Soft Matter Lab

Invited presentation by A. Ciarlo at SPIE-MNPM, San Diego, 5 August 2025

Graphical representation of colloidal interaction measurements using the automated miniTweezer. *(Image by A. Ciarlo.)*

miniTweezer: an autonomous high-throughput optical tweezers platform for force spectroscopy
Antonio Ciarlo, Martin Selin, Giuseppe Pesce, Carlos Bustamante, and Giovanni Volpe
Date: 5 August 2025
Time: 9:45 AM – 10:15 AM
Place: Conv. Ctr. Room 4

Optical tweezers are essential for single-molecule studies, providing direct access to the forces underlying biological processes such as protein folding, DNA transcription, and replication. However, manual experiments are labor-intensive, costly, and slow, especially when large data sets are required. Here we present the miniTweezer, a fully autonomous force spectroscopy platform that integrates optical tweezers with real-time image analysis and adaptive control. Once configured, it operates independently to perform high-throughput trapping, molecular attachment, and force measurements. Its robust design allows for extended unattended operation, significantly increasing the efficiency of data acquisition. We demonstrate its capabilities through DNA pulling experiments and highlight its broader applicability to microparticle interactions, colloidal assembly, and soft matter mechanics. By automating force spectroscopy, the miniTweezer enables large-scale, high-precision studies in biophysics, materials science, and nanotechnology.

Invited Presentation by B. Yeroshenko at SPIE-ETAI, San Diego, 5 August 2025

Kymographs of DNA inside Channel II. *(Image from 10.1038/s41592-022-01491-6. by Barbora Špačková)*

Pushing the limits of label-free single-molecule characterization by nanofluidic scattering microscopy
Bohdan Yeroshenko, Henrik Klein Moberg, Leyla Beckerman, Joachim Fritzsche, David Albinsson, Barbora Špačková, Daniel Midtvedt, Giovanni Volpe, Christoph Langhammer
SPIE-ETAI, San Diego, CA, USA, 3 – 7 August 2025
Date: 5 August 2025
Time: 8:45 AM – 9:15 AM PDT
Place: Conv. Ctr. Room 4

Nanofluidic Scattering Microscopy (NSM) is a label-free characterization method that leverages the interference of light scattered by nanochannels and single molecules within them. This technique enables accurate determination of molecular weight and hydrodynamic radius based solely on scattering, without requiring prior molecular knowledge. However, standard analysis methods limit NSM’s sensitivity to 66 kDa for proteins. In this presentation, I will demonstrate how we push this detection limit an order of magnitude further by integrating ultrasmall geometry with an advanced machine learning analysis approach, all while maintaining the same input laser power intensity.

Presentation by H. P. Thanabalan at SPIE-ETAI, San Diego, 5 August 2025

Inchworm-inspired soft robot. *(Image by H. P. Thanabalan.)*

Bio-inspired soft robot for multi-directionality
Hari Prakash Thanabalan, Lars Bengtsson, Ugo Lafont, Giovanni Volpe
SPIE Optics+Photonics, San Diego, CA, USA, 3-7 August 2025
Date: 5th August 2025
Time: 8:30 AM – 8:45 AM
Place: Conv. Ctr. Room 4

Soft robotics are the forefront of robotics evolution that leverages compliant materials such as silicone elastomer to mimic biological organisms. With infinite degrees of freedom, soft robots surpass rigid robots in adaptability making them ideal for exploration and manipulation tasks. Here we focus on inchworm inspired soft robot achieving multidirectional locomotion through groove-guided movement. By manipulating the groove angles on a substrate, we demonstrate multidirectional locomotion by utilising only a single actuator.

Poster by A. Callegari at SPIE-OTOM, San Diego, 4 August 2025

One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)

Experimenting with macroscopic active matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 3 – 7 August 2025
Date: 4 August 2025
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall A

Presenter: Giovanni Volpe
Contribution submitted by Agnese Callegari

Active matter is based on concepts of nonequilibrium thermodynamics applied to the most diverse disciplines. A key concept is the active Brownian particle, which, unlike passive ones, extracts energy from its environment to generate complex motion and emergent behaviors. Despite its significance, active matter remains absent from standard curricula. This work presents macroscopic experiments using commercially available Hexbugs to demonstrate active matter phenomena. We show how Hexbugs can be modified to perform both regular and chiral active Brownian motion and interact with passive objects, inducing movement and rotation. By introducing obstacles, we sort Hexbugs based on motility and chirality. Finally, we demonstrate a Casimir-like attraction effect between planar objects in the presence of active particles.

Reference
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
Playing with Active Matter, Americal Journal of Physics 92, 847–858 (2024)

Poster by A. Callegari at SPIE-ETAI, San Diego, 4 August 2025

Focused rays scattered by an ellipsoidal particles (left). Optical torque along y calculated in the x-y plane using ray scattering with a grid of 1600 rays (up, right) and using a trained neural network (down, right). (Image by the Authors of the manuscript.)

Dense neural networks for geometrical optics
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria Antonia Iatì, Giovanni Volpe, and Onofrio M. Maragò
SPIE-ETAI, San Diego, CA, USA, 3 – 7 August 2025
Date: 4 August 2025
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall A

Presenter: Giovanni Volpe
Contribution submitted by Agnese Callegari

Light can trap and manipulate microscopic objects through optical forces and torques, as seen in optical tweezers. Predicting these forces is crucial for experiments and setup design. This study focuses on the geometrical optics regime, which applies to particles much larger than the light’s wavelength. In this model, a beam is represented by discrete rays that undergo multiple reflections and refractions, transferring momentum and angular momentum. However, the choice of ray discretization affects the balance between computational speed and accuracy. We demonstrate that neural networks overcome this limitation, enabling faster and even more precise simulations. Using an optically trapped spherical particle with an analytical solution as a benchmark, we validate our method and apply it to study complex systems that would otherwise be computationally hard.

Reference
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò, Faster and more accurate geometrical-optics optical force calculation using neural networks, ACS Photonics 10, 234–241 (2023)

Presentation by A. Ciarlo at SPIE-OTOM, San Diego, 4 August 2025

Experimental trajectory (blue) of a particle trapped in air when the laser rotates at 1 Hz. The orange line represents the experimental laser trajectory. *(Image by A. Ciarlo.)*

Probing fluid dynamics inertial effects of particles using optical tweezers
Antonio Ciarlo, Giuseppe Pesce, Bernhard Mehlig, Antonio Sasso, and Giovanni Volpe
Date: 4 August 2025
Time: 11:45 AM – 12:00 PM
Place: Conv. Ctr. Room 3

Many natural phenomena involve dense particles suspended in a moving fluid, such as water droplets in clouds or dust grains in circumstellar disks. Studying these systems at the single particle level is challenging and requires precise control of flow and particle motion. Optical tweezers provide a powerful method for studying inertial effects in such environments. Here, we trap micrometer-sized particles in air and induce controlled dynamics by moving the trapping laser. We show that inertia becomes significant when the trap motion frequency is less than the harmonic trapping frequency, while at much higher motion frequencies, inertia has no effect. These results demonstrate the potential of trapping particles in air for studying inertial phenomena in fluids.

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

Rationale for the challenge organization. The interactions of biomolecules in complex environments, such as the cell membrane, regulate physiological processes in living systems. These interactions produce changes in molecular motion that can be used as a proxy to measure interaction parameters. Time-lapse single-molecule imaging allows us to visualize these processes with high spatiotemporal resolution and, in combination with single-particle tracking methods, provide trajectories of individual molecules. *(Image by the Authors of the manuscript.)*

Quantitative evaluation of methods to analyze motion changes in single-particle experiments
Gorka Muñoz-Gil, Harshith Bachimanchi, Jesús Pineda, Benjamin Midtvedt, Gabriel Fernández-Fernández, Borja Requena, Yusef Ahsini, Solomon Asghar, Jaeyong Bae, Francisco J. Barrantes, Steen W. B. Bender, Clément Cabriel, J. Alberto Conejero, Marc Escoto, Xiaochen Feng, Rasched Haidari, Nikos S. Hatzakis, Zihan Huang, Ignacio Izeddin, Hawoong Jeong, Yuan Jiang, Jacob Kæstel-Hansen, Judith Miné-Hattab, Ran Ni, Junwoo Park, Xiang Qu, Lucas A. Saavedra, Hao Sha, Nataliya Sokolovska, Yongbing Zhang, Giorgio Volpe, Maciej Lewenstein, Ralf Metzler, Diego Krapf, Giovanni Volpe, Carlo Manzo
Nature Communications 16, 6749 (2025)
arXiv: 2311.18100
doi: https://doi.org/10.1038/s41467-025-61949-x

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

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

In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. *(Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)*

From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
ArXiv: 2507.15772

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

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

Automated segnmentation of bacterial structures within a droplet. The image shows a bright-field microscopy view where a large biofilm region (green, outlined in blue) has been segmented from surrounding features. Small aggregates (yellow contours) are also highlighted. This segmentation enables structural differentiation of biofilm components for downstream quantitative analysis. *(Image by D. Pérez Guerrero.)*

Latent Space-Driven Quantification of Biofilm Formation using Time Resolved Droplet Microfluidics
Daniela Pérez Guerrero, Jesús Manuel Antúnez Domínguez, Aurélie Vigne, Daniel Midtvedt, Wylie Ahmed, Lisa D. Muiznieks, Giovanni Volpe, Caroline Beck Adiels
arXiv: 2507.07632

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.

Xinwen Zhang joins the Soft Matter Lab

Xinwen Zhang started her PhD at the Physics Department of Gothenburg University on 7 July 2025.

Xinwen holds a master’s degree in Physics (biophysics) from the University of Science and Technology of China (USTC), Hefei, China.

During her PhD, she will focus on label-free optical microscopy combined with deep learning, aiming to characterize nanoparticles and uncover their physical mechanisms.