluation of detector technology choices, the technical challenges to realizing the detector and the R&D required to meet those challenges. △ Less

Submitted 13 October, 2022; originally announced October 2022.

Journal ref: JINST 17 (2022) 10, P10019

Playing with Active Matter

Authors: Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe

Abstract: In the last 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. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are per… ▽ More In the last 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. This field deals with active particles, which, differently from passive 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, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment. △ Less

Submitted 9 September, 2022; originally announced September 2022.

Comments: 11 pages, 5 figures

Faster and more accurate geometrical-optics optical force calculation using neural networks

Authors: David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò

Abstract: Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonst… ▽ More Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise. △ Less

Submitted 5 September, 2022; originally announced September 2022.

Roadmap for Optical Tweezers

Authors: Giovanni Volpe, Onofrio M. Maragò, Halina Rubinzstein-Dunlop, Giuseppe Pesce, Alexander B. Stilgoe, Giorgio Volpe, Georgiy Tkachenko, Viet Giang Truong, Síle Nic Chormaic, Fatemeh Kalantarifard, Parviz Elahi, Mikael Käll, Agnese Callegari, Manuel I. Marqués, Antonio A. R. Neves, Wendel L. Moreira, Adriana Fontes, Carlos L. Cesar, Rosalba Saija, Abir Saidi, Paul Beck, Jörg S. Eismann, Peter Banzer, Thales F. D. Fernandes, Francesco Pedaci , et al. (58 additional authors not shown)

Abstract: Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force… ▽ More Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration. △ Less

Submitted 28 June, 2022; originally announced June 2022.

Comments: 181 pages, 61 figures

Steering self-organisation through confinement

Authors: Nuno A. M. Araújo, Liesbeth M. C. Janssen, Thomas Barois, Guido Boffetta, Itai Cohen, Alessandro Corbetta, Olivier Dauchot, Marjolein Dijkstra, William M. Durham, Audrey Dussutour, Simon Garnier, Hanneke Gelderblom, Ramin Golestanian, Lucio Isa, Gijsje H. Koenderink, Hartmut Löwen, Ralf Metzler, Marco Polin, C. Patrick Royall, Anđela Šarić, Anupam Sengupta, Cécile Sykes, Vito Trianni, Idan Tuval, Nicolas Vogel , et al. (4 additional authors not shown)

Abstract: Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and contro… ▽ More Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement occurs through interactions with boundaries, and can function as either a catalyst or inhibitor of self-organisation. It can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework for future research, we examine the role of confinement in self-organisation and identify overarching scientific challenges across disciplines that need to be addressed to harness its full scientific and technological potential. This framework will not only accelerate the generation of a common deeper understanding of self-organisation but also trigger the development of innovative strategies to steer it through confinement, with impact, e.g., on the design of smarter materials, tissue engineering for biomedicine and crowd management. △ Less

Submitted 21 April, 2022; originally announced April 2022.

Journal ref: Soft Matter, 19, 1695 (2023)

Single-shot self-supervised particle tracking

Authors: Benjamin Midtvedt, Jesús Pineda, Fredrik Skärberg, Erik Olsén, Harshith Bachimanchi, Emelie Wesén, Elin K. Esbjörner, Erik Selander, Fredrik Höök, Daniel Midtvedt, Giovanni Volpe

Abstract: Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on either vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, the data pr… ▽ More Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on either vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, the data produced by experiments are often challenging to label and cannot be easily reproduced numerically. Here, we propose a novel deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And Regression), that learns to tracks objects with sub-pixel accuracy from a single unlabeled experimental image. This is made possible by exploiting the inherent roto-translational symmetries of the data. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we analyze challenging experimental data containing densely packed cells or noisy backgrounds. We also exploit additional symmetries to extend the measurable particle properties to the particle's vertical position by propagating the signal in Fourier space and its polarizability by scaling the signal strength. Thanks to the ability to train deep-learning models with a single unlabeled image, LodeSTAR can accelerate the development of high-quality microscopic analysis pipelines for engineering, biology, and medicine. △ Less

Submitted 28 February, 2022; originally announced February 2022.

Comments: 19 pages, 4 figures

Tunable critical Casimir forces counteract Casimir-Lifshitz attraction

Authors: Falko Schmidt, Agnese Callegari, Abdallah Daddi-Moussa-Ider, Battulga Munkhbat, Ruggero Verre, Timur Shegai, Mikael Käll, Hartmut Löwen, Andrea Gambassi, Giovanni Volpe

Abstract: 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 m… ▽ More 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. This experimental demonstration can accelerate the development of micro- and nanodevices by preventing stiction as well as providing active control and precise tunability of the forces acting between their constituent parts. △ Less

Submitted 22 February, 2022; originally announced February 2022.

Comments: 27 pages, 5 figures

Microplankton life histories revealed by holographic microscopy and deep learning

Authors: Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, Giovanni Volpe

Abstract: The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards 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 understand… ▽ More The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards 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 oceanic 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. This method is particularly useful to explore the flux of carbon through micro-zooplankton, the most important and least known group of primary consumers in the global oceans. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division. △ Less

Submitted 18 February, 2022; originally announced February 2022.

Comments: 20 pages, 4 figure, 5 supplementary figure

Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion

Authors: Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo

Abstract: The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysi… ▽ More The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here, we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically-relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments. △ Less

Submitted 13 February, 2022; originally announced February 2022.

Comments: 17 pages, 5 figure, 2 supplementary figures

Self-organized lasers of reconfigurable colloidal assemblies

Authors: Manish Trivedi, Dhruv Saxena, Wai Kit Ng, Riccardo Sapienza, Giorgio Volpe

Abstract: Biological cells self-organize into living materials that uniquely blend structure with functionality and responsiveness to the environment. The integration of similar life-like features in man-made materials remains challenging, yet desirable to manufacture active, adaptive and autonomous systems. Here we show the self-organization of programmable random lasers from the reversible out-of-equilibr… ▽ More Biological cells self-organize into living materials that uniquely blend structure with functionality and responsiveness to the environment. The integration of similar life-like features in man-made materials remains challenging, yet desirable to manufacture active, adaptive and autonomous systems. Here we show the self-organization of programmable random lasers from the reversible out-of-equilibrium self-assembly of colloids. Random lasing originates from the optical amplification of light undergoing multiple scattering within the dissipative colloidal assemblies and therefore is crucially dependent on their self-organization behavior. Under external light stimuli, these dynamic random lasers are responsive and present a continuously tunable laser threshold. They can thus reconfigure and cooperate by emulating the ever-evolving spatiotemporal relationship between structure and functionality typical of living matter. △ Less

Submitted 14 January, 2022; originally announced January 2022.

Journal ref: Nature Physics 18, 939-944 (2022)

Objective comparison of methods to decode anomalous diffusion

Authors: Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás Firbas, Òscar Garibo i Orts, Alessia Gentili, Zihan Huang, Jae-Hyung Jeon, Hélène Kabbech, Yeongjin Kim, Patrycja Kowalek, Diego Krapf, Hanna Loch-Olszewska, Michael A. Lomholt, Jean-Baptiste Masson, Philipp G. Meyer, Seongyu Park, Borja Requena, Ihor Smal , et al. (9 additional authors not shown)

Abstract: Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest… ▽ More Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. △ Less

Submitted 21 August, 2025; v1 submitted 14 May, 2021; originally announced May 2021.

Comments: 96 pages, 5 main figures, 1 table, 29 supplementary figures. This is the author's version of the article published in Nature Communications under CC BY 4.0. The final published version is available at https://doi.org/10.1038/s41467-021-26320-w

Journal ref: Nat Commun 12, 6253 (2021)

Classification, inference and segmentation of anomalous diffusion with recurrent neural networks

Authors: Aykut Argun, Giovanni Volpe, Stefano Bo

Abstract: Countless systems in biology, physics, and finance undergo diffusive dynamics. Many of these systems, including biomolecules inside cells, active matter systems and foraging animals, exhibit anomalous dynamics where the growth of the mean squared displacement with time follows a power law with an exponent that deviates from $1$. When studying time series recording the evolution of these systems, i… ▽ More Countless systems in biology, physics, and finance undergo diffusive dynamics. Many of these systems, including biomolecules inside cells, active matter systems and foraging animals, exhibit anomalous dynamics where the growth of the mean squared displacement with time follows a power law with an exponent that deviates from $1$. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks can be overwhelmingly difficult when only few short trajectories are available, a situation that is common in the study of non-equilibrium and living systems. Here, we present a data-driven method to analyze single anomalous diffusion trajectories employing recurrent neural networks, which we name RANDI. We show that our method can successfully infer the anomalous exponent, identify the type of anomalous diffusion process, and segment the trajectories of systems switching between different behaviors. We benchmark our performance against the state-of-the art techniques for the study of single short trajectories that participated in the Anomalous Diffusion (AnDi) Challenge. Our method proved to be the most versatile method, being the only one to consistently rank in the top 3 for all tasks proposed in the AnDi Challenge. △ Less

Submitted 1 April, 2021; originally announced April 2021.

Science Requirements and Detector Concepts for the Electron-Ion Collider: EIC Yellow Report

Authors: R. Abdul Khalek, A. Accardi, J. Adam, D. Adamiak, W. Akers, M. Albaladejo, A. Al-bataineh, M. G. Alexeev, F. Ameli, P. Antonioli, N. Armesto, W. R. Armstrong, M. Arratia, J. Arrington, A. Asaturyan, M. Asai, E. C. Aschenauer, S. Aune, H. Avagyan, C. Ayerbe Gayoso, B. Azmoun, A. Bacchetta, M. D. Baker, F. Barbosa, L. Barion , et al. (390 additional authors not shown)

Abstract: This report describes the physics case, the resulting detector requirements, and the evolving detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC will be a powerful new high-luminosity facility in the United States with the capability to collide high-energy electron beams with high-energy proton and ion beams, providing access to those regions in the nucleon… ▽ More This report describes the physics case, the resulting detector requirements, and the evolving detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC will be a powerful new high-luminosity facility in the United States with the capability to collide high-energy electron beams with high-energy proton and ion beams, providing access to those regions in the nucleon and nuclei where their structure is dominated by gluons. Moreover, polarized beams in the EIC will give unprecedented access to the spatial and spin structure of the proton, neutron, and light ions. The studies leading to this document were commissioned and organized by the EIC User Group with the objective of advancing the state and detail of the physics program and developing detector concepts that meet the emerging requirements in preparation for the realization of the EIC. The effort aims to provide the basis for further development of concepts for experimental equipment best suited for the science needs, including the importance of two complementary detectors and interaction regions. This report consists of three volumes. Volume I is an executive summary of our findings and developed concepts. In Volume II we describe studies of a wide range of physics measurements and the emerging requirements on detector acceptance and performance. Volume III discusses general-purpose detector concepts and the underlying technologies to meet the physics requirements. These considerations will form the basis for a world-class experimental program that aims to increase our understanding of the fundamental structure of all visible matter △ Less

Submitted 26 October, 2021; v1 submitted 8 March, 2021; originally announced March 2021.

Comments: 902 pages, 415 authors, 151 institutions

Report number: BNL-220990-2021-FORE, JLAB-PHY-21-3198, LA-UR-21-20953

Journal ref: Nucl. Phys. A 1026 (2022) 122447

Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)

Authors: Alessia Gentili, Giorgio Volpe

Abstract: Diffusion processes are important in several physical, chemical, biological and human phenomena. Examples include molecular encounters in reactions, cellular signalling, the foraging of animals, the spread of diseases, as well as trends in financial markets and climate records. Deviations from Brownian diffusion, known as anomalous diffusion, can often be observed in these processes, when the grow… ▽ More Diffusion processes are important in several physical, chemical, biological and human phenomena. Examples include molecular encounters in reactions, cellular signalling, the foraging of animals, the spread of diseases, as well as trends in financial markets and climate records. Deviations from Brownian diffusion, known as anomalous diffusion, can often be observed in these processes, when the growth of the mean square displacement in time is not linear. An ever-increasing number of methods has thus appeared to characterize anomalous diffusion trajectories based on classical statistics or machine learning approaches. Yet, characterization of anomalous diffusion remains challenging to date as testified by the launch of the Anomalous Diffusion (AnDi) Challenge in March 2020 to assess and compare new and pre-existing methods on three different aspects of the problem: the inference of the anomalous diffusion exponent, the classification of the diffusion model, and the segmentation of trajectories. Here, we introduce a novel method (CONDOR) which combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy and infer its exponent with a small mean absolute error in single 1D, 2D and 3D trajectories corrupted by localization noise. Finally, we extend our method to the segmentation of trajectories where the diffusion model and/or its anomalous exponent vary in time. △ Less

Submitted 8 April, 2021; v1 submitted 15 February, 2021; originally announced February 2021.

Journal ref: Journal of Physics A: Mathematical and Theoretical (2021)