@inproceedings{KoerbitzerKraussSchneideretal.2016, author = {K{\"o}rbitzer, Berit Silke and Krauß, Peter and Schneider, J{\"o}rg and Thielemann, Christiane}, title = {Characterization Of Graphene-Coated Microelectrode Arrays For Recording And Stimulation Of Neuronal Cells}, series = {Frontiers}, volume = {2016}, booktitle = {Frontiers}, number = {MEA Meeting 2016}, doi = {10.3389/conf.fnins.2016.93.00013}, year = {2016}, subject = {Mikroelektrode}, language = {en} } @inproceedings{ThielemannRitterLayeretal.2012, author = {Thielemann, Christiane and Ritter, Sylvia and Layer, Paul and Frieß, Johannes and Daus, Andreas and Steger, Frederik and Heselich, Anja}, title = {IBER10 - Cellular effects of space radiation with relevance to cardiovascular diseases}, series = {European Space Agency - Life in Space for Life on Earth}, booktitle = {European Space Agency - Life in Space for Life on Earth}, address = {Aberdeen, Schottland, UK}, year = {2012}, subject = {Kosmische Strahlung}, language = {en} } @inproceedings{CibaBahmerThielemann2017, author = {Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, title = {Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays}, series = {BMC Neuroscience}, volume = {2017}, booktitle = {BMC Neuroscience}, number = {18}, doi = {10.1186/s12868-017-0372-1}, pages = {269}, year = {2017}, subject = {Mikroelektrode}, language = {en} } @techreport{StegerRitterDausetal.2011, author = {Steger, Frederik and Ritter, Sylvia and Daus, Andreas and Frieß, Johannes and Thielemann, Christiane}, title = {Analysis of electrophysiological characteristics of cardiomyocytes following radiation exposure}, organization = {GSI Helmholtzzentrum f{\"u}r Schwerionenforschung}, year = {2011}, subject = {Herzmuskelzelle}, language = {en} } @inproceedings{NickHeinleinSchneideretal.2012, author = {Nick, Christoph and Heinlein, Thorsten and Schneider, J{\"o}rg and Thielemann, Christiane}, title = {3D Carbon Nanotube Electrodes for Sensing of Neural Activity}, series = {Elsevier World Congress on Biosensors}, booktitle = {Elsevier World Congress on Biosensors}, address = {Cancun, Mexiko}, year = {2012}, subject = {Kohlenstoff-Nanor{\"o}hre}, language = {en} } @inproceedings{KoehlerWoelfelBochtleretal.2016, author = {K{\"o}hler, Tim and W{\"o}lfel, Maximilian and Bochtler, Ulrich and Thielemann, Christiane}, title = {TETRA specific long-term exposure of neuronal in vitro networks}, series = {BioEM2016}, booktitle = {BioEM2016}, year = {2016}, subject = {Neuronales Netz}, language = {en} } @article{ThielemannKrstićJuettneretal.2023, author = {Thielemann, Christiane and Krstić, Nenad and J{\"u}ttner, Jens and Giegerich, Lars and Mayer, Margot and Knuth, Monika and M{\"u}ller, Achim}, title = {3D printed biosensor for continuous glucose measurement in cell cultures}, series = {Annals of 3D Printed Medicine}, volume = {2023}, journal = {Annals of 3D Printed Medicine}, number = {-}, doi = {https://doi.org/10.1016/j.stlm.2023.100111}, pages = {- -- -}, year = {2023}, abstract = {A novel 3D-printed glucose sensor is presented for cell culture application. Glucose sensing was performed using a fluorescence resonance energy transfer (FRET)-based assay principle based on ConA and dextran. Both molecules are encapsulated in alginate microspheres and embedded in the UV-curable, stable hydrogel polyvinyl alcohol (PVA). The rheology of the formulation was adapted to obtain good properties for an extrusion-based printing process. The printed sensor structures were tested for their ability to detect glucose in vitro. A proportional increase in fluorescence intensity was observed in a concentration range of 0 - 2 g/L glucose. Tests with HEK cell cultures also showed good cell compatibility and excellent adhesion properties on plasma-treated Petri dishes. The printed sensors were able to detect the glucose decay associated with the metabolic activities of the fast-growing HEK cells in the cell culture medium over ten days. The proof-of-principle study shows that metabolic processes in cell cultures can be monitored with the new printed sensor using a standard fluorescence wide-field microscope.}, subject = {Biosensor}, language = {en} } @techreport{HelmFrankFriessetal.2014, author = {Helm, Alexander and Frank, Simon and Frieß, Johannes and Thielemann, Christiane and Durante, Marco and Ritter, Sylvia}, title = {Electrophysiological response of mouse embryonic stem cell-derived cardiomyocytes after X-ray and C-ion exposure}, series = {GSI SCIENTIFIC REPORT 2014}, journal = {GSI SCIENTIFIC REPORT 2014}, doi = {10.15120/GR-2015-1-APPA-HEALTH-21}, year = {2014}, subject = {Embryonale Stammzelle}, language = {en} } @techreport{FriessHeselichRitteretal.2014, author = {Frieß, Johannes and Heselich, Anja and Ritter, Sylvia and Layer, Paul and Thielemann, Christiane}, title = {Combined effects of ionizing radiation and cardio-active drugs on human iPSC-derived cardiomyocytes}, series = {GSI SCIENTIFIC REPORT 2014}, journal = {GSI SCIENTIFIC REPORT 2014}, doi = {10.15120/GR-2015-1-APPA-HEALTH-37}, year = {2014}, subject = {Herzmuskelzelle}, language = {en} } @techreport{MayerArrizabalagaRitteretal.2015, author = {Mayer, Margot and Arrizabalaga, Onetsine and Ritter, Sylvia and Thielemann, Christiane}, title = {Human embryonic stem cell derived neurospheres form functional networks on microelectrode arrays}, series = {GSI Scientific Report 2015}, volume = {2015}, journal = {GSI Scientific Report 2015}, number = {GSI Report 2016-1}, doi = {10.15120/GR-2016-1}, pages = {205 -- 205}, year = {2015}, subject = {Embryonale Stammzelle}, language = {en} } @article{CibaPetzoldAlvesetal.2025, author = {Ciba, Manuel and Petzold, Marc and Alves, Caroline L. and Rodrigues, Francisco A. and Jimbo, Yasuhiko and Thielemann, Christiane}, title = {Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data}, series = {Scientific Reports}, volume = {15}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-025-99479-7}, year = {2025}, abstract = {Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA \$\$_A\$\$ receptor antagonist known to induce hypersynchrony, demonstrated the workflow's ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90\%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline's effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.}, subject = {Maschinelles Lernen}, language = {en} } @article{SallumAlvesdeOToutainetal.2025, author = {Sallum, Loriz Francisco and Alves, Caroline L. and de O Toutain, Thaise Graziele L and Porto, Joel Augusto Moura and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {Revealing patterns in major depressive disorder with machine learning and networks}, series = {Chaos, Solitons \& Fractals}, volume = {194}, journal = {Chaos, Solitons \& Fractals}, publisher = {Elsevier BV}, issn = {0960-0779}, doi = {https://doi.org/10.1016/j.chaos.2025.116163}, year = {2025}, abstract = {Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.}, subject = {Depression}, language = {en} } @article{AlvesCibadeOToutainetal.2024, author = {Alves, Caroline L. and Ciba, Manuel and de O Toutain, Thaise Graziele L and Moura Porto, Joel Augusto and de Sena, Eduardo Pond{\´e} and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments}, series = {Journal of Physics: Complexity}, volume = {2024}, journal = {Journal of Physics: Complexity}, number = {1}, doi = {10.1088/2632-072X/ad1c68}, pages = {1 -- 2}, year = {2024}, abstract = {There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89\%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.}, subject = {Neuronales Netz}, language = {en} } @article{AlvesRodriguesSallumetal.2024, author = {Alves, Caroline L. and Rodrigues, Francisco A. and Sallum, Loriz Francisco and Toutain, Thaise and Porto, Joel and Aguiar, Patricia and Thielemann, Christiane and M{\"o}ckel, Michael}, title = {Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis}, series = {plos one}, volume = {2024}, journal = {plos one}, number = {19(10)}, doi = {https://doi.org/10.1371/journal.pone.0305630}, pages = {1 -- 35}, year = {2024}, abstract = {Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98\%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML c}, subject = {Aufmerksamkeitsdefizit-Syndrom}, language = {en} } @unpublished{SchickelBenderKaysanetal.2025, author = {Schickel, Esther and Bender, Tamara and Kaysan, Leon and Hufgard, Simone and Mayer, Margot and Grosshans, David R. and Thielemann, Christiane and Schroeder, Insa S.}, title = {Human cerebral organoids model tumor infiltration and migration supported by astrocytes in an autologous setting}, series = {bioRxiv}, volume = {2025}, journal = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, doi = {https://doi.org/10.1101/2025.01.29.635456}, pages = {43}, year = {2025}, abstract = {SummaryEfforts to achieve precise and efficient tumor targeting of highly malignant brain tumors are constrained by the dearth of appropriate models to study the effects and potential side effects of radiation, chemotherapy, and immunotherapy on the most complex human organ, the brain. We established a cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC as one of the most common oncogenes in brain tumors. GFP+/c-MYChighcells were isolated from tumor organoids and used in two different culture approaches: assembloids comprising of a normal cerebral organoid with a GFP+/c-MYChightumor sphere and co-culture of cerebral organoid slices at air-liquid interface with GFP+/c-MYChighcells. GFP+/c-MYChighcells used in both approaches exhibited tumor-like properties, including overexpression of the c-MYC oncogene, high proliferative and invasive potential, and an immature phenotype as evidenced by increased expression of Ki-67, VIM, and CD133. Organoids and organoid slices served as suitable scaffolds for infiltrating tumor-like cells. Using our highly reproducible and powerful model system that allows long-term culture, we demonstrated that the migratory and infiltrative potential of tumor-like cells is shaped by the environment in which glia cells provide support to tumor-like cells.}, subject = {Hirntumor}, language = {en} } @article{SchickelBenderKaysanetal.2025, author = {Schickel, Esther and Bender, Tamara and Kaysan, Leon and Hufgard, Simone and Mayer, Margot and Grosshans, David R. and Thielemann, Christiane and Schroeder, Insa S.}, title = {Human cerebral organoids model tumor initiation and infiltration in an autologous astrocyte-supported setting}, series = {iScience}, journal = {iScience}, publisher = {Elsevier BV}, issn = {2589-0042}, doi = {https://doi.org/10.1016/j.isci.2025.113334}, year = {2025}, abstract = {Efforts to efficiently target brain tumors are constrained by the dearth of appropriate models to study tumor behavior towards treatment approaches as well as potential side effects to the surrounding normal tissue. We established a reproducible cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC, a common oncogene in brain tumors. GFP+/c-MYChigh cells were isolated from tumor organoids and used in two different approaches: GFP+/c-MYChigh cells co-cultured with cerebral organoid slices or fused as spheres to whole organoids. GFP+/c-MYChigh cells used in both approaches exhibited tumor-like properties, including an immature phenotype and a highly proliferative and invasive potential. We demonstrate that the latter is influenced by astrocytes supporting the GFP+/c-MYChigh cells while X-ray irradiation significantly kills and impairs tissue infiltration of GFP+/c-MYChigh cells. In summary, the model represents major features of tumorous and adjacent normal tissue and may be used to evaluate appropriate cancer treatments.}, subject = {Hirntumor}, language = {en} } @inproceedings{HufgardMayerSchroederetal.2025, author = {Hufgard, Simone and Mayer, Margot and Schroeder, Insa S. and Thielemann, Christiane}, title = {3D compartmentilisation for analyzing functional long-range connectivity between brain regions}, series = {4th Microphysiological Systems World Summit, Brussels, Belgium}, booktitle = {4th Microphysiological Systems World Summit, Brussels, Belgium}, number = {June 2025}, year = {2025}, subject = {Hirnareal}, language = {en} } @unpublished{AlvesPaulodeFariaetal.2025, author = {Alves, Caroline and Paulo, Artur Jos{\´e} Marques and de Faria, Danilo Donizete and Sato, Jo{\~a}o Ricardo and Borges, Vanderci and Silva, Sonia de Azevedo and Ferraz, Henrique Ballalai and Rodrigues, Francisco A. and Thielemann, Christiane and Moeckel, Michael and Aguiar, Patricia de Carvalho}, title = {Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning}, publisher = {Springer Science and Business Media LLC}, doi = {https://doi.org/10.21203/rs.3.rs-7483388/v1}, year = {2025}, abstract = {Dystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100\%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces.}, subject = {Dystonie}, language = {en} } @article{FlachsEmmerichRothetal.2019, author = {Flachs, Dennis and Emmerich, Florian and Roth, Gian-Luca and Hellmann, Ralf and Thielemann, Christiane}, title = {Laser-bonding of FEP/FEP interfaces for a flexiblemanufacturing process of ferroelectrets}, series = {Journal of Physics: Conference Series}, volume = {1407}, journal = {Journal of Physics: Conference Series}, number = {012107}, doi = {10.1088/1742-6596/1407/1/012107}, pages = {1 -- 5}, year = {2019}, abstract = {This paper presents an optimized laser-bonding process for piezoelectric energy-harvesters based on thin fluorinated-ethylene-propylene (FEP) foils, using an ultra-short-pulse(USP) laser. Due to the minimized thermal stress in the material during bonding, achieved bypulse durations of few picoseconds, we created seams down to 40μm width without generatingholes in the 12.5μm thick FEP-foils. Using a galvanometer scanning system allowed for fastbonding-speed up to several centimeters per second, making the process also suitable for largestructures and areas. The achieved bond strength of the seams under influence of shearingstress was examined using tensile testing, which showed a sufficient strength of about 25 \% of the maximum strength of an unbonded, single layer of FEP.}, subject = {Ultrakurzzeitlaser}, language = {en} } @article{FlachsZhukovZechetal.2024, author = {Flachs, Dennis and Zhukov, Sergey and Zech, Isabella and Schreck, Timo and Belle, Stefan and Seggern, Heinz von and Kupnik, Mario and Altmann, Alexander Anton and Thielemann, Christiane}, title = {Enzymatic Self-Degradable PLA-Based Electrets}, series = {Journal of Polymers and the Environment}, volume = {2024}, journal = {Journal of Polymers and the Environment}, number = {1}, doi = {Journal of https://doi.org/10.1007/s10924-024-03240-6}, pages = {1 -- 11}, year = {2024}, abstract = {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.}, subject = {Biologisch abbaubarer Kunststoff}, language = {en} } @article{FlachsEmmerichThielemann2022, author = {Flachs, Dennis and Emmerich, Florian and Thielemann, Christiane}, title = {Fabrication process for FEP piezoelectrets based on photolithographically structured thermoforming templates}, series = {Microsystem Technologies}, volume = {2022}, journal = {Microsystem Technologies}, number = {November}, doi = {doi.org/10.1007/s00542-022-05405-6}, pages = {0 -- 0}, year = {2022}, abstract = {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 fabri� cated 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.}, subject = {Mikrosystemtechnik}, language = {en} } @inproceedings{AltmannSuppeltRuhletal.2024, author = {Altmann, Alexander Anton and Suppelt, Sven and Ruhl, Max and Schaumann, Stephan and Latsch, Bastian and Ben Dali, Omar and Zhukov, Sergey and Flachs, Dennis and Zhang, Xiaoqing and Thielemann, Christiane and Seggern, Heinz von and Kupnik, Mario}, title = {Monolithic Wideband Air-Coupled Ultrasonic Transducer Based on Additively Manufactured Ferroelectrets}, series = {2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)}, booktitle = {2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)}, publisher = {IEEE}, doi = {10.1109/UFFC-JS60046.2024.10793858}, pages = {1 -- 4}, year = {2024}, abstract = {Air-coupled ultrasonic transducers are widely used in non-destructive testing, acoustical sonar systems, and biomedical imaging. These applications require transducers that operate effectively across a broad acoustic frequency spectrum, offer adaptable geometric designs, and increasingly incorporate eco-friendly materials. In this work, we present a monolithic, 3D-printed air-coupled ultrasonic transducer based on ferroelectrets (FEs) and fabricated from biocompatible polylactic acid (PLA). We evaluated the transducer's acoustical performance by measuring the surface velocity of its active area using laser Doppler vibrometry and assessed its robustness during continuous operation over a 19-day period. Additionally, we measured the sound pressure level (SPL) and wideband characteristics in an anechoic chamber across excitation frequencies from 1kHz to 100kHz. At a resonance frequency of 33kHz, our transducer achieved an SPL of 94.3dB and surface velocities up to 37mm/s. The measured bandwidth of 65.2kHz at the -6dB threshold corresponds to a fractional bandwidth of 189\%. The observed exponential decay of the surface velocity, stabilizing at 15\% of its initial amplitude, aligns with the isothermal surface potential decay typically observed in FE films made from PLA. These results demonstrate the effectiveness of the transducer, which features an adaptable backplate for tuning acoustic properties. The low-cost transducer, manufactured from biocompatible PLA, is particularly suited for imaging and biomedical applications furthering green electronics.}, subject = {Ultraschallwandler}, language = {en} } @inproceedings{FlachsBernhardThielemann2024, author = {Flachs, Dennis and Bernhard, Levin and Thielemann, Christiane}, title = {Fully 3D-Printed Rotational Energy Harvester Based On Bipolar Charged PLA Electrets}, series = {Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)}, volume = {2024}, booktitle = {Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)}, publisher = {IEEE}, isbn = {979-8-3503-8020-0}, doi = {979-8-3503-8020-0}, year = {2024}, abstract = {Rotational energy harvesters have emerged as a promising solution for sustainable power generation in a variety of applications, ranging from small-scale devices to large-scale industrial systems. In this work, we present a fully 3D-printed electret rotational harvester based on the biodegradable and compostable polymer polylactic acid (PLA). The 3D-printed harvester consists of a rotor made from a bipolar-charged PLA electret, electrode pairs of conductive PLA that function as the stator, and a PLA bearing. An output power of 61μ W was achieved at a rotational speed of 400 rpm and a load resistance of 28.2MΩ. To assess the durability of the harvester, particularly the 3D-printed bearing, the extent of mechanical wear was examined after one million rotations at a rotational speed of 400 rpm. The findings indicate that the rollers undergo a 1.2\% loss in mass, yet the functionality remains intact. The charge decay of PLA electrets is influenced by external factors such as elevated humidity and temperature. Future research will focus on improving the charge stability of PLA by addressing these factors, expanding the potential applications of this rotational energy harvester.}, subject = {Elektret}, language = {en} } @article{FlachsEtzelMayeretal.2022, author = {Flachs, Dennis and Etzel, Johannes and Mayer, Margot and Harbecke, Frederic and Belle, Stefan and Rickmeyer, Tim and Thielemann, Christiane}, title = {Characterization of electrically conductive, printable ink based on alginate hydrogel and graphene nanoplatelets}, series = {Biomedical Engineering Advances}, volume = {2022}, journal = {Biomedical Engineering Advances}, number = {4}, doi = {https://doi.org/10.1016/j.bea.2022.100045}, pages = {1 -- 2}, year = {2022}, abstract = {In recent years, there has been an increasing interest in electrically conductive hydrogels for a wide range of biomedical applications, like tissue engineering or biosensors. In this study, we present a cost-effective conductive hydrogel based on alginate and graphene nanoplatelets for extrusion-based bioprinters. The hydrogel is prepared under ambient conditions avoiding high temperatures detrimental for cell culture environments. Investigation of the hydrogel revealed a conductivity of up to 7.5 S/cm, depending on the ratio of platelets. Furthermore, in vitro tests with human embyronic kidney cells - as an example cell type - showed good adhesion of the cells to the surface of the conductive hydrogel. Electrochemical measurements revealed a low electrode impedance which is desirable for the extracellular recording, but also low electrode capacitance, which is unfavorable for electrical stimulation purposes. Therefore, future experiments with the graphene nanoplatelets-based hydrogels will focus on electrodes for biosensors and extracellular recordings of neurons or cardiac myocytes.}, subject = {Hydrogel}, language = {en} } @article{FlachsKoehlerThielemann2018, author = {Flachs, Dennis and K{\"o}hler, Tim and Thielemann, Christiane}, title = {Transparent poly(3,4-ethylenedioxythiophene)-based microelectrodes for extracellular recording}, series = {Biointerphases}, volume = {2018}, journal = {Biointerphases}, number = {13:041008}, doi = {10.1116/1.5041957}, pages = {1 -- 1}, year = {2018}, abstract = {It is well known that at the interface between neuronal tissue and recording electrode low electrical impedance is required. However, if simultaneous optical detection or stimulation is an issue, good optical transmittance of the electrode material is desirable as well. State-of-the-art titanium nitride electrodes provide superior low impedance compared to gold or iridium, but are nontransparent. Transparent electrode materials like the transparent conducting oxide, indium tin oxide (ITO), or graphene offer high light transmittance (>80\%) but reveal relatively high impedance. In this paper, the authors propose the conducting polymer poly(3,4-ethylenedioxythiophene) with the counter ion NO-3 as the electrode material for low impedance and good optical transmittance properties. The polymer is electrochemically deposited onto ITO improving the relatively high impedance of ITO. This multilayer electrode allows not only for electrophysiological recordings of cardiomyocytes but also for monitoring of cell contraction under the microscope. Electrochemical impedance spectroscopy and action potential recordings reveal that the new transparent electrodes are a good compromise in terms of low impedance and transparency if deposition parameters are optimized.}, subject = {Impedanzspektroskopie}, language = {en} } @article{HoelterRickmeyerThielemann2025, author = {H{\"o}lter, Jan and Rickmeyer, Tim and Thielemann, Christiane}, title = {CellRex: Software platform for managing biological cell data}, series = {SoftwareX}, volume = {31}, journal = {SoftwareX}, publisher = {Elsevier BV}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2025.102284}, year = {2025}, abstract = {This work introduces the software platform CellRex, a research data management system for laboratories capable of storing, searching, and enriching data with biological metadata. CellRex addresses data management challenges by storing data in an ontology-based directory structure within the filesystem, with metadata saved as JSON files and in a document-oriented SQLite database. The framework, deployed as container services in a software-as-a-service model, features a web-based GUI and API for user interaction and machine-readable access, providing functionalities such as duplicate detection, experiment grouping, and templating. CellRex improves research efficiency and facilitates data reuse, providing a targeted solution for laboratories focused on cell analysis research.}, subject = {Forschungsdaten}, language = {en} } @article{AltmannSuppeltWuestetal.2025, author = {Altmann, Alexander Anton and Suppelt, Sven and W{\"u}st, Philipp and D{\"o}rsam, Jan Helge and Latsch, Bastian and Flachs, Dennis and Blaeser, Andreas and Zhang, Xiaoqing and Thielemann, Christiane and Seggern, Heinz von and Kupnik, Mario}, title = {Self-supporting mid-air 3D printing of single-layer polypropylene structures: Flow rate-dependent analytical modeling and surface characterization}, series = {Additive Manufacturing}, volume = {113}, journal = {Additive Manufacturing}, publisher = {Elsevier BV}, issn = {2214-8604}, doi = {https://doi.org/10.1016/j.addma.2025.105010}, year = {2025}, abstract = {High-precision deposition in material extrusion-based additive manufacturing (MEX-AM), especially in mid-air extrusion, is essential for creating microfluidic channels, integrated sensors, and tissue scaffolds with fine vascular features. However, despite advances in support-free slicing and multi-axis printing, no standardized method exists for fabricating enclosed air cavities with high shape fidelity, particularly in single-layer structures. Reliable deposition at sub-millimeter scales remains challenging, especially when printing low-stiffness polymers such as polypropylene (PP), due to delayed solidification and viscoelastic effects. This study introduces a flow-rate-dependent analytical model to predict extrusion behavior, interfacial bonding, and surface morphology in self-supporting single-layer PP structures. To validate the model, surface roughness, material distribution, and intralayer bonding were quantified using spectral analysis, cross-sectional thickness measurements, and Abbott-Firestone curve evaluation. Results show that reducing the flow rate (relative to 100\% nominal flow through a 0.4 mm nozzle) to 40\% improves surface roughness (Ra) to below 20 m on average across all surfaces, with the lowest Ra of 4.52 ± 0.49 m observed at a 20\% flow rate on the top surfaces enclosing the cavity. The intralayer bonding between adjacent extruded lines increases up to 70\%, improving deposition uniformity. Nozzle-induced smoothing effects enhance surface quality at flow rates of 60\% or lower, while higher flow rates result in increased surface waviness and geometric irregularities. These findings enable the fabrication of enclosed microfluidic channels and functional cavities with well-defined surfaces and reduced mechanical stiffness, suitable for applications involving sensing, controlled deformation, or flexible system integration.}, subject = {Fertigungstechnik}, language = {en} } @article{AlvesToutainPortoetal.2023, author = {Alves, Caroline L. and Toutain, Thaise and Porto, Joel and Aguiar, Patricia and de Sena, Eduardo Pond{\´e} and Rodrigues, Francisco A. and Pineda, Aruane M. and Thielemann, Christiane}, title = {Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia}, series = {Journal of Neural Engineering}, volume = {2023}, journal = {Journal of Neural Engineering}, number = {20/5}, doi = {10.1088/1741-2552/acf734}, pages = {0 -- 0}, year = {2023}, abstract = {Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100\% and an accuracy of 98.5\% for the fMRI, an AUC of 95\%, and an accuracy of 95.4\% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.}, subject = {Maschinelles Lernen}, language = {en} }