Tag: Giovanni Volpe

Anisotropic dynamics of a self-assembled colloidal chain in an active bath published on Soft Matter

Bright-field microscopy image of a magnetic chain trapped at the liquid-air interface in a bacterial bath

Anisotropic dynamics of a self-assembled colloidal chain in an active bath
Mehdi Shafiei Aporvari, Mustafa Utkur, Emine Ulku Saritas, Giovanni Volpe & Joakim Stenhammar
Soft Matter, 2020, 16, 5609-5614
doi: https://doi.org/10.1039/D0SM00318B
arXiv: 2002.09961

Anisotropic macromolecules exposed to non-equilibrium (active) noise are very common in biological systems, and an accurate understanding of their anisotropic dynamics is therefore crucial. Here, we experimentally investigate the dynamics of isolated chains assembled from magnetic microparticles at a liquid–air interface and moving in an active bath consisting of motile E. coli bacteria. We investigate both the internal chain dynamics and the anisotropic center-of-mass dynamics through particle tracking. We find that both the internal and center-of-mass dynamics are greatly enhanced compared to the passive case, i.e., a system without bacteria, and that the center-of-mass diffusion coefficient D features a non-monotonic dependence as a function of the chain length. Furthermore, our results show that the relationship between the components of D parallel and perpendicular with respect to the direction of the applied magnetic field is preserved in the active bath compared to the passive case, with a higher diffusion in the parallel direction, in contrast to previous findings in the literature. We argue that this qualitative difference is due to subtle differences in the experimental geometry and conditions and the relative roles played by long-range hydrodynamic interactions and short-range collisions.

Characterisation of Physical Processes from Anomalous Diffusion Data, special issue on Journal of Physics A

Logo of the AnDi challenge.

Characterisation of Physical Processes from Anomalous Diffusion Data
Guest Editors
Miguel A Garcia-March, Maciej Lewenstein, Carlo Manzo, Ralf Metzler, Gorka Muñoz-Gil, Giovanni Volpe
Journal of Physics A: Mathematical and Theoretical
URL: Special Issue on Characterisation of Physical Processes from Anomalous Diffusion Data

In many systems, stochastic transport deviates from the standard laws of Brownian motion. Determining the exponent α characterising anomalous diffusion and identifying the physical origin of this behaviour are crucial steps to understanding the nature of the systems under observation. However, the determination of these properties from the analysis of the measured trajectories is often difficult, especially when these trajectories are short, irregularly sampled, or switching between different behaviours.

Over the last years, several methods have been proposed to quantify anomalous diffusion and the underlying physical process, going beyond the classical calculation of the mean squared displacement. More recently, the advent of machine learning has produced a boost in the methods to quantify anomalous diffusion.

The AnDi challenge aims at bringing together a vibrating and multidisciplinary community of scientists working on this problem. The use of the same reference datasets will allow an unbiased assessment of the performance of methods for characterising anomalous diffusion from single trajectories. This Special Issue will report on these approaches and their performance.

The deadline for submissions will be 30th June 2021 and you can submit manuscripts through ScholarOne Manuscripts. All papers will be refereed according to the usual high standards of the journal.

Giovanni Volpe awarded with the ERC Proof of Concept Grant

Giovanni Volpe has been awarded with the ERC Proof of Concept Grant for the research project LUCERO: Smart Optofluidic micromanipulation of Biological Samples.

The grant, consisting of 150k EUR, is meant to commercialize the research project LUCERO, providing an innovative method that combines artificial intelligence and optical tweezers to analyze cells easily and inexpensively.

The current technologies for cell analysis have many limitations: they require access to a large number of cells and considerable expertise. The available methods are also labor-intensive and in some cases the cells are destroyed.

The new method developed in LUCERO simplifies the work and lowers the costs of biomedical research by allowing ordinary standard microscopes, which are already in use in biomedical laboratories, to be used to perform the cell analysis.

The method of LUCERO can be used in several areas, from artificial insemination to forensic medicine. It has potentially a large commercial market.

Giovanni Volpe expects that LUCERO will provide around 20 jobs for university-trained experts and researchers within the next five years.

The project LUCERO has already received initial funding and support from two different organizations (Venture Cup and SPIE). Two doctoral students, Falko Schmidt and Martin B. Mojica, are part of LUCERO’s contributors team.

Links:
Press release of the Swedish Research Council: in English, in Swedish.
News on Gothenburg University website: in Swedish.

Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials published on ACS Photonics

Counter-propagating laser beam intensity, represented and projected on the yz plane.
Gain-Assisted Optomechanical Position Locking of Metal/Dielectric Nanoshells in Optical Potentials
Paolo Polimeno, Francesco Patti, Melissa Infusino, Jonathan Sánchez, Maria A. Iatì, Rosalba Saija, Giovanni Volpe, Onofrio M. Maragò & Alessandro Veltri
ACS Photonics 7(5), 1262–1270 (2020)
doi: https://doi.org/10.1021/acsphotonics.0c00213

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 counter-propagating Gaussian beam configuration. We study the optical forces and trapping behavior 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 an externally controlled nanophotonic system.

Ordering of Binary Colloidal Crystals by Random Potentials published on Soft Matter

Ordering of binary colloidal crystals by random potentials

Ordering of Binary Colloidal Crystals by Random Potentials
André S. Nunes, Sabareesh K. P. Velu, Iryna Kasianiuk, Denys Kasyanyuk, Agnese Callegari, Giorgio Volpe, Margarida M. Telo da Gama, Giovanni Volpe & Nuno A. M. Araújo
Soft Matter 16, 4267-4273 (2020)
doi: https://doi.org/10.1039/D0SM00208A
arXiv: 1903.01579

Structural defects are ubiquitous in condensed matter, and not always a nuisance. For example, they underlie phenomena such as Anderson localization and hyperuniformity, and they are now being exploited to engineer novel materials. Here, we show experimentally that the density of structural defects in a 2D binary colloidal crystal can be engineered with a random potential. We generate the random potential using an optical speckle pattern, whose induced forces act strongly on one species of particles (strong particles) and weakly on the other (weak particles). Thus, the strong particles are more attracted to the randomly distributed local minima of the optical potential, leaving a trail of defects in the crystalline structure of the colloidal crystal. While, as expected, the crystalline ordering initially decreases with an increasing fraction of strong particles, the crystalline order is surprisingly recovered for sufficiently large fractions. We confirm our experimental results with particle-based simulations, which permit us to elucidate how this non-monotonic behavior results from the competition between the particle-potential and particle-particle interactions.

AnDi: The Anomalous Diffusion Challenge on ArXiv

Logo of the AnDi challenge

AnDi: The Anomalous Diffusion Challenge
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Ralf Metzler, Maciej Lewenstein & Carlo Manzo
arXiv: 2003.12036

The deviation from pure Brownian motion generally referred to as anomalous diffusion has received large attention in the scientific literature to describe many physical scenarios. Several methods, based on classical statistics and machine learning approaches, have been developed to characterize anomalous diffusion from experimental data, which are usually acquired as particle trajectories. With the aim to assess and compare the available methods to characterize anomalous diffusion, we have organized the Anomalous Diffusion (AnDi) Challenge (http://www.andi-challenge.org/). Specifically, the AnDi Challenge will address three different aspects of anomalous diffusion characterization, namely: (i) Inference of the anomalous diffusion exponent. (ii) Identification of the underlying diffusion model. (iii) Segmentation of trajectories. Each problem includes sub-tasks for different number of dimensions (1D, 2D and 3D). In order to compare the various methods, we have developed a dedicated open-source framework for the simulation of the anomalous diffusion trajectories that are used for the training and test datasets. The challenge was launched on March 1, 2020, and consists of three phases. Currently, the participation to the first phase is open. Submissions will be automatically evaluated and the performance of the top-scoring methods will be thoroughly analyzed and compared in an upcoming article.

Invited talk by G. Volpe at Nanolight, Benasque, Spain, 8-14 March 2020

Giovanni Volpe will give an invited presentation at Nanolight 2020.

The conference, organized by Luis Martín Moreno (ICMA, CSIC – U. Zaragoza) and Niek van Hulst (ICFO, Barcelona), aims at the exploration of the frontiers in the field of subwavelength optics. It is meant to facilitate the interaction between worldwide researchers working in the field, with a special emphasis on interaction between young and more experienced researchers.
The conference is held in Benasque, Spain, from 8 to 14 March 2020.

The contributions of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Deep Learning for Microscopy
Date: 12 March 2020
Time: 15:35 CET

Link: Nanolight 2020 program

The AnDi challenge: an “anomalous” competition

Logo of the AnDi challenge.

Researchers from ICFO, UVic, Gothenborg University, Politecnica de Valencia and Potsdam University organize the AnDi challenge, a physics challenge to address Brownian motion and Anomalous diffusion.

Brownian motion was first observed in 1827 by Robert Brown: pollen grains suspended in water show a characteristic erratic motion. Almost 80 years after, Albert Einstein provided a theoretical foundation for the Brownian motion. Though the Brownian motion is observed in many different systems, significant deviations from it have also been observed, starting from biological systems to economics.

The deviation from Brownian motion is indicated with the term Anomalous diffusion. It is connected to non-equilibrium phenomena, complex environments, flows of energy and information, and transport in living systems. To understand the nature of such systems one must correctly identify the physical origin of the anomalous diffusion, and correctly characterize it, through the calculation of its properties. A simple data analysis of trajectories, though, often provides limited information, in particular when the trajectories are either short, or noisy, or irregularly sampled, or featuring mixed behaviors. Several methods going beyond the calculation of classical estimators have been proposed, in the last years, to quantify anomalous diffusion.

The AnDi challenge has been thought as a competition to test these methods as well as other alternative approaches, by bringing together the scientific community currently working on the quantification of the anomalous diffusion.

The use of the same reference datasets will allow an unbiased assessment of the performance of published and unpublished methods for characterizing anomalous diffusion from single trajectories. Participants can submit the results of their analysis on the internet until November 1st, 2020. These results will be then automatically scored and ranked among all competitors.

In addition to the main objective of the AnDi Challenge, the top-ranked participants will be invited to present their results in a workshop held at ICFO, in Barcelona, on February 17-20, 2021.

Organizers:

Website: www.andi-challenge.org

Codalab: https://competitions.codalab.org/competitions/23601

e-mail: andi.challenge@gmail.com

twitter: @AndiChallenge

Anisotropic dynamics of a self-assembled colloidal chain in an active bath on ArXiv

Bright-field microscopy image of a magnetic chain trapped at the liquid-air interface in a bacterial bath

Anisotropic dynamics of a self-assembled colloidal chain in an active bath
Mehdi Shafiei Aporvari, Mustafa Utkur, Emine Ulku Saritas, Giovanni Volpe & Joakim Stenhammar
arXiv: 2002.09961

Anisotropic macromolecules exposed to non-equilibrium (active) noise are very common in biological systems, and an accurate understanding of their anisotropic dynamics is therefore crucial. Here, we experimentally investigate the dynamics of isolated chains assembled from magnetic microparticles at a liquid-air interface and moving in an active bath consisting of motile E. coli bacteria. We investigate both the internal chain dynamics and the anisotropic center-of-mass dynamics through particle tracking. We find that both the internal and center-of-mass dynamics are greatly enhanced compared to the passive case, and that the center-of-mass diffusion coefficient D features a non-monotonic dependence as a function of the chain length. Furthermore, our results show that the relationship between the parallel and perpendicular components of D is preserved in the active bath compared to the passive case, with a higher diffusion parallel to the chain direction, in contrast to previous findings in the literature. We argue that this qualitative difference is due to subtle differences in the experimental geometry and conditions and the relative roles played by long-range hydrodynamic interactions and short-range collisions.

Shaping the future of machine learning for active matter

Machine learning has proven to be very useful for the study of active matter, a collective term referring to things like cells and microorganisms. The field is quite new and growing fast. In an attempt to inspire more researchers to try the methods a group of scientists have published a paper in prestigious publication Nature Machine Intelligence reviewing what has been accomplished so far – and what lies ahead. Continue reading (English)

Press release:
Shaping the future of machine learning for active matter (In English)
Formar framtiden för AI-forskning på aktiv materia (In Swedish)

Article:
Machine learning for active matter