Giovanni Volpe is part of the committee of the conference Optical Manipulation and its Applications (OMA), which is part of the OSA Biophotonics Congress: Optics in the Life Sciences.
Optical Manipulation encompasses all areas of manipulation and measurement using light, from optical manipulation of microparticles to photoactivated materials and optogenetics, emphasizing new and developing application areas in biophysics and biomedicine.
The categories of topics in the conference are Optical Manipulation in Biophysics and Biomedicine, Optical Manipulation Fundamentals, Optical Manipulation Applications, and Alternative Manipulation Techniques.
The conference will be held online 12-16 April 2021.
Deep learning for microscopy and optical trapping
Giovanni Volpe
21 January 2021, 16:30 CEST
Online
Invited seminar for Indian Institute of Science Education and Research (IISER), Pune, India
After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.
Phase and amplitude signals from representative particles for testing the performance of the Deep-learning approach
Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography
Benjamin Midtvedt, Erik Olsén, Fredrik Eklund, Fredrik Höök, Caroline Beck Adiels, Giovanni Volpe, Daniel Midtvedt
ACS Nano 15(2), 2240–2250 (2021)
doi: 10.1021/acsnano.0c06902
arXiv: 2006.11154
The characterisation of the physical properties of nanoparticles in their native environment plays a central role in a wide range of fields, from nanoparticle-enhanced drug delivery to environmental nanopollution assessment. Standard optical approaches require long trajectories of nanoparticles dispersed in a medium with known viscosity to characterise their diffusion constant and, thus, their size. However, often only short trajectories are available, while the medium viscosity is unknown, e.g., in most biomedical applications. In this work, we demonstrate a label-free method to quantify size and refractive index of individual subwavelength particles using two orders of magnitude shorter trajectories than required by standard methods, and without assumptions about the physicochemical properties of the medium. We achieve this by developing a weighted average convolutional neural network to analyse the holographic images of the particles. As a proof of principle, we distinguish and quantify size and refractive index of silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. As an example of an application beyond the state of the art, we demonstrate how this technique can monitor the aggregation of polystyrene nanoparticles, revealing the time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates. This technique opens new possibilities for nanoparticle characterisation with a broad range of applications from biomedicine to environmental monitoring.
ERC logo.Giovanni Volpe has been awarded a new European Research Council (ERC) Consolidator Grant on Wednesday, December 9th 2020.
The title of his project is “Microscopic Active Particles with Embodied Intelligence”.
Active particles and active matter research tries to understand and replicate the characteristics of living microorganisms in artificial systems. Over billions of years of evolution, living organisms have developed complex strategies to survive and thrive. The artificial active particles are still incapable of autonomous information processing.
Giovanni Volpe’s project aims to address three main challenges in the current research on active matter:
Make active particles capable of autonomous information processing.
Optimize the behavioral strategies of individual active particles.
Optimize the interactions between active particles.
On 25 November 2020, Giovanni Volpe gave an online lecture on Graph Theory Concepts, in the scope of Karolinska Institute graduate course 3064: Imaging in Neuroscience: With a focus on structural MRI methods
Giovanni Volpe, photo of Johan Wingborg.Giovanni Volpe’s new project “Active matter goes smart” has been featured on the website of the Knut and Alice Wallenberg (KAW) Foundation.
The feature article explains the project and its main aim of creating smart particles that react to their environment to a general audience.
The article is available both in English and in Swedish.
Representation of a particle in a force fieldEnhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
Applied Physics Reviews 7, 041404 (2020)
doi: 10.1063/5.0019105
arXiv: 2006.08963
The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.
Funding:
H2020 European Research Council (ERC) Starting Grant ComplexSwimmers (677511).
Optical Tweezers Go Smart
Giovanni Volpe
28 October 2020, 15:00
Invited Talk (Online) at OSA-EPN Student Chapter, Escuela Politécnica Nacional, Ecuador
