TY - JOUR A1 - Molinnus, Denise A1 - Janus, Kevin Alexander A1 - Fang, Anyelina C. A1 - Drinic, Aleksander A1 - Achtsnicht, Stefan A1 - Köpf, Marius A1 - Keusgen, Michael A1 - Schöning, Michael Josef T1 - Thick-film carbon electrode deposited onto a biodegradable fibroin substrate for biosensing applications JF - Physica status solidi (a) N2 - This study addresses a proof-of-concept experiment with a biocompatible screen-printed carbon electrode deposited onto a biocompatible and biodegradable substrate, which is made of fibroin, a protein derived from silk of the Bombyx mori silkworm. To demonstrate the sensor performance, the carbon electrode is functionalized as a glucose biosensor with the enzyme glucose oxidase and encapsulated with a silicone rubber to ensure biocompatibility of the contact wires. The carbon electrode is fabricated by means of thick-film technology including a curing step to solidify the carbon paste. The influence of the curing temperature and curing time on the electrode morphology is analyzed via scanning electron microscopy. The electrochemical characterization of the glucose biosensor is performed by amperometric/voltammetric measurements of different glucose concentrations in phosphate buffer. Herein, systematic studies at applied potentials from 500 to 1200 mV to the carbon working electrode (vs the Ag/AgCl reference electrode) allow to determine the optimal working potential. Additionally, the influence of the curing parameters on the glucose sensitivity is examined over a time period of up to 361 days. The sensor shows a negligible cross-sensitivity toward ascorbic acid, noradrenaline, and adrenaline. The developed biocompatible biosensor is highly promising for future in vivo and epidermal applications. KW - biocompatible materials KW - biodegradable electronic devices KW - biosensors KW - carbon electrodes KW - glucose Y1 - 2022 U6 - http://dx.doi.org/10.1002/pssa.202200100 SN - 1862-6319 N1 - Corresponding author: Michael J. Schöning VL - 219 IS - 23 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim ER - TY - JOUR A1 - Röthenbacher, Annika A1 - Cesari, Matteo A1 - Doppler, Christopher E.J. A1 - Okkels, Niels A1 - Willemsen, Nele A1 - Sembowski, Nora A1 - Seger, Aline A1 - Lindner, Marie A1 - Brune, Corinna A1 - Stefani, Ambra A1 - Högl, Birgit A1 - Bialonski, Stephan A1 - Borghammer, Per A1 - Fink, Gereon R. A1 - Schober, Martin A1 - Sommerauer, Michael T1 - RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria JF - Scientific Reports N2 - REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics. Y1 - 2022 U6 - http://dx.doi.org/10.1038/s41598-022-25163-9 SN - 2045-2322 VL - 12 IS - Article number: 20886 SP - 1 EP - 14 PB - Springer Nature CY - London ER - TY - JOUR A1 - Molinnus, Denise A1 - Iken, Heiko A1 - Johnen, Anna Lynn A1 - Richstein, Benjamin A1 - Hellmich, Lena A1 - Poghossian, Arshak A1 - Knoch, Joachim A1 - Schöning, Michael Josef T1 - Miniaturized pH-Sensitive Field-Effect Capacitors with Ultrathin Ta₂O₅ Films Prepared by Atomic Layer Deposition JF - physica status solidi (a) applications and materials science N2 - Miniaturized electrolyte–insulator–semiconductor capacitors (EISCAPs) with ultrathin gate insulators have been studied in terms of their pH-sensitive sensor characteristics: three different EISCAP systems consisting of Al–p-Si–Ta2O5(5 nm), Al–p-Si–Si3N4(1 or 2 nm)–Ta2O5 (5 nm), and Al–p-Si–SiO2(3.6 nm)–Ta2O5(5 nm) layer structures are characterized in buffer solution with different pH values by means of capacitance–voltage and constant capacitance method. The SiO2 and Si3N4 gate insulators are deposited by rapid thermal oxidation and rapid thermal nitridation, respectively, whereas the Ta2O5 film is prepared by atomic layer deposition. All EISCAP systems have a clear pH response, favoring the stacked gate insulators SiO2–Ta2O5 when considering the overall sensor characteristics, while the Si3N4(1 nm)–Ta2O5 stack delivers the largest accumulation capacitance (due to the lower equivalent oxide thickness) and a higher steepness in the slope of the capacitance–voltage curve among the studied stacked gate insulator systems. KW - atomic layer deposition KW - capacitive field-effect sensors KW - pH sensors KW - ultrathin gate insulators Y1 - 2022 U6 - http://dx.doi.org/10.1002/pssa.202100660 SN - 1862-6319 VL - 219 IS - 8 PB - Wiley-VCH CY - Weinheim ER - TY - JOUR A1 - Kaulen, Lars A1 - Schwabedal, Justus T. C. A1 - Schneider, Jules A1 - Ritter, Philipp A1 - Bialonski, Stephan T1 - Advanced sleep spindle identification with neural networks JF - Scientific Reports N2 - Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance. Y1 - 2022 U6 - http://dx.doi.org/10.1038/s41598-022-11210-y SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 12 IS - Article number: 7686 SP - 1 EP - 10 PB - Springer Nature CY - London ER - TY - RPRT A1 - Siegert, Petra A1 - Bongaerts, Johannes A1 - Wagner, Torsten A1 - Schöning, Michael Josef A1 - Selmer, Thorsten T1 - Abschlussbericht zum Projekt zur Überwachung biotechnologischer Prozesse mittels Diacetyl-/Acetoin-Biosensor und Evaluierung von Acetoin-Reduktasen zur Verwendung in Biotransformationen Y1 - 2022 N1 - Laufzeit: 01.01.2016 – 31.12.2019 (verlängert bis 31.12.2020) Förderkennzeichen: 322-8.03.04.02-FH-Struktur 2016/02 Gefördert durch: Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen CY - Aachen ER -