Nanoscience Colloquia

Open, advanced talks on nanoscience

The Nanoscience Colloquia – from the latin word ”loqui” that means talk – are a series of advanced talks on nanoscience, open to everyone within and outside academia. Welcome to join the conversation – see you in k-space at the Physics Department!

Flowering magnolias to symbolize spring term. Foto: Kennet Ruona

2024 Colloquia

Nanoscience colloquia are on Thursdays at 15:15 in k-space, department of Physics (unless otherwise stated).

Colloquia scheduled

16 Juni 2024

Birte Höcker - Learning from nature how to design new proteins

Abstract:

Proteins are the machines of life. These diverse macromolecules are essential for all cellular processes. Nature has generated an impressive set of proteins through evolution. Many protein structures and even more protein sequences are known by now. This enormous set of data can be used on the one hand to learn about the evolutionary history and how different proteins came about. On the other hand, we can extract information to be applied in the design of new tailor-made proteins. The ability to design custom proteins, such as reagent antibodies, biosensors or enzymes, is a major goal in protein biochemistry and will be necessary to tackle global challenges that we face today. Here I will discuss different approaches and show some highlights from our work on designing complex proteins.

Page of Birte Höcker

23 May 2024

Agustín Mihi - Scalable Photonic Architectures by Nanoimprinting Unconventional Materials

Abstract:

Photonic and plasmonic architectures can concentrate the electric field through resonances, increase the light optical path by strong diffraction and exhibit many other interesting optical phenomena that cannot be achieved with traditional lenses and mirrors. The use of these structures within actual devices will be most beneficial for enhanced light absorption solar cells, photodetectors and improved new sensors and light emitters. However, emerging optoelectronic devices rely on large area and low-cost fabrication routes to cut manufacturing expenses and increase the production throughput. If the exciting properties exhibited photonic structures are to be implemented in these devices, then they too have to be processed in a similar fashion as the devices they intend to improve.
In this presentation, I will illustrate how the technique of soft nanoimprinting lithography provides an exciting opportunity for the fabrication of nanostructures in a scalable, fast and inexpensive way. In our group, we use pre-patterned soft elastomeric stamps to induce a nanostructure in a variety materials from conductive polymers to cellulose1. We also use our patterned stamps to induce the long range ordering of metal colloids2 and perovskite nanocrystals3 in what is known as template-induced self-assembly. In all cases, the resulting photonic architectures can exhibit a resolution below 100 nm while covering an area of 1 cm2. This fabrication route allows us to combine the photonic properties of the pattern with those of the original material resulting in a new generation of inexpensive photonic components such as biodegradable photonic films, highly efficient SERS platforms for sensing, chiral metamaterials,4 improved efficiency solar cells and more.

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18 April 2024

Jos Haverkort - Direct bandgap hexagonal SiGe nanowires

Abstract:

It has been a holy grail for several decades to observe efficient direct bandgap emission from silicon. Unfortunately, cubic silicon has an indirect bandgap, thus impeding the efficient emission of light. The VLS growth method allows to grow III/V nanowires in either the cubic (zincblende) or hexagonal (wurtzite) crystal phase. This allows to grow hexagonal crystal phase SiGe nanowire shells that feature a direct bandgap in the spectral region between 1.5 and 3.4 µm. This material shows efficient light emission and a subnanosecond radiative recombination lifetime. Moreover, by transferring these nanowires to an AlN substrate, we observe a nonlinear increase of the Fabry-Perot cavity modes, thus providing a clear prove of stimulated emission in hex-SiGe. Our recent research is focused on hex-Ge/SiGe nanoshells which feature type I band alignment. Moreover, the quantum well emission spectra show clear quantum confinement effects. The realization of these quantum heterostructures in hex-SiGe is a first step towards quantum well lasers and hex-SiGe single photon emitters.

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11 January 2024 14:00

Very welcome to this mini-symposium with with Giovanni Volpe and Pawel Sikorski!

Agenda

14:00-14:45 Pawel Sikorski: Nanotechnology meets bioengineering with some examples from nano-, microfabrications and biomaterials.

14:45:15:15 Coffee Break

15:15-16:00 Giovanni Volpe: Deep Learning for Imaging and Microscopy

Speaker information and abstracts

Pawel Sikorski, Professor at the Department of Physics, Norwegian University of Science and Technology (NTNU)

Nanotechnology meets bioengineering with some examples from nano-, microfabrications and biomaterials.

In this presentation, I aim to highlight connections between bioengineering, biomaterials, and nanotechnology. I will introduce nanoscale effects that can be important for bioengineering research and technology development. Nanotechnology has the potential to contribute to biomedical research by providing new tools and new experimental methods. Compared to traditional approaches, these techniques often allow for miniaturization and better control of the experimental system. In our research, we are interested in the fabrication of nanostructured and microstructured surfaces that are easy and inexpensive to make, and that are compatible with typical workflows in biomedical research. I will describe two different fabrication approaches and how they are used to study biological systems.

Giovanni Volpe, Professor at the Department of Physics, University of Gothenburg

Deep Learning for Imaging and Microscopy

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 have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.