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Biocompatible and Interactive Artificial Micro- and Nanoswimmers and Their Applications

Periodic Reporting for period 4 - ComplexSwimmers (Biocompatible and Interactive Artificial Micro- and Nanoswimmers and Their Applications)

Okres sprawozdawczy: 2021-03-01 do 2021-08-31

The interest in active matter, of which microswimmers are an example, arises both for fundamental and applied reasons. On the fundamental side, the study of active matter can shed light on the far-from-equilibrium physics underlying the adaptive and collective behavior of biological entities such as chemotactic bacteria and eukaryotic cells. From the more applied side, active matter provides tantalizing options to perform tasks not easily achievable with other available techniques, such as the targeted localization, pick-up and delivery of microscopic and nanoscopic cargoes in applications such as drug delivery, bioremediation and chemical sensing.
However, at the beginning of this project, there were still several open challenges that needed to be tackled in order to achieve the full scientific and technological potential of active matter in real-life settings. The main challenges were:
(1) to identify a biocompatible propulstion mechanism and energy supply capable of lasting for the whole particle life-cycle;
(2) to understand their behavior in complex and crowded environments;
(3) to learn how to engineer emergent behaviors; and (4) to scale down their dimensions towards the nanoscale.
The ERC-StG ComplexSwimmers aimed at tackling these challenges by developing biocompatible microswimmers capable of elaborate behaviors, by engineering their performance when interacting with other particles and with a complex environment, and by developing working nanoswimmers.
To achieve these goals, we laid out a roadmap that led us to push the frontiers of the current understanding of active matter both at the mesoscopic and at the nanoscopic scale, and permitted us to develop some technologically disruptive techniques.
In this project, we have developed some more complex microswimmers, we have studied their motion in crowded environments, we have studied how autonomous agents react to some sensorial cues, and we have developed nanoswimmers.

We have realised some new species of active microscopic systems. First, we have developed a microscopic critical engine using an optically trapped particle immersed in a critical mixture. We have also realised active colloidal molecules that self-assemble starting from passive building blocks whenever they are illuminated. We have finally worked on the development of self-phoretic microswimmers. Building on these results we have then developed the so-called "active droploids", where a feedback between particles and environment leads to the formation of self-propelling drops encapsulating microswimmers.

We have studied how microswimmers interact with a background of passive particles. We have done this by employing numerical simulations and experiments with bacteria and colloidal particles. These results have led us to develop new techniques for quantitative microscopy based on the use of deep learning and to the development of the open-source software for quantitative digital microscopy DeepTrack (now at version DeepTrack 2.0). We have widely applied this software to problems in microscopy and active matter (e.g. to characterise nanoparticles and to virtually satin tissues).

We have studied the emergence of complex phototactic behaviours in the presence of sensorial delays. We have done this using theory, numerical simulations and swarms of microrobots.

We have also developed and studied some new class of nanoswimmers extending the concept of the microscopic critical engine towards the nanoscale. This in particular has permitted us to explore the behaviour of nanoswimmers and their differences from microswimmers.

Finally, we have introduced new methods to calibrate optical traps (FORMA), for digital video microscopy (DeepTrack) and for the characterisation of anomalous diffusion (DeepCalib), based on artificial intelligence and deep learning.

These results have been widely disseminated in scientific publications and conference.
Furthermore, the results of this project have led to the creation of a startup company, Lucero Bio AB, which has been supported in its first phases by the ERC-PoC Lucero and is now growing within the Chalmers Ventures incubation program on the AstraZeneca Gothenburg Innovation Campus.
The results obtained during the realisation of the ERC-StG ComplexSwimemrs have taken us greatly beyond the current state of the art. This has been particularly true in the development of nanoswimmers and in the creation of tools to combine digital video microscopy with deep learning. Furthermore, we have challenged several established theories. In particular, we have shown that Levy flights are not optimal for foraging in the presence of complex environments and we have developed more powerful tools (FORMA) to calibrate optical tweezers.
Using a neural network, DeepTrack determines the exact position of a microscopic particle.