Beatrix Campos Estrada
The nature and composition of small planets’ interiors remain uncertain. Catastrophically evaporating rocky planets provide a unique opportunity to study the composition of small planets. The surface composition of these planets can be constrained via modelling their comet-like tails of dust. In this work we present a new self-consistent model of the dusty tails. We model two catastrophically evaporating planets: KIC 1255 b and K2-22 b. For both planets we find the dust is likely composed of magnesium-iron silicates (olivine and pyroxene), consistent with an Earth-like composition.
Till Käufer
Planets form in so-called protoplanetary disks. These regions are investigated to improve our understanding of planet formation and analyse the material that will make up planets. The analysis is done by the comparison of models to observations. However, here lie many challenges, especially regarding the computational speed of complex disk models.
During my PhD I tackled this problem in different ways, including Machine Learning and the development of new disk models. This talk is focused on two projects. I present how we created Neural networks that can predict the spectral energy distributions (SEDs) of protoplanetary disks within milliseconds. We performed a full Bayesian analysis for 30 well-known protoplanetary disks to determine their physical disk properties, including uncertainties and degeneracies. Additionally, I show how we created a disk model that can describe the molecular line emission as well as the dust continuum as seen by the James Webb Space Telescope. We benchmark this model and apply it to a JWST spectrum to extract the conditions under which molecules emit in protoplanetary disks.
Sven Kiefer
In our Solar System, seven out of eight planets have clouds. The materials that the clouds are made out of and how they impact the thermal structure strongly varies between the planets. For exoplanets, observing the clouds with space telescopes is difficult. To gain insights into clouds on exoplanet atmospheres, we rely on models and simulations. In my work, I have studied the interplay of clouds with the chemistry and the dynamics within exoplanet atmospheres. Here, I will present two important findings of my work. First, I will talk about the impact of cloud formation on the gas-phase. Not only do clouds deplete the atmosphere but they can also act as catalysts for gas-phase species. Secondly, I will show how clouds interacting with radiation coming from the host star can impact the thermal structure of the atmosphere. To conduct such simulations, the global climate of exoplanets has to be modeled using General Circulation Models which were original designed to study the climate of Earth.
Gabriel Hernández Rodríguez
The presence of materials that can effectively act against the formation and accretion of ice would have a tremendous impact on how we experience daily life during winter seasons or in places where low temperatures are a constant challenge. The current demand is towards passive anti-icing systems, i.e., materials that prevent and autonomously mitigate ice without any external power input. Gradient polymer coatings deposited via initiated chemical vapor deposition (iCVD) showed promising icephobic properties and proved to fully act in different aspects. These coatings significantly decrease ice adhesion, the freezing of water drops is delayed more than 5 hours, and frost propagation occurs extremely slowly. The limited mobility of condensation droplets consequence of strong pinning and the presence of dry zones played an important role. However, gradient polymers showed no correlation between wettability and icephobicity, then, a very important question arises: “What is the origin of the icephobic properties?”. We found out that the icephobicity is related to a surface energy discontinuity produced by a random orientation of fluorinated groups at the coating's surface. Hence, tuning the architecture in gradient polymers via iCVD resulted in a simple and effective approach to induce randomness and thus, promote icephobicity, showing how slight changes at atomistic level have drastic macroscopic consequences on this property.
Theodoros Dimitriadis
Controlling water transport and management is imperative for ensuring the reliable operation of systems amidst harsh climatic conditions. Both external and internal components play pivotal roles in facilitating smooth system operation. While passive strategies employing nonwetting surfaces are desirable, the integration of superhydrophobic coatings into practical applications has been hindered by durability concerns and, in certain instances, non-compliance with environmental regulations. The method of application significantly influences the conformality and uniformity of coatings, particularly on internal components such as electronics. In this study, we present the development of robust surfaces based on discontinuity-enhanced coatings, targeting two distinct objectives: (a) enhancing water transport and management in external components through contrast wettability for capillary-driven mechanisms, and (b) achieving conformal and uniform protection for internal components via gradient polymerization. Drawing inspiration from natural surface patterning observed in living organisms, the fabrication process for external coatings involves the integration of a hydrophobic coating with hard-anodized aluminium patterning, employing a scalable femtosecond laser microtexturing technique. Conversely, for internal components, we leverage initiated chemical vapor deposition (iCVD) for its adaptability to substrates with complex geometrical features,
flexibility in surface chemistry modification, superior adhesion, and chemical resistance. Ultra-thin iCVD coatings, measuring less than 500 nm in thickness, are applied as conformal coatings for electronic components, particularly those characterized by stringent dimension tolerances. Our conceptual framework targets heavy-duty engineering applications, particularly in environments characterized by aggressive weather conditions where corrosion prevails, impacting both external and internal components. The modified substrates, featuring discontinuity-enhanced characteristics, exhibit enduring durability in both standardized natural and lab-based artificial and corrosion tests, contrasting with the degradation tendencies observed in superhydrophobic coatings. In summary, our study offers insights into the development of durable coatings with tailored characteristics for enhanced water transport and management in engineering systems, bridging the gap between theoretical advancements and practical applications amidst challenging environmental conditions.
Henrik Siboni
Nanoparticles have proven to be an effective way of administering drugs in the form of so-called nanoscale drug delivery systems [1]. While atomic force microscopy is a popular method in the nanosciences, its use within pharmaceutics has so far been limited [2]. In this project, we have therefore used atomic force microscopy to study a specific type of soft nanoparticle called proticles [3] which consist of the pharmaceutically active ingredient microRNA and the recipient – a helper molecule – protamine which is a short peptide.
Nanoparticles can be imaged with atomic force microscopy by depositing them on a surface, so I first present how we improved proticle images by choosing the right concentration and substrate during the deposition procedure. By comparing the volume to dynamic light scattering measurements, we find that the proticles consist of >99 % water by volume. By mixing citric acid into the proticle, we further show that the shape of deposited proticles shifts slightly into being less compact [4].
Following this, I will go on to discuss further approaches that we have investigated.
1. Blanco, E.; Shen, H.; Ferrari, M. Principles of Nanoparticle Design for Overcoming Biological Barriers to Drug Delivery. Nature Biotechnology 2015 33:9 2015, 33, 941–951, doi:10.1038/nbt.3330.
2. Pillet, F.; Chopinet, L.; Formosa, C.; Dague, É. Atomic Force Microscopy and Pharmacology: From Microbiology to Cancerology. Biochimica et Biophysica Acta (BBA) - General Subjects 2014, 1840, 1028–1050, doi:10.1016/J.BBAGEN.2013.11.019.
3. Junghans, M.; Kreuter, J.; Zimmer, A. Antisense Delivery Using Protamine–Oligonucleotide Particles. Nucleic Acids Res 2000, 28, e45–e45, doi:10.1093/nar/28.10.e45.
4. Fresacher-Scheiber, K.; Ruseska, I.; Siboni, H.; Reiser, M.; Falsone, F.; Grill, L.; Zimmer, A. Modified Stability of MicroRNA-Loaded Nanoparticles. Pharmaceutics 2022, 14, 1829, doi:10.3390/pharmaceutics14091829.
Johannes Mellak
Solving strongly correlated quantum many-body problems is limited by the exponential growth of the Hilbert space comprising all possible realizations of a system. A similar situation arises in most deep learning problems, where a high dimensional input is to be reduced to a small number of relevant features. So-called neural network quantum states (NQS) [1] can efficiently express strongly correlated quantum wavefunctions (or density matrices) without relying on presumptions. These methods were initially based on and motivated by physical systems, but the problem of finding approximations of quantum wavefunctions can also be interpreted as a strongly non-convex optimization problem using ”black-box” variational ansatz functions. In analogy to the eigenvalue problem of the groundstate energy we investigate open quantum systems and how to obtain the non-equilibrium steady state solution of a Lindblad master equation using NQS. We develop an advanced optimization algorithm that is capable of finding the steady state density matrix to calculate transport properties in a driven dissipative spin chain. We also address some weaknesses of prevalent NQS methods and introduce a more general ansatz that can be designed to incorporate certain symmetries and invariances, improving upon previous results with less computational effort and greater scalability.
[1] G. Carleo and M. Troyer, “Solving the Quantum Many-Body Problem with Artificial Neural Networks,” Science, vol. 355, pp. 602–606, Feb. 2017. arXiv: 1606.02318.
Daniel Teubenbacher
Understanding Mercury's magnetosphere is a primary goal of the BepiColombo mission. In addition to spacecraft observations, numerical modeling efforts have shown to add invaluable insight to the Hermean magnetic field topology, current systems and plasma distributions. However, existing comparisons between observed and modeled data are predominantly qualitative, lacking quantitative agreement due to diverse mathematical approaches. Notably, quantitative inconsistencies of observed and modeled ion densities and energies are particularly affected. Hence, this study addresses systematic and stochastic deviations, focusing on establishing confidence intervals for "ion counting" within the hybrid AIKEF (Adaptive Ion Kinetic Electron Fluid) model. The kinetic treatment of the ions enables to directly compare model results with observations of the Planetary Ion Camera (PICAM), which is a part of the SERENA suite onboard the BepiColombo mission. Multiple ion counting methods are introduced and evaluated, including a simple sphere method, an omnidirectional method, and a field-of-view method. Our findings demonstrate that applying the field-of-view method to the modeled data, within the derived confidence interval, yields ion velocity distributions consistent with PICAM observations of Mercury’s magnetosheath. The AIKEF model and the developed analysis tools serve as a powerful and convenient method of reproducing the ion and electro-magnetic field profile around Mercury for the BepiColombo mission, both in flyby and in-orbit measurements.
Moritz Theissing: Investigation of the Recrystallization Behavior of Aluminum Alloys using in situ Methods in Electron Microscopy
Elias Ehl: UV Dual-Comb Spectroscopy for Laboratory Astrophysics
Armin Speletz: Dual-Comb Raman Spectroscopy
Anna Karner: (Extreme) Ultraviolet Metaoptics
Isabella Kohlhauser: Elevation Dependency of Temperature in the Greater Alpine Region
Josef Simbrunner: An efficient method for indexing grazing-incidence X-ray diffraction data of epitaxially grown thin films
Simon Hollweger: Optimizing Growth Conditions for Metastable Polymorphs using Deep Reinforcement Learning
Dean Vidakovic: Systematic DIsecting for Controlled ReforMulation of PoLymer-BlEnd-Systems with CompleX Functionalities