Falko Schmidt will defend his PhD Thesis in Physics on Friday, 15 January 2021.
The disputation will take place at 9 a.m., in PJ salen, Fysikgården.
Falko Schmidt’s opponent, Peer Fischer, will give an introductory presentation with title “Microswimmers and motile active matter”.
Title: Active Matter in a Critical State: From passive building blocks to active molecules, engines and droplets
The motion of microscopic objects is strongly affected by their surrounding environment. In quiescent liquids, motion is reduced to random fluctuations known as Brownian motion. Nevertheless, microorganisms have been able to develop mechanisms to generate active motion. This has inspired researchers to understand and artificially replicate active motion. Now, the field of active matter has developed into a multi-disciplinary field, with researchers developing artificial microswimmers, producing miniaturized versions of heat engines and showing that individual colloids self-assemble into larger microstructures. This thesis taps into the development of artificial microscopic and nanoscopic systems and demonstrates that passive building blocks such as colloids are transformed into active molecules, engines and active droplets that display a rich set of motions. This is achieved by combining optical manipulation with a phase-separating environment consisting of a critical binary mixture. I first show how simple absorbing particles are transformed into fast rotating microengines using optical tweezers, and how this principle can be scaled down to nanoscopic particles. Transitioning then from single particles to self-assembled modular swimmers, such colloidal molecules exhibit diverse behaviour such as propulsion, orbital rotation and spinning, and whose formation process I can control with periodic illumination. To characterize the molecules dynamics better, I introduce a machine-learning algorithm to determine the anomalous exponent of trajectories and to identify changes in a trajectory’s behaviour. Towards understanding the behaviour of larger microstructures, I then investigate the interaction of colloidal molecules with their phase-separating environment and observe a two-fold coupling between the induced liquid droplets and their immersed colloids. With the help of simulations I gain a better physical picture and can further analyse the molecules’ and droplets’ emergence and growth dynamics. At last, I show that fluctuation-induced forces can solve current limitations in microfabrication due to stiction, enabling a further development of the field towards smaller and more stable nanostructures required for nowadays adaptive functional materials. The insights gained from this research mark the path towards a new generation of design principles, e.g., for the construction of flexible micromotors, tunable micromembranes and drug delivery in health care applications.
Extracting quantitative biological information from brightfield cell images using deep learning
Saga Helgadottir, Benjamin Midtvedt, Jesús Pineda, Alan Sabirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe
Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.
Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces
Daniel Andrén, Denis G. Baranov, Steven Jones, Giovanni Volpe, Ruggero Verre, Mikael Käll
Nanostructured dielectric metasurfaces offer unprecedented opportunities to manipulate light by imprinting an arbitrary phase-gradient on an impinging wavefront. This has resulted in the realization of a range of flat analogs to classical optical components like lenses, waveplates and axicons. However, the change in linear and angular optical momentum associated with phase manipulation also results in previously unexploited forces acting on the metasurface itself. Here, we show that these optomechanical effects can be utilized to construct optical metavehicles – microscopic particles that can travel long distances under low-power plane-wave illumination while being steered through the polarization of the incident light. We demonstrate movement in complex patterns, self-correcting motion, and an application as transport vehicles for microscopic cargo, including unicellular organisms. The abundance of possible optical metasurfaces attests to the prospect of developing a wide variety of metavehicles with specialized functional behavior.
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 November 25, the members of the Soft Matter Lab and of the Biological Physics Lab joined for the Digital Christmas Lunch 2020.
This activity has been held in substitution of the traditional Physics Department Christmas Lunch, which this year cannot take place in the usual format because of the ongoing coronavirus epidemic.
After the usual group meeting, which is held online on Zoom since the beginning of March 2020, the two groups shared a common lunch, in respect of the current recommendations of the Folkhälsomyndigheten, which do not allow public gatherings with more than 8 people.
Several group members joined from their homes. The group members involved in experimental work, who, in any case, had to be present in the respective labs, joined the group lunch from various rooms in Soliden, to comply with the current rules of social distancing.
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
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
The OSA Student chapter together with FFF will host a career webinar again, this time with Christian Reimer, Co-founder and Head of Product at HyperLight and OSA Ambassador.
Christian Reimer will give a talk with title: Electro-optics with thin film lithium niobite and what it is like to work at a start-up company.
In the scientific part of his talk, Christian will give an introduction to the field of integrated photonics with thin-film lithium niobate, with a focus on electro-optic applications, as well as recent progress on transforming the field from chip-based proof-of-concept realizations for wafer-scale production.
In the professional development section, he will then share his experience transitioning from academia to a start-up company. He will talk about differences and similarities in the work environment, what to expect in terms of tasks and responsibilities, and explain how salaries at start-ups can include combinations of equity and incentives.
Christian Reimer´s mini bio: Dr. Christian Reimer is a physicist and entrepreneur working in the fields of nonlinear optics, integrated photonics and quantum optics. He received graduate degrees from the Karlsruhe Institute of Technology in Germany, Heriot-Watt University in Scotland, and the National Institute of Scientific Research in Canada. He then worked as a postdoctoral fellow at Harvard University, before becoming Co-Founder and Head of Product of HyperLight Corporation. HyperLight, a Venture-Capital funded start-up out of Harvard University, is specialized on integrated lithium niobate technologies for ultra-high performance photonic solutions.
The webinar will be on the 9th of December at 16:30 via zoom.