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An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLSAA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.
A Robust Interface for Head Motion based Control of a Robot Arm using MARG and Visual Sensors
(2018)
Head-controlled human machine interfaces have gained popularity over the past years, especially in the restoration of the autonomy of severely disabled people, like tetraplegics. These interfaces need to be reliable and robust regarding the environmental conditions to guarantee safety of the user and enable a direct interaction between a human and a machine. This paper presents a hybrid MARG and visual sensor system for head orientation estimation which is in this case used to teleoperate a robotic arm. The system contains a Magnetic Angular Rate Gravity (MARG)-sensor and a Tobii eye tracker 4C. A MARG sensor consists of tri-axis accelerometer, gyroscope as well as a magnetometer which enable a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The tri-axis magnetometer is sensitive to external magnetic fields which result in incorrect orientation estimation from the sensor fusion process. In this work the Tobii eye tracker 4C is used to increase head orientation estimation because it also features head tracking even though it is commonly used for eye tracking. This type of visual sensor does not suffer magnetic drift. However, it computes orientation data only, if a user is detectable. Within this work a state machine is presented which enables data fusion of the MARG and visual sensor to improve orientation estimation. The fusion of the orientation data of MARG and visual sensors enables a robust interface, which is immune against external magnetic fields. Therefore, it increases the safety of the human machine interaction.
In this experimental work we present a novel electrolyzer system for the production of hydrogen and oxygen at high pressure levels without an additional mechanical compressor. Due to its control strategies, the operation conditions for this electrolyzer can be kept optimal for each load situation of the system. Furthermore, the novel system design allows for dynamic long-term operation as well as for easy maintainability. Therefore, the device meets the requirements for prospective power-to-gas applications, especially, in order to store excess energy from renewable sources. A laboratory scale device has been developed and high-pressure operation was validated. We also studied the long-term stability of the system by applying dynamic load cycles with load changes every 30 sec. After 80 h of operation the used membrane electrode assembly (MEA) was investigated by means of SEM, EDX and XRD analysis.
Performance enhancing study for large scale PEM electrolyzer cells based on hydraulic compression
(2017)
A compact and efficient PEM electrolyser stack design based on hydraulic single cell compression
(2019)
Purpose
Although courage has generally been understood as a powerful virtue, research to establish it as a psychological construct is in its infancy. We examined courage in organizations against the backdrop of positive psychology with a design in the Grounded Theory tradition that connects Positive Organizational Behavior and Positive Organizational Scholarship.
Method
The sample consists of organizations that define courage in their mission statement and organizations without such a definition. It includes employees and executives, exploring workplace courage on the macro as well as the micro level. Eleven organizations and 23 participants contributed to the interview study.
Results
Applying Glaser's theoretical coding, specifically the C-family, we propose that courage arises from a decisional conflict in three major domains: the self, social interaction, and performance. It is located on a continuum between apathy and foolhardiness and can take on reactive, proactive, or autonomous forms. Whether and to what extent courage manifests, is a dynamic process contingent upon organizational structure, culture, and communication climate as well as individual cognitiveaffective personality systems.
Limitations
The model depicts the complexity of the phenomenon, rather than details of its individual components. It goes beyond pre-defined categories and prevailing definitions.
Implications
Modern organizations are characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).
Courage is crucial in such an environment and can be systematically fostered across the whole human
resource management cycle.
Value
The study advances theory building on courage in the workplace and highlights its potential to be
measured, developed and managed for more effective work performance.
Improved Plasma Membrane Models as Test Systems for the Membrane
Disrupting Activity of Kalata B1
(2017)
Steps Towards an Open All-in-one Rich-Client Environment for Particle-Based Mesoscopic Simulation
(2018)
Web advertisements are the primary financial source for many online services, but also for cybercriminals. Successful ad campaigns rely on good online profiles of their potential customers. The financial potentials of displaying ads have led to the rise of malware that injects or replaces ads on websites, in particular, so-called adware. This development leads to always further optimized and customized advertising. For these customization's, various tracking methods are used. However, only sparse work has gone into privacy issues emerging from adware. In this paper, we investigate the tracking capabilities and related privacy implications of adware and potentially unwanted programs (PUPs). Therefore, we developed a framework that allows us to analyze any network communication of the Firefox browser on the application level to circumvent encryption like TLS. We use this to dynamically analyze the communication streams of over 16,000 adware or potentially unwanted programs samples that tamper with the users' browser session. Our results indicate that roughly 37% of the requests issued by the analyzed samples contain private information and are accordingly able to track users. Additionally, we analyze which tracking techniques and services are used.
Renewable and sustainable energy production by many small and distributed producers is revolutionizing the energy landscape as we know it. Consumers produce energy, making them to prosumers in the smart grid. The interaction between prosumers and other entities in the grid and the optimal utilization of new smart grid components (electric cars, freezers, solar panels, etc.) are crucial for the success of the smart grid. The Power Trading Agent Competition is an open simulation platform that allows researchers to conduct low risk studies in this new energy market. In this work we present Maxon16, an autonomous energy broker and champion of the 2016's Power Trading Agent Competition. We present the strategies the broker used in the final round and evaluate the effectiveness of the strategies by analyzing the tournament's results.
This technical report is about the architecture and integration of commercial UAVs in Search and Rescue missions. We describe a framework that consists of heterogeneous UAVs, a UAV task planner, a bridge to the UAVs, an intelligent image hub, and a 3D point cloud generator. A first version of the framework was developed and tested in several training missions in the EU project TRADR.
This technical report is about the mission and the experience gained during the reconnaissance of an industrial hall with hazardous substances after a major fire in Berlin. During this operation, only UAVs and cameras were used to obtain information about the site and the building. First, a geo-referenced 3D model of the building was created in order to plan the entry into the hall. Subsequently, the UAVs were used to fly in the heavily damaged interior and take pictures from inside of the hall. A 360° camera mounted under the UAV was used to collect images of the surrounding area especially from sections that were difficult to fly into. Since the collected data set contained similar images as well as blurred images, it was cleaned from non-optimal images using visual SLAM, bundle adjustment and blur detection so that a 3D model and overviews could be calculated. It was shown that the emergency services were not able to extract the necessary information from the 3D model. Therefore, an interactive panorama viewer with links to other 360° images was implemented where the links to the other images depends on the semi dense point cloud and located camera positions of the visual SLAM algorithm so that the emergency forces could view the surroundings.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.
In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.
Advanced Determination of Temperature Coefficients of Photovoltaic Modules by Field Measurements
(2023)
In this work data from outdoor measurements, acquired over the course of up to three years on commercially available solar panels, is used to determine the temperature coefficients and compare these to the information as stated by the producer in the data sheets. A program developed in MatLab App Designer allows to import the electrical and ambient measurement data. Filter algorithms for solar irradiance narrow the irradiance level down to ~1000 W/m2 before linear regression methods are applied to obtain the temperature coefficients. A repeatability investigation proves the accuracy of the determined temperature coefficients which are in good agreement to the supplier specification if the specified values for power are not larger than -0.3%/K. Further optimization is achieved by applying wind filter techniques and days with clear sky condition. With the big (measurement) data on hand it was possible to determine the change of the temperature coefficients for varying irradiance. As stated in literature we see an increase of the temperature coefficient of voltage and a decline for the temperature coefficient of power with increasing irradiance.
Upgrade of Bioreactor System Providing Physiological Stimuli
to Engineered Musculoskeletal Tissues
(2017)
A novel central control interface (CCI) is developed to improve the modular bioreactor system with regard to extendability and modifiability in Tissue Engineering (TE) applications. This paper presents the results developed in the project with open-source hardware and the graphical programming system LabVIEW. A new platform independent User Interface was further developed to contribute to the new flexibility of the device.
An energy economy with high share of renewable but volatile energy sources is dependent on storage strategies in order to ensure sufficient energy delivery in periods of e.g. low wind and/or low solar radiation. Hydrogen as environmental friendly energy carrier is thought to be an appropriate solution for large scale energy storage. In 2011 the NOW (national organisation for hydrogen in Germany) calculated the demand for hydrogen energy systems as positive (0.8 GW to 5.25 GW) and negative supply for varying power demand (0.68 to 4.3 GW) for the German energy economy in 2025. Due to its dynamic behaviour on load changes polymer electrolyte membrane fuel cells (PEMFC) as well as water electrolyser systems (PEMEL) can play a significant role for large scale hydrogen based storage systems. In this work a novel design concept for modular fuel cell and electrolyser stacks is presented with single cells in pockets surrounded by a hydraulic medium. This hydraulic medium introduces necessary compression forces on the membrane electrode assembly (MEA) of each cell within a stack. Furthermore, ideal stack cooling is achieved by this medium. Due to its modularity and scalability the modular stack design with hydraulic compression meets the requirements for large PEMFC as well as PEMEL units. Small scale prototypes presented in this work illustrate the potential of this design concept.
To further increase platinum utilisation in PEM fuel cells CNFs are investigated as catalyst support material due to the CNF’s high specific surface area. Furthermore, CNFs provide suitable properties concerning corrosion resistance as well as electrical conductivity in contrast to conventional carbon supports.
This work presents the results of an electrode preparation procedure based on O2 plasma activated CNFs. The plasma treatment leads to CNF dispersibility in alcohol/water for a spray coating process. Furthermore, O2 plasma activation enhances metal deposition on the CNF’s surface. Pulse plating procedure as well as wet chemical metal synthesis have been used for particle deposition. For pulse plating a potentiostat/galvanostat type MMates 510 AC from Materials Mates, Italy has been used. Electrode morphology has been determined in SEM type XL 30 ESEM from Philips, The Netherlands.
In polymer electrolyte membrane fuel cells (PEMFC) noble metal nano particles are deposited on graphitic supports serving as electrocatalysts for devices with high power density. In this study anodes are analysed with low platinum loading of about 0.1 mg cm-2. These electrodes are prepared by carbon nano fibres (CNF) decorated with platinum nano particles. For electrode manufacturing two sorts of fibres, which are produced in an industrial scale, are used with different graphitisation degree and surface area. CNF layers are applied on commercially available graphitic substrate by spray coating which leads to a porous structure with high surface area. Subsequently, platinum deposition is achieved by pulsed electroplating for an improved platinum utilisation in PEMFC electrodes. Spray coating and platinum deposition are assisted by a previous oxygen plasma activation process. Prepared anode material is characterised by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction spectroscopy (XRD), X-ray fluorescence spectroscopy (XRF) and thermogravimetry (TGA). Electrochemical analyses (cyclic voltammetry and corrosion test) are carried out in 0.5 M sulphuric acid. The effect of graphitisation degree of carbon nano fibres on the performance of prepared electrodes is investigated in-situ in a PEM fuel cell test bench.
In state of the art polymer electrolyte membrane fuel cells (PEMFC) rare and expensive platinum group metals (PGM) are used as catalyst material. Reduction of PGM in PEMFC electrodes is strongly required to reach cost targets for this technology. An optimal catalyst utilisation is achieved in the case of nano-structured particles supported on carbon material with a large specific surface area. In this study, graphitic material in form of carbon nanofibres (CNFs) is decorated with platinum (Pt) particles serving as catalyst material for PEMFC electrodes with low Pt loading. For electrode preparation CNFs have been previously activated by means of radio frequency induced oxygen plasma. This kind of treatment results in formation of functional groups on the CNF’s surface which directly influences the characteristics of subsequent Pt particle deposition. Different plasma parameters (plasma power, gas flow or exposure time) have to be set in order to achieve formation of oxygen containing functional groups (hydroxylic, carboxylic or carbonylic) on the CNF’s surface. In the frame of this experimental work, electrodes are investigated in respect of optimal morphology, microstructure as well as electrochemical properties. Therefore, samples were characterised by means of scanning electron microscopy combined with energy dispersive X-ray analysis, transmission electron microscopy, thermogravimetry, X-ray diffraction, X-ray fluorescence as well as polarisation measurements.
For this study gas diffusion electrodes (GDE) with low platinum loading are prepared for the application as anode in polymer electrolyte membrane fuel cell (PEMFC) systems based on hydraulic compression. As catalyst support material, carbon nanofibers (CNF) are investigated because of their high specific surface area and high graphitization degree. The electrode preparation is optimized by an economic and environmental friendly pre-treatment process in oxygen plasma. For GDE manufacture an ink containing oxygen plasma activated CNFs as well as hydrophilic polymer is used. After spray coating of this CNF ink on a graphitic substrate, platinum is deposited using the pulse plating technique. Preliminary results showed a considerable improvement of CNF dispersibility as well as an increased amount and an optimized morphology of the deposited platinum. Morphology and microstructure are observed by scanning electron microscopy as well as transmission electron microscopy. Platinum loading is determined by thermogravimetric analysis to be in the range of 0.01 mg cm-2 to 0.017 mg cm-2. Furthermore, MEAs are prepared from these GDEs and testing is performed in a novel modular fuel cell test stack based on hydraulic compression. Technical information about stack design and functions is given in this work.
The membrane electrode assemblies (MEA) for polymer electrolyte membrane fuel cells (PEMFC) developed at the Westphalian Energy Institute are based on oxygen plasma activated carbon nanotubes (CNT) doped with platinum particles. For electrode preparation an ink is used containing the activated CNTs as well as hydrophobic and hydrophilic material in solved form. After this ink is sprayed onto a graphitic substrate platinum particles are deposited by pulse plating method, where the plasma activation enhances CNT dispersibility as well as platinum deposition. This materials mixture is structured in nanoscale with the aim to increase the catalyst particles’ specific surface. For low reactance at operation, homogeneous compression of the MEA’s layers is necessary within a PEMFC. A novel stack architecture for electrochemical cells, especially PEMFC as well as PEM electrolysers, has been developed in order to achieve ideal cell operation conditions. Single cells of such a stack are inserted into flexible slots that are surrounded by a hydraulic medium which is pressurised during operation in order to achieve an even compression and cooling of the stack’s cells. With this stack design it has been possible to construct a test facility for simultaneous characterisation of several MEA samples. As compression and temperature conditions of every single sample are the same, the effects of e.g. different electrode configurations can be investigated with the novel test system.
Since the 1980’s, against the backdrop of global warming and the decline of conventional energy resources, low emission and renewable energy systems have gotten into the focus of politics as well as research and development. In order to decrease the emission of greenhouse gases Germany intents to generate 80% of its electrical energy from renewable and low emission sources by 2050. For low emission electricity generation hydrogen operated fuel cells are a potential solution. However, although fuel cell technology has been well known since the 19th century cost effective materials are needed to achieve a breakthrough in the market.
Proton Exchange Membrane Fuel Cells with Carbon Nanotubes as Electrode Material
At the Westphalian Energy Institute of the Wesphalian University of Applied Sciences one main focus is on the research of proton exchange membrane fuel cells (PEMFC). PEMFC membrane electrode assemblies (MEA) consist of a polymer membrane with electrolytic properties covered on both sides by a catalyst layer (CL) as well as a porous and electrical conductive gas diffusion layer (GDL).
For PEMFC carbon nanotubes (CNT) have ideal properties as electrode material concerning electrical conductivity, oxidation resistance and media transport. CNTs are suitable for the use as catalyst support material within the CL due to their large surface in comparison to conventional carbon supports. Furthermore, oxygen plasma treated CNTs show electrochemical activity referred to hydrogen adsorption and desorption, which has been shown by cyclic voltammetry in 0.5 M sulfuric acid solution. According to the PEMFCs anode a GDL coated with oxygen plasma activated CNTs has promising properties to significantly reduce catalyst content (e.g. platinum) of the anodic CL.
Inspired by the super-human performance of deep learning models in playing the game of Go after being presented with virtually unlimited training data, we looked into areas in chemistry where similar situations could be achieved. Encountering large amounts of training data in chemistry is still rare, so we turned to two areas where realistic training data can be fabricated in large quantities, namely a) the recognition of machine-readable structures from images of chemical diagrams and b) the conversion of IUPAC(-like) names into structures and vice versa. In this talk, we outline the challenges, technical implementation and results of this study.
Optical Chemical Structure Recognition (OCSR): Vast amounts of chemical information remain hidden in the primary literature and have yet to be curated into open-access databases. To automate the process of extracting chemical structures from scientific papers, we developed the DECIMER.ai project. This open-source platform provides an integrated solution for identifying, segmenting, and recognising chemical structure depictions in scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilises a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
DECIMER.ai is data-driven, relying solely on the training data to make accurate predictions without hand-coded rules or assumptions. The latest model was trained with 127 million structures and 483 million depictions (4 different per structure) on Google TPU-V4 VMs
Name to Structure Conversion: The conversion of structures to IUPAC(-like) or systematic names has been solved algorithmically or rule-based in satisfying ways. This fact, on the other side, provided us with an opportunity to generate a name-structure training pair at a very large scale to train a proof-of-concept transformer network and evaluate its performance.
In this work, the largest model was trained using almost one billion SMILES strings. The Lexichem software utility from OpenEye was employed to generate the IUPAC names used in the training process. STOUT V2 was trained on Google TPU-V4 VMs. The model's accuracy was validated through one-to-one string matching, BLEU scores, and Tanimoto similarity calculations. To further verify the model's reliability, every IUPAC name generated by STOUT V2 was analysed for accuracy and retranslated using OPSIN, a widely used open-source software for converting IUPAC names to SMILES. This additional validation step confirmed the high fidelity of STOUT V2's translations.
The DECIMER.ai Project
(2024)
Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats.
To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery.
This experimental work deals with the preparation and investigation of PEM fuel cell electrodes, which are obtained using Graphene Related Material (GRM) serving as catalyst support material for platinum nanoparticles. The applied GRM belong to the group of carbon nanofibers and exhibits a helical-ribbon structure with dimensions of 50 nm in diameter and an average length up to a few µm. Furthermore, utilized GRM provide a superior graphitisation degree of about 100 %, which leads to both high corrosion resistance and low ohmic resistance. Material stability plays one of the main roles for long term fuel cell operation, whereby a great electrical catalyst contact combined with high specific surface area yields in high fuel cell performances.
Prior to GRM dispersion and deposition onto a gas diffusion layer, the graphene structures are functionalized by oxygen plasma treatment. Through this step, functional oxygen groups are generated onto the GRM outer surface providing an improved hydrophilic behaviour and facilitating the GRM suspension preparation. In addition, the oxygen groups act as anchors for platinum nanoparticles which are subsequently deposited onto the GRM surface through a pulse electrodeposition process.
Membrane electrode assemblies produced with the prepared electrodes are investigated in-situ in a PEM fuel cell test bench.
This work deals with the preparation and investigation of PEM fuel cell electrodes, which are obtained using graphene related material (GRM) serving as catalyst support for platinum nanoparticles. Applied GRM are used for the preparation of suspensions in four distinct mixing ratios. Two sorts of GRM have been investigated: carbon nanofibers (CNF) and graphene oxide (GO). Utilized CNFs provide a superior graphitization degree of about 100%, which leads to both high corrosion resistance and low ohmic resistance in PEM fuel cells.
For electrode preparation a GRM containing layer serving as catalyst support is applied onto a gas diffusion layer (GDL). Prior to GRM suspension and deposition onto a GDL, the graphene structures are functionalized by plasma treatment. Due to this step, an improved hydrophilic behavior for facilitating suspension preparation is achieved. In addition, a subsequent platinum nanoparticle deposition by pulsed electrodeposition process is optimized.
Recommendations for the Development of a Robotic Drinking and Eating Aid - An Ethnographic Study
(2021)
Being able to live independently and self-determined in one’s own home is a crucial factor or human dignity and preservation of self-worth. For people with severe physical impairments who cannot use their limbs for every day tasks, living in their own home is only possible with assistance from others. The inability to move arms and hands makes it hard to take care of oneself, e.g. drinking and eating independently. In this paper, we investigate how 15 participants with disabilities consume food and drinks. We report on interviews, participatory observations, and analyzed the aids they currently use. Based on our findings, we derive a set of recommendations that supports researchers and practitioners in designing future robotic drinking and eating aids for people with disabilities.
Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.
As a first step toward this goal, we devise an in situ pipeline tailored to the neuroscientific use case. It is capable of recording and transferring simulation data to an analysis/visualization process, while the simulation is still running. The developed libraries are made publicly available as open source projects. We provide a proof-of-concept integration, coupling the neuronal simulator NEST to basic 2D and 3D visualization.
The Unfitted Discontinuous Galerkin Method for Solving the EEG Forward Problem: A Second Order Study
(2016)
Platinum is one of the most effective electro catalysts for PEMFCs (proton exchange membrane fuel cells), but because of its prohibitive price, the use of this metal in industrial purposes is limited. As a consequence, during last years, several materials have been investigated, in order to obtain an efficient catalyst for both ORR (oxygen reduction reaction) and HOR (hydrogen oxidation reaction), which can replace the expensive platinum but preserving the same properties: high electrical conductivity, structural stability and good corrosion resistance. Moreover, one of the most important parameters for catalyst materials is the electrochemical surface area (real surface area), which has a strong influence on the reaction rate and also on the current density.
CNFs (carbon nanofibers) are considered to be a promising catalyst support material due to their unique characteristics, excellent mechanical, electrical and structural properties, high surface area and nevertheless, good interaction with platinum particles.
The possibility of preparing CNFs decorated with platinum by electrochemical methods was tested, using a hexachloroplatinic solution bath. The experiments were carried out with the aid of a Potentiostat/Galvanostat MMate 510, in a three – electrode cell.
The aim of the present work was to determine the electrochemical surface area of the CNFs – Pt catalysts, using an electrochemical method. The obtained results correlate very well with the particles size and distribution of platinum, analyzed by SEM (scanning electron microscopy) respectively with the quantity of deposited platinum determined by TG (thermo gravimetrical analyses). Cyclic voltammetry is a suitable method for estimation of the real surface area for catalyst particles.
This paper describes a new concept and experiences of a distributed interdisciplinary learning programme for students across continents. The aim is to provide students with a truly Global Intercultural Project Experience (GIPE) by working together with peers from around the world, and solving real-life client’s problems. We have received seed-funding for four annual projects to engage students from Germany (Europe), Namibia (Africa), Indonesia (Asia), and Peru (Latin-America). In 2020, 30 students from four continents engaged in a one-semester distributed software development project for a Namibian client. Despite Covid-19 they successfully completed the project expressing deep appreciation for the learning opportunities overcoming challenges of working across wide-spread time zones, cultures, changing requirements, and various technical challenges. Considering the vast learning benefits, we suggest to incorporate such projects in all tertiary education curricula across the globe.
Competency-oriented exams offer a wide range of advantages, especially where the use and mastery of third-party applications and tools play an important role. Therefore, we developed a competency-oriented setup for both our programming classes and exams ensuring their constructive alignment.
Exams were moved to the computer lab and designed to test both conceptional skills as well as the use of state-of-the-art programming tools. At the peak of the COVID-19 pandemic, when exams had to be moved from lab to online, we needed to design an online setup for our practical programming exams preserving the competency-oriented approach and its constructive alignment as well as the validity, reliability and fairness of the exams. The key was to use the same online tools that have been introduced
for running lectures and practical classes offering almost the same learning experience as before the pandemic. However, to ensure the validity and fairness of the exams, some kind of online supervision needed to be implemented as technical solutions were found to be either unusable or not working
properly in our case. This paper discusses the driving factors, the resulting technical and organizational setup as well as students’ feedback and lessons learned for further improvements. Therefore, COVID-19 has not been able to ruin our competency-oriented programming exams.
This paper reveals various approaches undertaken over more than two decades of teaching undergraduate programming classes at different Higher Education Institutions, in order to improve student activation and participation in class and consequently teaching and learning effectiveness.
While new technologies and the ubiquity of smartphones and internet access has brought new tools to the classroom and opened new didactic approaches, lessons learned from this personal long-term study show that neither technology itself nor any single new and often hyped didactic approach ensured sustained improvement of student activation. Rather it needs an integrated yet open approach towards a participative learning space supported but not created by new tools, technology and innovative teaching methods.
This paper presents a pragmatic approach for stepwise introduction of peer assessment elements in undergraduate programming classes, discusses some lessons learned so far and directions for further work. Students are invited to challenge their peers with their own programming exercises to be submitted through Moodle and evaluated by other students according to a predefined rubric and supervised by teaching assistants. Preliminary results show an increased activation and motivation of students leading to a better performance in the final programming exams.