Angelo is a master student in Complex Adaptive Systems at Chalmers University of Technology.
During his time at the Soft Matter Lab, he will investigate the behaviour of active matter via experiments with toy robots (HEXBUG nano®).
News
CORDIS News article on ComplexSwimmers
CORDIS, the Community Research and Development Information Service of the European Commission, recently covered Giovanni Volpe’s ComplexSwimmers ERC-StG grant in a news:
Throwing down the scientific gauntlet to assess methods for anomalous diffusion.
The article highlights the joint results obtained by three EU-backed research projects (NOQIA, OPTOlogic and ComplexSwimmers) dealing with anomalous diffusion.
Visit by Claus Roll, OPTICA director in Europe, 19 November 2021
Claus Roll is visiting the Soft Matter Lab on the 19 November 2021.
Claus is the director in Europe of OPTICA (former OSA) and he will be in Gothenburg for an hybrid event organised together with the local OPTICA student chapter and the FFF (Föreningen för Forskarstuderande i Fysik) group.
The visit starts with a tour of different labs including the Soft matter and Biophysics lab. The tour is followed by an hybrid career seminar by Claus Roll, both in person and online starting at 10:30. The presentation is followed by a social lunch and networking session.
Presentation by G. V. P. Kumar, 24 November 2021
Thermoplasmonic Tweezers: Probing single-molecules and more
G. V. Pavan Kumar
IISER, Pune, India.
24 November 2021
Online
In this presentation, we will discuss two specific issues: How to perform single-molecule surface enhanced Raman scattering (SERS) in an optothermal trap? and how to design optothermal fields to trap and interrogate molecules and colloids in a fluid?
In recent years, performing SERS in optical traps has emerged as an important development in nano- and bio-photonics. To this end, tweezer techniques based on surface-plasmons facilitate deeper optical potentials at sub-wavelength scales, and simultaneously provide enhanced electric and optothermal fields. In this
presentation, we will discuss various strategies developed in my laboratory to perform single-molecule SERS in optical and plasmonic tweezer platforms. Specifically, we will highlight some thermoplasmonic effects and directionality aspects of the tweezer platforms in metallic thin film and some plasmonic nano-architectures.
Short bio:
G.V. Pavan Kumar is an associate professor of physics at the Indian Institute of Science Education and Research (IISER), Pune, India.
He obtained his PhD from JNCASR, Bangalore. Subsequently he was a postdoctoral fellow at ICFO-Barcelona and Purdue University, before joining IISER in 2010.
His current research interests are optical, optothermal and nanophotonic forces and their utility in probing single molecules and soft-matter systems at micro and nanoscale.
To this end, his lab has been interfacing optical tweezer platforms with a variety of optical spectroscopy and microscopy tools.
He blogs on topics related to science: https://backscattering.wordpress.com/
Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis published in Environmental Science: Nano
Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Bronte Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
Environmental Science: Nano 9, 145 – 161 (2022)
ChemRxiv: https://doi.org/10.33774/chemrxiv-2021-h59n1
doi: https://doi.org/10.1039/D1EN00553G
Tire and Road Wear Particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ~ 5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of < 5 μm MPs. Here we show that Raman Tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TWRPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman Tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.
Featured in:
University of Gothenburg > News and Events: New technology enables the detection of microplastics from road wear
Phys.org > News > Nanotechnology:New technology enables the detection of microplastics from road wear
Nonsologreen > Green: Le Raman-tweezers per la guerra alle nanoplastiche che inquinano fiumi e mari
Invited Presentation by G. Volpe at FiO LS, 4 November 2021
Giovanni Volpe
Invited Presentation at Frontiers in Optics + Laser Science
Online
4 November 2021
4:00 PM
We present DeepTrack 2.0, a software to design, train, and validate deep-learning solutions for digital microscopy. We demonstrate it for applications from particle localization, tracking, and characterization, to cell counting and classification, to virtual staining.
Link: FTh6A.3
Press release on Objective comparison of methods to decode anomalous diffusion
The article Objective comparison of methods to decode anomalous diffusion has been featured in the News of the University of Gothenburg.
The study, published in Nature Communications and co-written by researchers at the Soft Matter Lab of the Department of Physics at the University of Gothenburg, originates from the AnDi Challenge, a competition co-organised by Giovanni Volpe with researchers from University of Vic – Central University of Catalunya, Institute of Photonic Sciences in Barcelona, University of Potsdam, and Valencia Polytechnic University.
The challenge was held during March–November 2020 and consisted of three main tasks concerning anomalous exponent inference, model classification, and trajectory segmentation. The goal was to provide an objective assessment of the performance of methods to characterise anomalous diffusion from single trajectories.
Here the links to the press releases:
English: A scientific competition led to improved methods for analysing the diffusion of particles.
Swedish: En vetenskaplig tävling ledde till förbättrade metoder för att analysera diffusion av partiklar.
Seminar by D. Midtvedt at Freie Universität Berlin, 29 October 2021
Daniel Midtvedt
(online at) Freie Universität Berlin, Germany
29 October 2021
Video microscopy has a long history of providing insight and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time-consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis.
However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we recently introduced a software, DeepTrack 2.0, to design, train, and validate deep-learning solutions for digital microscopy.
In this talk, I will show how this software can be used in a broad range of applications, from particle localization, tracking, and characterization, to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and thanks to its open-source, object-oriented programing, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Objective comparison of methods to decode anomalous diffusion published in Nature Communications
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, Taegeun Song, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Arthur Widera, Maciej Lewenstein, Ralf Metzler, and Carlo Manzo
Nat. Commun. 12, Article number: 6253 (2021)
doi: 10.1038/s41467-021-26320-w
arXiv: 2105.06766
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.
Invited Talk by G. Volpe at Microscopies and Spectroscopies: Accessing the Nanoscale, 28 October 2021
Giovanni Volpe
Invited Talk at the XXXVI Trobades Cientifíques de la Mediterránia – Josep Miquel Vidal
Microscopies and Spectroscopies: Accessing the Nanoscale
Menorca, Spain
28 October 2021
11:40 AM
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep- learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user- friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.