Controlling Colloidal Dynamics by Critical Casimir Forces preprint in arXiv

Controlling the dynamics of colloidal particles by critical Casimir forces

Controlling the dynamics of colloidal particles by critical Casimir forces
Alessandro Magazzù, Agnese Callegari, Juan Pablo Staforelli, Andrea Gambassi, Siegfried Dietrich & Giovanni Volpe
Soft Matter (2019), accepted
arXiv: 1806.11403

We measure the time evolution of the distance between the two colloids to determine their relative diffusion and drift velocity. Furthermore, we show how critical Casimir forces change the dynamic properties of this two-colloid system by studying the temperature dependence of the distribution of the so-called first-passage time, i.e., of the time necessary for the particles to reach for the first time a certain separation, starting from an initially assigned one. These data are in good agreement with theoretical results obtained from Monte Carlo simulations and Langevin dynamics.

Digital video microscopy enhanced by deep learning on ArXiv

Digital video microscopy enhanced by deep learning

Digital video microscopy enhanced by deep learning
Saga Helgadottir, Aykut Argun & Giovanni Volpe
arXiv: 1812.02653

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard current methods rely on algorithmic approaches: by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.

FORMA – Enhanced Optical Tweezers Calibration published in Nature Commun.

High-Performance Reconstruction of Microscopic Force Fields from Brownian Trajectories

High-Performance Reconstruction of Microscopic Force Fields from Brownian Trajectories
Laura Pérez García, Jaime Donlucas Pérez, Giorgio Volpe, Alejandro V. Arzola & Giovanni Volpe
Nature Communications 9, 5166 (2018)
doi: 10.1038/s41467-018-07437-x
arXiv: 1808.05468

The accurate measurement of microscopic force fields is crucial in many branches of science and technology, from biophotonics and mechanobiology to microscopy and optomechanics. These forces are often probed by analysing their influence on the motion of Brownian particles. Here we introduce a powerful algorithm for microscopic force reconstruction via maximum-likelihood-estimator analysis (FORMA) to retrieve the force field acting on a Brownian particle from the analysis of its displacements. FORMA estimates accurately the conservative and non-conservative components of the force field with important advantages over established techniques, being parameter-free, requiring ten-fold less data and executing orders-of-magnitude faster. We demonstrate FORMA performance using optical tweezers, showing how, outperforming other available techniques, it can identify and characterise stable and unstable equilibrium points in generic force fields. Thanks to its high performance, FORMA can accelerate the development of microscopic and nanoscopic force transducers for physics, biology and engineering.

See also freeware software at 10.6084/m9.figshare.7181888

Featured in:
Optimerad optisk pincett, Forskning.se

Phototactic Robot Tunable by Sensorial Delays published in Phys. Rev. E

Phototactic Robot Tunable by Sensorial Delays

Tuning phototactic robots with sensorial delays (Editors’ suggestion)
Maximilian Leyman, Freddie Ogemark, Jan Wehr & Giovanni Volpe
Physical Review E 98(26), 052606 (2018)
DOI: 10.1103/PhysRevE.98.052606
arXiv: 1807.11765

The presence of a delay between sensing and reacting to a signal can determine the long-term behavior of autonomous agents whose motion is intrinsically noisy.
In a previous work [M. Mijalkov, A. McDaniel, J. Wehr, and G. Volpe, Phys. Rev. X 6, 011008 (2016)], we have shown that sensorial delay can alter the drift and the position probability distribution of an autonomous agent whose speed depends on the illumination intensity it measures. Here, using theory, simulations, and experiments with a phototactic robot, we generalize this effect to an agent for which both speed and rotational diffusion depend on the illumination intensity and are subject to two independent sensorial delays. We show that both the drift and the probability distribution are influenced by the presence of these sensorial delays. In particular, the radial drift may have positive as well as negative sign, and the position probability distribution peaks in different regions depending on the delay.
Furthermore, the presence of multiple sensorial delays permits us to explore the role of the interaction between them.

Clustering of Janus Particles preprint on ArXiv

Clustering of Janus particles in optical potential driven by hydrodynamic fluxes

Clustering of Janus Particles in Optical Potential Driven by Hydrodynamic Fluxes
S. Masoumeh Mousavi, Sabareesh K. P. Velu, Agnese Callegari, Luca Biancofiore & Giovanni Volpe
arXiv: 1811.01989

Self-organisation is driven by the interactions between the individual components of a system mediated by the environment, and is one of the most important strategies used by many biological systems to develop complex and functional structures. Furthermore, biologically-inspired self-organisation offers opportunities to develop the next generation of materials and devices for electronics, photonics and nanotechnology. In this work, we demonstrate experimentally that a system of Janus particles (silica microspheres half-coated with gold) aggregates into clusters in the presence of a Gaussian optical potential and disaggregates when the optical potential is switched off. We show that the underlying mechanism is the existence of a hydrodynamic flow induced by a temperature gradient generated by the light absorption at the metallic patches on the Janus particles. We also perform simulations, which agree well with the experiments and whose results permit us to clarify the underlying mechanism. The possibility of hydrodynamic-flux-induced reversible clustering may have applications in the fields of drug delivery, cargo transport, bioremediation and biopatterning.

Intracavity Optical Trapping preprint on ArXiv

Intracavity Optical Trapping

Intracavity Optical Trapping
Fatemeh Kalantarifard, Parviz Elahi, Ghaith Makey, Onofrio M. Maragò, F. Ömer Ilday & Giovanni Volpe
arXiv: 1808.07831

Standard optical tweezers rely on optical forces that arise when a focused laser beam interacts with a microscopic particle: scattering forces, which push the particle along the beam direction, and gradient forces, which attract it towards the high-intensity focal spot. Importantly, the incoming laser beam is not affected by the particle position because the particle is outside the laser cavity. Here, we demonstrate that intracavity nonlinear feedback forces emerge when the particle is placed inside the optical cavity, resulting in orders-of-magnitude higher confinement along the three axes per unit laser intensity on the sample. We present a toy model that intuitively explains how the microparticle position and the laser power become nonlinearly coupled: The loss of the laser cavity depends on the particle position due to scattering, so the laser intensity grows whenever the particle tries to escape. This scheme allows trapping at very low numerical apertures and reduces the laser intensity to which the particle is exposed by two orders of magnitude compared to a standard 3D optical tweezers. We experimentally realize this concept by optically trapping microscopic polystyrene and silica particles inside the ring cavity of a fiber laser. These results are highly relevant for many applications requiring manipulation of samples that are subject to photodamage, such as in biological systems and nanosciences.

Stability of Brain Graph Measures published in Sci. Rep.

Stability of graph theoretical
measures in structural brain
networks in Alzheimer’s disease

Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Gustav Mårtensson, Joana B. Pereira, Patrizia Mecocci, Bruno Vellas, Magda Tsolaki, Iwona Kłoszewska, Hilkka Soininen, Simon Lovestone, Andrew Simmons, Giovanni Volpe & Eric Westman
Scientific Reports 8, 11592 (2018)
DOI: 10.1038/s41598-018-29927-0

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.

Review on Optical Tweezers published in J. Quant. Spectrosc. Rad. Transf.

Optical tweezers and their applications

Optical tweezers and their applications
Paolo Polimeno, Alessandro Magazzù, Maria Antonia Iata, Francesco Patti, Rosalba  Saija, Cristian Degli Esposti Boschi, Maria Grazia Donato, Pietro G. Gucciardi, Philip H. Jones, Giovanni Volpe & Onofrio M. Maragò
Journal of Quantitative Spectroscopy and Radiative Transfer 218(October 2018), 131—150 (2018)
DOI: 10.1016/j.jqsrt.2018.07.013

Optical tweezers, tools based on strongly focused light, enable optical trapping, manipulation, and characterisation of a wide range of microscopic and nanoscopic materials. In the limiting cases of spherical particles either much smaller or much larger than the trapping wavelength, the force in optical tweezers separates into a conservative gradient force, which is proportional to the light intensity gradient and responsible for trapping, and a non-conservative scattering force, which is proportional to the light intensity and is generally detrimental for trapping, but fundamental for optical manipulation and laser cooling. For non-spherical particles or at intermediate (meso)scales, the situation is more complex and this traditional identification of gradient and scattering force is more elusive. Moreover, shape and composition can have dramatic consequences for optically trapped particle dynamics. Here, after an introduction to the theory and practice of optical forces with a focus on the role of shape and composition, we give an overview of some recent applications to biology, nanotechnology, spectroscopy, stochastic thermodynamics, critical Casimir forces, and active matter.

Active Atoms and Interstitials published in Phys. Rev. Lett.

Active Atoms and Interstitials in Two-dimensional Colloidal Crystals

Active Atoms and Interstitials in Two-dimensional Colloidal Crystals
Kilian Dietrich, Giovanni Volpe, Muhammad Nasruddin Sulaiman, Damina Renggli, Ivo Buttinoni & Lucio Isa
Physical Review Letters 120(26), 268004 (2018)
DOI: 10.1103/PhysRevLett.120.268004
arXiv: 1710.08680

We study experimentally and numerically the motion of a self-phoretic active particle in two-dimensional loosely packed colloidal crystals at fluid interfaces. Two scenarios emerge depending on the interactions between the active particle and the lattice: the active particle either navigates throughout the crystal as an interstitial or is part of the lattice and behaves as an active atom. Active interstitials undergo a run-and-tumble-like motion, with the passive colloids of the crystal acting as tumbling sites. Instead, active atoms exhibit an intermittent motion, stemming from the interplay between the periodic potential landscape of the passive crystal and the particle’s self-propulsion. Our results constitute the first step towards the realization of non-close-packed crystalline phases with internal activity.

Influence of Sensorial Delay on Clustering and Swarming preprint in arXiv

Influence of Sensorial Delay on Clustering and Swarming

Influence of Sensorial Delay on Clustering and Swarming
Rafal Piwowarczyk, Martin Selin, Thomas Ihle & Giovanni Volpe
arXiv:  1803.06026

We show that sensorial delay alters the collective motion of self-propelling agents with aligning interactions: In a two-dimensional Vicsek model, short delays enhance the emergence of clusters and swarms, while long or negative delays prevent their formation. In order to quantify this phenomenon, we introduce a global clustering parameter based on the Voronoi tessellation, which permits us to efficiently measure the formation of clusters. Thanks to its simplicity, sensorial delay might already play a role in the organization of living organisms and can provide a powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous robots.