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Die Beiträge, der Konferenz:
• Nanotechnologie –Von Wunder-Materialien und solchen die es werden wollen
• Mikrotechnik –to improve the qualityof live
• Quantum Sensing for Industrial Applications
• Geordnete Defekte in Graphit: Ein Fahrplan in Richtung Raumtemperatur Supraleitung
• Funktionale mikro-und nanostrukturierte Folien als Bestandteil hochintegrierter Systeme
This paper deals with the issue of automating the
process of machine learning and analyzing bio-datasets. For this
a user-friendly website has been developed for the interaction
with the researchers. On this website it is possible to upload
datasets and to share them, if desired, with other scientists. The
uploaded data can also be analyzed by various methods and
functions. The signals inside these datasets can also be visualized.
Furthermore several algorithms have been implemented to create
machine learning models with the uploaded data. Based on these
generated models new data can be classified or calculated. For all
these applications the simplest possible handling was
implemented to make the website available to all interested
researchers.
When developing new battery technologies, fundamental research means assembling new batteries by hand since a production line is not worthwhile for building and testing individual cells. This causes high production tolerances to occur because manual manufacturing is not as precise as machine-made. When putting these prototypes into operation, problems can arise due to the varying parameters. One of the most important exercise is finding a criterion of a full battery. This can be challenging when parameters like the capacity or the end of charge voltage are not precisely known due to the tolerances. Furthermore, new battery types do not necessarily rely on the same stopping criteria. For example zinc-air secondary batteries do not offer an end of charging voltage. Its charging current is not going to decrease when the battery is full and the charging voltage is held at a fixed value. But instead of de-oxidising zinc oxide, hydrogen is produced. In the majority of cases overcharging should be avoided as it harms the battery. Another even more dangerous consequence is the possibility of an explosion. Especially lithium based batteries are known for their need of compatible ambient and charging parameters. This paper proposes a new criterion for detecting the end of charge that is based on the rate of change of electrochemical impedance spectra of the examined batteries. Device parameter fluctuations influence every measurement. Therefore, using the rate of change offers the possibility to not depend on these fluctuations.
The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal–air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc–air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents.
Keywords:
electrochemical impedance spectroscopy; artificial neural networks; support vector regression; zinc-air battery; state estimation; state of charge
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
Metal air batteries provide a high energy density as the ca-thodic reaction uses the surrounding air. Different metals can be usedbut zinc is very promising due to its disposability and nontoxic behav-ior. State estimation is quite complicated as the voltage characteristicof the battery is rather flat. Especially estimating the state of chargeis important as a secondary electrolysis process during overcharging canlead to an unsafe state. Another technique for state estimation is theelectrochemical impedance spectroscopy. Therefore, this paper describesthe process of setup and measuring a time series of impedance spectraat known states of charge. Then these spectra are used to derive anequivalent circuit. Finally the development of the circuit’s parameter areanalyzed to extract most important parameters.