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This study presents the concept of AstroBioLab, an autonomous astrobiological field laboratory tailored for the exploration of (sub)glacial habitats. AstroBioLab is an integral component of the TRIPLE (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) DLR-funded project, aimed at advancing astrobiology research through the development and deployment of innovative technologies. AstroBioLab integrates diverse measurement techniques such as fluorescence microscopy, DNA sequencing and fluorescence spectrometry, while leveraging microfluidics for efficient sample delivery and preparation.
We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan–Meier estimator.
In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.
Atmospheric pressure plasma-jet treatment of PAN-nonwovens—carbonization of nanofiber electrodes
(2022)
Carbon nanofibers are produced from dielectric polymer precursors such as polyacrylonitrile (PAN). Carbonized nanofiber nonwovens show high surface area and good electrical conductivity, rendering these fiber materials interesting for application as electrodes in batteries, fuel cells, and supercapacitors. However, thermal processing is slow and costly, which is why new processing techniques have been explored for carbon fiber tows. Alternatives for the conversion of PAN-precursors into carbon fiber nonwovens are scarce. Here, we utilize an atmospheric pressure plasma jet to conduct carbonization of stabilized PAN nanofiber nonwovens. We explore the influence of various processing parameters on the conductivity and degree of carbonization of the converted nanofiber material. The precursor fibers are converted by plasma-jet treatment to carbon fiber nonwovens within seconds, by which they develop a rough surface making subsequent surface activation processes obsolete. The resulting carbon nanofiber nonwovens are applied as supercapacitor electrodes and examined by cyclic voltammetry and impedance spectroscopy. Nonwovens that are carbonized within 60 s show capacitances of up to 5 F g⁻¹.
Carbon nanofiber nonwovens represent a powerful class of materials with prospective application in filtration technology or as electrodes with high surface area in batteries, fuel cells, and supercapacitors. While new precursor-to-carbon conversion processes have been explored to overcome productivity restrictions for carbon fiber tows, alternatives for the two-step thermal conversion of polyacrylonitrile precursors into carbon fiber nonwovens are absent. In this work, we develop a continuous roll-to-roll stabilization process using an atmospheric pressure microwave plasma jet. We explore the influence of various plasma-jet parameters on the morphology of the nonwoven and compare the stabilized nonwoven to thermally stabilized samples using scanning electron microscopy, differential scanning calorimetry, and infrared spectroscopy. We show that stabilization with a non-equilibrium plasma-jet can be twice as productive as the conventional thermal stabilization in a convection furnace, while producing electrodes of comparable electrochemical performance.
Attitude and Orbital Dynamics Modeling for an Uncontrolled Solar-Sail Experiment in Low-Earth Orbit
(2015)
Gossamer-1 is the first project of the three-step Gossamer roadmap, the purpose of which is to develop, prove and demonstrate that solar-sail technology is a safe and reliable propulsion technique for long-lasting and high-energy missions. This paper firstly presents the structural analysis performed on the sail to understand its elastic behavior. The results are then used in attitude and orbital simulations. The model considers the main forces and torques that a satellite experiences in low-Earth orbit coupled with the sail deformation. Doing the simulations for varying initial conditions in attitude and rotation rate, the results show initial states to avoid and maximum rotation rates reached for correct and faulty deployment of the sail. Lastly comparisons with the classic flat sail model are carried out to test the hypothesis that the elastic behavior does play a role in the attitude and orbital behavior of the sail
Close interrelations between sound and image are not a mere phenomenon of today’s multimedia technology. The idea of the synthesis of different media lies at the core of the concept of the Gesamtkunstwerk in the second half of the 19th century and it can also be traced back to the synaesthesia debate at the beginning of the 20th century [...].
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.