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Multilayers of silicon oxide/silicon nitride/silicon oxide (ONO) are known for their good
electret properties due to deep energy traps near the material interfaces, facilitating charge
storage. However, measurement of the space charge distribution in such multilayers is a
challenge for conventional methods if layer thickness dimensions shrink below 1 μm. In this
paper, we propose an atomic force microscope based method to determine charge distributions in
ONO layers with spatial resolution below 100 nm. By applying Kelvin probe force microscopy
(KPFM) on freshly cleaved, corona-charged multilayers, the surface potential is measured
directly along the z-axis and across the interfaces. This new method gives insights into charge
distribution and charge movement in inorganic electrets with a high spatial resolution.
Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach to model complex cell systems artificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting (EBB) process was used to deposit human embryonic kidney (HEK) cells in a defined pattern. It was shown that the bioprinted construct featured a high degree in viability reaching up to 77% 10 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be applied to in vitro drug screening tests using various types of cells.
Terrestrial Trunked Radio (TETRA) is a worldwide common mobile communication standard, used by authorities and organizations with security tasks. Previous studies reported on health effects of TETRA, with focus on the specific pulse frequency of 17.64 Hz, which affects calcium efflux in neuronal cells. Likewise among others, it was reported that TETRA affects heart rate variability, neurophysiology and leads to headaches. In contrast, other studies conclude that TETRA does not affect calcium efflux of cells and has no effect on people's health.
In the present study we examine whether TETRA short- and long-term exposure could affect the electrophysiology of neuronal in vitro networks. Experiments were performed with a carrier frequency of 395 MHz, a pulse frequency of 17.64 Hz and a differential quaternary phase-shift keying (π/4 DQPSK) modulation. Specific absorption rates (SAR) of 1.17 W/kg and 2.21 W/kg were applied.
In conclusion, the present results do not indicate any effect of TETRA exposure on electrophysiology of neuronal in vitro networks, neither for short-term nor long-term exposure. This applies to the examined parameters spike rate, burst rate, burst duration and network synchrony.
Microelectrode array (MEA) technology in combination with three-dimensional (3D) neuronal cell models derived from human embryonic stem cells (hESC) provide an excellent tool for neurotoxicity screening. Yet, there are significant challenges in terms of data processing and analysis, since neuronal signals have very small amplitudes and the 3D structure enhances the level of background noise. Thus, neuronal signal analysis requires the application of highly sophisticated algorithms. In this study, we present a new approach optimized for the detection of spikes recorded from 3D neurospheres (NS) with a very low signal-to-noise ratio. This was achieved by extending simple threshold-based spike detection utilizing a highly sensitive algorithm named SWTTEO. This analysis procedure was applied to data obtained from hESC-derived NS grown on MEA chips. Specifically, we examined changes in the activity pattern occurring within the first ten days of electrical activity. We further analyzed the response of NS to the GABA receptor antagonist bicuculline. With this new algorithm method we obtained more reliable results compared to the simple threshold-based spike detection.
In this work we present a new micro-system-technology based production process for
unipolar ferroelectrets. Theoretical analysis of the influence of the air-gap size on the electric
field distribution as well as on the induced charge on the electrodes shows superior performance
of unipolar piezoelectrets with small air-gaps. For the production of these small air-gaps we
developed a new design using a photoresist thermoforming master, an integrated micro-heater
and shadow masks for metallization. Unipolar piezoelectrets produced with this technology
exhibit increased d33-coefficients compared to designs in previous publications. These
piezoelectrets are highly preferable for energy-harvesting applications, as they promise high
electric power output.
Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here,
we used an EEG dataset to investigate the ability to automatically detect changes in brain
activity using machine learning and complex networks. Machine learning was applied at
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of
the EEG time series, and (C) the complex network measures calculated from (B). Further, at
the abstraction level of (C), we developed new measures of complex networks relating to
community detection. As a result, the machine learning method was able to automatically
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are
more important for the detection of ayahuasca. The most activated areas were the frontal
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic
mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that
the dissemination of information in functional brain networks is slower when this drug is
present. Overall, our methodology was able to automatically detect changes in brain activity
during ayahuasca consumption and interpret how these psychedelics alter brain networks,
as well as provide insights into their mechanisms of action
Piezoelectrets fabricated from fluoroethylenepropylene (FEP)-foils have shown drastic increase of their piezoelectric
properties during the last decade. This led to the development of FEP-based energy harvesters, which are about to evolve
into a technology with a power-generation-capacity of milliwatt per square-centimeter at their resonance frequency. Recent
studies focus on piezoelectrets with solely negative charges, as they have a better charge stability and a better suitability for
implementation in rising technologies, like the internet of things (IOT) or portable electronics. With these developments
heading towards applications of piezoelectrets in the near future, there is an urgent need to also address the fabrication
process in terms of scalability, reproducibility and miniaturization. In this study, we firstly present a comprehensive review
of the literature for a deep insight into the research that has been done in the field of FEP-based piezoelectrets. For the first
time, we propose the employment of microsystem-technology and present a process for the fabrication of thermoformed
FEP piezoelectrets based on thermoforming SU-8 templates. Following this process, unipolar piezoelectrets were fabricated with air void dimensions in the range of 300–1000 lm in width and approx. 90 lm in height. For samples with a void
size of 1000 lm, a d33-coefficient up to 26,508 pC/N has been achieved, depending on the applied seismic mass. Finally,
the properties as energy harvester were characterized. At the best, an electrical power output of 0.51 mW was achieved for
an acceleration of 1 g with a seismic mass of 101 g. Such piezoelectrets with highly defined dimensions show good
energy output in relation to volume, with high potential for widespread applications.
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.