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Verfahren zur Beanspruchung von Partikeln mit Elektroimpulsen und Verwendung einer Vorrichtung zur Beanspruchung von Partikeln
The invention relates to a method for stressing particles (100) by means of electric pulses (200) using a device (1) comprising a process column (2) with a vertically oriented process chamber (3), wherein the process chamber (3) has an inlet (4) at an upper end (5) of the process column (2) and an outlet (6) at a lower end (7) of the process column (2), and the process chamber (3) has a substantially constant width (30) between the inlet (4) and the outlet (6). A supply device (8) for supplying particles (100) to be stressed is paired with the inlet (4), and a discharge device (9) for discharging stressed particles (100) is paired with the outlet (6). The process chamber (3) is associated with two electrodes (10) which are provided on different sides of the process chamber (3) and which are connected to an electric pulse generator (11). The particles (100) to be stressed are continuously guided, as a particle bed (150), from the inlet (4) to the outlet (6) through the process chamber (3) and are stressed by means of electric pulses (200) using the electrodes (100). The invention also relates to the use of such a device for stressing particles (100) by means of electric pulses (200) in order to carry out a method according to one of the preceding claims.
Computer-implemented Method for Single Time Point Dosimetry
The present invention relates to a computer-implemented single time-point dosimetry method (200) comprising the following steps: a. Deriving for one or more organs at least one pre-therapy metrics from a pre-therapy scan of a patient (201); b. Deriving for the one or more organs the activity value at a specific time from a posttherapy scan of the patient (203); c. Computing the time-integrated activity value for the one or more organs (204); wherein the time-integrated value is computed based on the activity value from the posttherapy scan, the specific time of the posttherapy scan and a predicted effective half-life value, and wherein the predicted effective half-life value is determined by a machine learning-model trained according to the method of the present invention.
Vorrichtung und Verfahren zur Entfernung von Stickoxiden aus dem Abgas von mager betriebenen Wasserstoffverbrennungsmotoren
Eine Vorrichtung (1) zur Entfernung von Stickoxiden aus dem Abgas von mager betriebenen Wasserstoffverbrennungsmotoren (2) weist einen ersten Katalysator (3), der einen Grundkörper (5), eine auf dem Grundkörper (5) aufgebrachte Trägerschicht (6) aus einem Metalloxid und eine auf der Trägerschicht (6) aufgebrachte aktive Komponente (7) aus Gold und/oder Rhodium aufweist und in dem die Stickoxide mit Wasserstoff zu Ammoniak umgesetzt werden, und einen dem ersten Katalysator (3) in Strömungsrichtung (x) des Abgases nachgeschalteten zweiten SCR-Katalysator (4) auf, in dem mit dem in dem ersten Katalysator (3) erzeugten Ammoniak in dem ersten Katalysator (3) nicht umgesetzten Stickoxide umgesetzt werden.
Success Stories
Featured Spin-off Companies
Companies founded on technologies from our partner institutions
LabCrate Solutions
LabCrate Solutions designs modular, customizable substrate storage systems for research laboratories, offering protection and efficient organization for valuable samples.
Mo:re
Mo:re develops an AI-assisted robotic platform that automates 3D cell culture for animal-free medical research.
minimum energy GmbH
minimum energy GmbH provides advanced software for the optimized planning of PV and battery storage systems, supporting data-driven energy investment decisions for planners and installers.
Participating Research Organizations
Explore the technology portfolios of leading research institutions.
University of Hamburg
University of Bern
Helmholtz-Zentrum Berlin für Materialien und Energie
Forschungszentrum Jülich
Paul Scherrer Institut
Friedrich-Alexander-Universität Erlangen-Nürnberg
Universität für Bodenkultur Wien
Universität Osnabrück
Hochschule Osnabrück
Technische Universität Bergakademie Freiberg
Ostbayerische Technische Hochschule Regensburg
Paracelsus Medizinische Privatuniversität (PMU)
University of Bonn
Fraunhofer FOKUS
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