2023 BIST Symposium on Microscopy, Nanoscopy and Imaging Sciences

The sixth edition of the BIST Symposium on Microscopy, Nanoscopy and Imaging Sciences will take place on March 10, 2023, at Institute of Photonic Sciences (ICFO).

The event will include a series of talks and discussions on Optical Microscopy, Electron Microscopy, Scanning probe Microscopy, and Imaging Technology featuring experts in the various fields. This year, it will have an especial attention the topic of Machine learning on imaging analysis. A cutting-edge topic with a great interest in the field. Taking advantage that this Symposium is also part of the BIST Master curriculum, there will be a poster presentation by the master students in which they will be presenting their current research.

by Prof. Dr. Christina Scheu
Max-Planck-Institut für Eisenforschung GMBH Dusseldorf, Germany

 

Abstract

Climate change is one of the biggest threats for the human society and it is oft upmost importance to minimize it by changing the energy sector to an environmentally friendly one. Solar power can be used to generate electricity via solar cells or to spit water into oxygen and hydrogen, the later acting as fuel for fuel cells. Typically, the used materials for solar cells or photochemical cells are not ideal but have imperfections such as interfaces. Interfaces can occur within the same material (i.e. grain boundaries) or between dissimilar materials (i.e. heterophase interfaces including surfaces) and play an important role for the functionality of the material. Therefore, it is important to understand their atomic arrangement as well as the chemical composition with highest spatial resolution and sensitivity. In order to obtain this information, we correlate aberration corrected scanning transmission electron microscopy (STEM) with atom probe tomography (APT) to overcome the limits of both techniques. In ideal cases, we can apply this correlative approach at identical location by directly doing the STEM measurement at the APT needle. Like this, we were able to detect trace impurities at facetted grain boundaries in polycrystalline Si solar cells [1] explaining their properties. In other cases, such as hollow TiO2 nanowires which were annealed in a reducing atmosphere to increase their photocatalytic activity, we had to develop a new sample preparation technique [2]. We successfully embedded the material in a metallic matrix in order to field evaporate individual atoms from the APT tip. This allowed us to analyse the surface composition and we observed that B, Na and N segregated there. These impurities stem from the synthesis and annealing procedure [2]. Besides such static measurements, we also perform in-situ heating experiments in the STEM to investigate phenomena at liquid-solid interfaces. These are for example important for the solid-liquid-vapour growth of Al2O3 nanowires acting at catalyst support material [3].

[1] C. Liebscher, A. Stoffers, M. Alam, L. Lymperakis, O. Cojocaru-Miredin, B. Gault, J. Neugebauer, G. Dehm, C. Scheu, and D. Raabe Phys. Rev. Materials 2018, 2, 023804

[2] J. Lim, S.-H. Kim, R. Aymerich Armengol, O. Kasian, P.-P. Choi, L. T. Stephenson, B. Gault, and C. Scheu, Angew. Chem. Int. Ed. 2020, 59, 5651 –5655

[3] S. H. Oh, M. F. Chisholm, Y. Kauffmann, W. D. Kaplan, W. Luo, M. Rühle, and C. Scheu, Science 2010, 330, 489 – 493.

[4] The author would like to acknowledge all colleagues who have contributed to the work.

 

Bio

Prof. Christina Scheu holds a joint position as a full professor at the RWTH Aachen University and as an independent group leader at the Max-Planck-Institut für Eisenforschung GmbH (MPIE) in Düsseldorf Germany. From 2008-2014 she was a full professor at the Ludwig-Maximilian-University (Munich, Germany) where she started her research activities on renewable energy materials. Prof. Scheu has a diploma degree in physics and did her doctorate at the Max-Planck-Institute for Metals Research in Stuttgart (Germany) in the field of material science. She spent two years as a Minerva Fellow at the Technion – Israel Institute of Technology – in Haifa, Israel. Her expertise is the structural and chemical analysis of functional materials with ex-situ and in-situ transmission electron microscopy and electron energy loss spectroscopy in combination with atom probe tomorgraphy. The investigated materials range from (photo)catalyst for hydrogen production to thermoelectric materials. 

By Prof. Patricia Abellan
Institute of Materials of NanotesJean Rouxel, CNRS
Nantes, France

 

Abstract

The use of modern micro-fabricated chips and dedicated liquid holders in combination with (scanning) transmission electron microscopy, (S)TEM, allows the observation of functional materials under their operating conditions giving us our greatest ever opportunity to understand the origin of their unique properties. Using liquid cells in the (S)TEM, so termed liquid cell electron microscopy, LCEM, in situ observations of dynamic processes in solution can be made with (sub)nanometer spatial resolution. The flexibility of this approach for studying liquid phase reactions relies upon both: the possibility of obtaining information by using well established as well as novel capabilities in the TEM platform, since liquid cells are designed to fit in any TEM, and the possibility of customizing the design of the experiment. Indeed, the experiment layout can be adapted for a specific application by using chips designed for continuous flow applications, mixing of two chemicals or with electrical contacts. In LCEM, a high-energy electron beam (typically 30-300 keV) is used to probe a sample while it unavoidably induces typically unwanted, chemical processes due to radiolysis. A main focus in the field has been to find means to perform LCEM while keeping the electron dose as low as possible. However, for many experiments and specimens, current standards are still insufficient. The major limitation to many experiments in LCEM is indeed the high sensitivity of solutions to ionizing radiation and the very high doses used in electron microscopy. In this presentation, I will introduce the field of liquid cell electron microscopy, the range of applications currently possible, as well as the challenges encountered by the LCEM community and some of the fundamental processes undergoing during experiments. 

By Prof. Sergei V. Kalinin
Oak Ridge National Laboratory and University of Tennessee-Knoxville
Tenesse, USA

 

Abstract

The last note left by Richard Feynman stated “What I cannot create, I do not understand.” Building solid state quantum computers, creating nanorobots, and designing new classes of biological molecules and catalysts alike requires the capability to manipulate and assemble matter atom by atom, probe the resulting structures, and connecting them to macroscopic world. In this presentation, I will discuss recent progress in automated experiment in electron microscopy, ranging from feature and physics discovery via active learning to direct atomic fabrication. I introduce the concept of the reward-driven experimental workflow planning and discuss how these workflows can be implemented via domain-specific hyper languages. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. The real-time image analysis allows spectroscopic experiments at the predefined features of interest and atomic manipulation and modification with preset policies. I further illustrate ML methods for autonomous discovery, where the microstructural elements maximizing physical response of interest are discovered. These deep kernel learning (DKL) methods offer significant advantage compared to simple Gaussian Processes often tend to produce sub-optimal results due to the lack of prior knowledge and very simplified (via learned kernel function) representation of spatial complexity of the system. The DKL AE is illustrated via experimental discovery of the edge plasmons in STEM/EELS. The forensic analysis of the automated experiment makes the discovery process explainable and allows for human in the loop interventions. Finally, I will discuss the opportunities and strategies for direct atomic fabrication via electron beams, targeting desired structures and desired functionalities.

Prof. Carmen Rubio Verdú
Institute of Photonics Sciences (ICFO)
Barcelona, Spain

by Prof. Sophie Brasselet
Institute Fresnel
Marselle, France

 

Abstract

maging molecular orientation at the nanoscale in live cells and tissues is fundamental in the understanding of proteins’ organization, which is driven by their structural and conformational properties. Measuring fluorescent molecules’ orientation is a way to approach this problem, providing that the label is rigidly attached to the protein of interest. Despite the great progresses in fluorescence imaging down to nanometric scales with Single Molecule Localization Microscopy (SMLM), orientation imaging is still only at its early stage. Measuring single molecules’ 3D orientations in addition to their 3D spatial localization is a challenge due to the difficulty to disentangle spatial and orientational parameters in the SM point spread function (PSF) image formation, nevertheless the search for optimal methods to solve this challenge is rapidly progressing. We present examples of polarized fluorescence microscopy methods that are able to report both orientational and spatial information from single molecules in a non-ambiguous way. These methods, based on phase manipulation [1] or polarized splitting imaging or Fourier plane polarization [2], give access to orientation parameters in combination with high spatial localization precision. We will present the potential and limits of these approaches for the imaging of the nanoscale organization of proteins in cells. 

[1] V. Curcio et al. Nat. Communications 11 (1) (2020) DOI: 10.1038/s41467-020-19064-6 

[2] C. Rimoli et al. Nat. Communications 13, 301 (2022). DOI: 10.1038/s41467-022-27966-w  

By Prof. Ricardo Henriques
Instituto Gulbenkian de Ciência
Oeiras, Portugal

 

Abstract

Computational analysis has become an essential part of microscopy, enabling and enhancing quantitative imaging approaches. Several cutting-edge microscopy methods now depend on an analytical step to process large volumes of recorded data, extract analytical information, and produce a final rendered image. Single-molecule localization-based super-resolution microscopy is a notorious example. In recent years, our team and collaborators have built an open-source ecosystem of combined computational and optical approaches particularly dedicated to improving live-cell microscopy, super-resolution imaging, and helping researchers retrieve high-fidelity quantitative data from their images. This talk will present some of the recent technologies we have recently developed. First, I will introduce ZeroCostDL4Mic, an entry-level platform simplifying the application of Deep-Learning (DL) analysis to biological microscopy images, by exploiting free openly-accessible cloud-based computational resources. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL tasks to perform segmentation, object detection, denoising, super-resolution microscopy, and microscopy modality image-to-image translation. We’ll demonstrate the application of the platform to study multiple biological processes, including in eucaryotic and procaryotic cells, and to analyze SMLM data. Next, I will cover recent development we have created for super-resolution microscopy through the NanoJ platform, highlighting the newenhanced Super-Resolution Radial Fluctuations” (eSRRF) approach and its combination with real-time controlled microfluidics live-to-fix cell imaging, dubbed NanoJ-Fluidics, as well as real-time quality control on the predicted superresolution images via the SQUIRREL.

By Prof. Giovanni Volpe
University of Gothenburg
Ghotenburg, Sweden

 

Abstract

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.

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