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Media Brand Management
(2022)
The management of media brands faces challenges. In order to be able to point out possible solutions, this article first explains the concept and the nature of “media brands.” Subsequently, various theoretical approaches to the explanation of media brands and their management are presented. Regardless of theoretical preferences, it is important to keep in mind the brand-strategic complexity of media management that is subsequently described. Due to their specificity, special attention is paid to the basic strategic positioning options and to the communication management of media brands. In this way, the special features of media brand management become clear in comparison with other products and services.
Hydrogen concentrations in ZnO single crystals exposing different surfaces have been determined to be in the range of (0.02–0.04) at.% with an error of ±0.01 at.% using nuclear reaction analysis. In the subsurface region, the hydrogen concentration has been determined to be higher by up to a factor of 10. In contrast to the hydrogen in the bulk, part of the subsurface hydrogen is less strongly bound, can be removed by heating to 550°C, and reaccommodated by loading with atomic hydrogen. By exposing the ZnO(10-10) surface to water above room temperature and to atomic hydrogen, respectively, hydroxylation with the same coverage of hydrogen is observed.
Socio-cultural dynamics in spatial policy: explaining the on-going success of cluster politics
(2013)
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 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.
Hydrogen produced via water electrolysis powered by renewable electricity or green H2 offers new decarbonization pathways. Proton exchange membrane water electrolysis (PEMWE) is a promising technology although the current density, temperature, and H2 pressure of the PEMWE will have to be increased substantially to curtail the cost of green H2. Here, a porous transport layer for PEMWE is reported, that enables operation at up to 6 A cm−2, 90 °C, and 90 bar H2 output pressure. It consists of a Ti porous sintered layer (PSL) on a low‐cost Ti mesh (PSL/mesh‐PTL) by diffusion bonding. This novel approach does not require a flow field in the bipolar plate. When using the mesh‐PTL without PSL, the cell potential increases significantly due to mass transport losses reaching ca. 2.5 V at 2 A cm−2 and 90 °C.
Even though we live in a period when the word digitization is prevalent in many social areas, the COVID-19 pandemic has divided mankind into two main categories: some people have seen this crisis as an opportunity to move the activities online and, furthermore, to accelerate digitization in as many areas as possible, while others have been reluctant, keeping their preferences for face-to-face activities. The current work presents the results of an analysis on 249 students from 11 engineering faculties. The study aims to identify the impact of the COVID-19 pandemic on students’ educational experiences when switching from face-to-face to online education during a public health emergency or COVID 19-related state of alert. The overall conclusion was that, although the pandemic has brought adverse consequences on the health and life quality of many people, the challenges that humankind has been subjected to have led to personal and professional development and have opened up new perspectives for carrying out the everyday activities.
This paper makes a contribution to the discussion on microfoundations of dynamic capabilities – actions and interactions in organizations that enable continuous organizational renewal. More specifically, we propose the idea that dynamic capabilities of an organization
are a positive function of corresponding dynamic capabilities of individual and collective actors in the organization. Further, we develop the assumption that not only individual acts of managers but also of individuals and teams without managerial responsibility relate to dynamic capabilities of the organization. Following a holistic view, we also take into consideration empowering working conditions as enhancing factor of this function. To
examine these roots of dynamic capabilities, we use a multi level model of competence provided by Wilkens, Keller and Schmette (2006) that operationalizes the concept of dynamic
capabilities provided by Teece (2007) on a concisely behavioural base. We investigated our hypotheses with a standardized questionnaire in a case study of a German plant engineering company with 112 participants and found first support for our assumptions. Our results show an impact of individual dynamic capabilities on dynamic capabilities of the organization which is mediated by team dynamic capabilities. Psychological and social-structural empowerment moderated this relationship. A case-specific interpretation and implications for future research and practice are discussed.
Under ambient conditions, almost all metals are coated by an oxide. These coatings, the result of a chemical reaction, are not passive. Many of them bind, activate and modify adsorbed molecules, processes that are exploited, for example, in heterogeneous catalysis and photochemistry. Here we report an effect of general importance that governs the bonding, structure formation and dissociation of molecules on oxidic substrates. For a specific example, methanol adsorbed on the rutile TiO2(110) single crystal surface, we demonstrate by using a combination of experimental and theoretical techniques that strongly bonding adsorbates can lift surface relaxations beyond their adsorption site, which leads to a sig- nificant substrate-mediated interaction between adsorbates. The result is a complex super- structure consisting of pairs of methanol molecules and unoccupied adsorption sites. Infrared spectroscopy reveals that the paired methanol molecules remain intact and do not depro- tonate on the defect-free terraces of the rutile TiO2(110) surface.
Impact of cobalt content and grain growth inhibitors in laser-based powder bed fusion of WC-Co
(2022)
Processing of tungsten carbide‑cobalt (WC-Co) by laser-based powder bed fusion (PBF-LB) can result in characteristic microstructure defects such as cracks, pores, undesired phases and tungsten carbide (WC) grain growth, due to the heterogeneous energy input and the high thermal gradients. Besides the processing conditions, the material properties are affected by the initial powder characteristics. In this paper, the impact of powder composition on microstructure, phase formation and mechanical properties in PBF-LB of WC-Co is studied.
Powders with different cobalt contents from 12 wt.-% to 25 wt.-% are tested under variation of the laser parameters.
Furthermore, the impact of vanadium carbide (VC) and chromium (Cr) additives is investigated. Both are known as grain growth inhibitors for conventional sintering processes. The experiments are conducted at a pre-heating temperature of around 800 ◦C to prevent crack formation in the samples. Increasing laser energy input reduces porosity but leads to severe embrittlement for low cobalt content and to abnormal WC grain growth for high cobalt content. It is found that interparticular porosity at low laser energy is more severe for low cobalt content due to poor wetting of the liquid phase. Maximum bending strength of σB > 1200 MPa and Vickers hardness of approx. 1000 HV3 can be measured for samples generated from WC-Co 83/17 powder with medium laser energy input. The addition of V and Cr leads to increased formation of additional phases such as Co3W3C, Co3V and Cr23C6 and to increased lateral and multi-laminar growth of the WC grains. In contrast to conventional sintering, a grain growth inhibiting effect of V and Cr in the laser molten microstructure is not achieved.
Bifacial photovoltaic (PV) modules are able to utilize light from both sides and can therefore significantly increase the electric yield of PV power plants, thus reducing the cost and improving profitability. Bifacial PV technology has a huge potential to reach a major market share, in particular when considering utility scale PV plants. Accordingly, bifacial PV is currently attracting increasing attention from involved engineers, scientists and investors. There is a lack of available, structured information about this topic. A book that focuses exclusively on bifacial PV thus meets an increasing need. Bifacial Photovoltaics: Technology, applications and economics provides an overview of the history, status and future of bifacial PV technology with a focus on crystalline silicon technology, covering the areas of cells, modules, and systems. In addition, topics like energy yield simulations and bankability are addressed. It is a must-read for researchers and manufacturers involved with cutting-edge photovoltaics.
n-type silicon modules
(2023)
The photovoltaic industry is facing an exponential growth in the recent years fostered by a dramatic decrease in installation prices. This cost reduction is achieved by means of several mechanisms. First, because of the optimization of the design and installation process of current PV projects, and second, by the optimization, in terms of performance, in the manufacturing techniques and material combinations within the modules, which also has an impact on both, the installation process, and the levelized cost of electricity (LCOE).
One popular trend is to increase the power delivered by photovoltaic modules, either by using larger wafer sizes or by combining more cells within the module unit. This solution means a significant increase in the size of these devices, but it implies an optimization in the design of photovoltaic plants. This results in an installation cost reduction which turns into a decrease in the LCOE.
However, this solution does not represent a breakthrough in addressing the real challenge of the technology which affects the module requirements. The innovation efforts must be focused on improving the modules capability to produce energy without enlarging the harvesting area. This challenge can be faced by approaching some of the module characteristics which are summarized in this chapter.
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.
Intercultural Communication Competence: Implications for Learning and Teaching in a Globalized World
(2007)
Three dinuclear zinc carboxylate complexes [L1−3Zn(μ,η2-O2CPh)]2 (1, 2, 4) containing either the bidentate N,N′-chelating β-diketiminate ligand RNC(Me)C(H)C(Me)NR (R = 2,6-iPr2-C6H3, L1, complex 1), the tridentate O,N,N-chelating ligand OC(Me)C(H)C(Me)NCH2CH2NMe2 (L2, complex 2) or the bis-N,N′-chelating bis-β-diketiminate ligand RNC(Me)C(H)C(Me)NNC(Me)C(H)C(Me)NR (R = 2,6-iPr2-C6H3, L3, complex 4) were synthesized and characterized including single-crystal X-ray diffraction. Reaction of the neutral bis-β-diketimine (L3(H)2) with two equivalents of ZnMe2 leads to the expected heteroleptic dinuclear zinc complex L3(ZnMe)2 3 in 93 % yield. Further reaction with benzoic acid PhCO2H leads to complex 4. Complex 2 forms a rather strong carboxylate-bridged dimer, whereas the carboxylate groups in complexes 1 and 4 act as asymmetrical bridges between both Zn atoms, pointing to the formation of a weakly bonded dimer. The zinc atoms in 1 and 4 are tetrahedrally coordinated, whereas in 2 the coordination number is increased to five due to the coordination of the pendant donor arm. The ring opening polymerization (ROP) of rac-lactide was investigated with the zinc complexes 1–4 and diazabicycloundec-7-ene (DBU) as a co-catalyst. Complexes 2 and 3 are active polymerization catalysts, which in the presence of DBU converted 200 equiv. of rac-lactide into polylactide within 10 min at ambient temperature. The analysis of the crude polymer showed that the lactide polymerization with catalyst 2 occurs via a slightly modified activated-monomer mechanism.
Recent experimental results showing atypical nonlinear absorption and marked deviations from well known universality in the low temperature acoustic and dielectric losses in amorphous solids prove the need for improving the understanding of the nature of two-level systems (TLSs) in these materials. Here we suggest the study of TLSs focused on their properties which are nonuniversal. Our theoretical analysis shows that the standard tunneling model and the recently suggested two-TLS model provide markedly different predictions for the experimental outcome of these studies. Our results may be directly tested in disordered lattices, e.g KBr:CN, where there is ample theoretical support for the validity of the two-TLS model, as well as in amorphous solids. Verification of our results in the latter will significantly enhance understanding of the nature of TLSs in amorphous solids, and the ability to manipulate them and reduce their destructive effect in various cutting edge applications including superconducting qubits.
Description and Analysis of Glycosidic Residues in the Largest Open Natural Products Database
(2021)
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
The German supply chain law ( Lieferkettensorgfaltspflichtengesetz, abbreviated: LkSG) which enters into force on 1 January 2023 is part of the developing legal framework for human rights in global supply chains. Like the French vigilance law, it represents a new generation of supply chain laws which impose mandatory human rights due diligence obligations. The LkSG requires enterprises to exercise a number of due diligence obligations – from conducting risk analysis to undertaking preventive measures or remedial actions. The law is based on public enforcement via a competent authority, the Federal Office for Economic Affairs and Export Control (BAFA). The BAFA monitors and enforces compliance with the due diligence obligations. Non-compliant enterprises can be fined with up to 800,000 Euros and, in some cases, up to 2% of the annual turnover. Whilst the LkSG is an important step towards achieving greater corporate sustainability, it also has limitations. It was a political compromise and, as such, it does not include a new civil liability for non-compliance. Moreover, by default, it only applies to the enterprise’s own business area and its direct suppliers, whereas indirect suppliers are only included where the enterprise has substantiated knowledge that an obligation has been violated.
This chapter is a commentary on Principle 20 of the United Nations Guiding Principles on Business and Human Rights (UNGPs). The UNGPs, endorsed by the United Nations Human Rights Council in 2011, are the first universally accepted framework for addressing business responsibilities for human rights. They outline State obligations to protect human rights, businesses’ responsibility to respect human rights, and the importance of both States and businesses offering adequate remedies for human rights breaches.
This chapter is a commentary on Principle 21 of the United Nations Guiding Principles on Business and Human Rights (UNGPs). The UNGPs, endorsed by the United Nations Human Rights Council in 2011, are the first universally accepted framework for addressing business responsibilities for human rights. They outline State obligations to protect human rights, businesses’ responsibility to respect human rights, and the importance of both States and businesses offering adequate remedies for human rights breaches.
In this work, a novel polymer electrolyte membrane water electrolyzer (PEMWE) test cell based on hydraulic single-cell compression is described. In this test cell, the current density distribution is almost homogeneous over the active cell area due to hydraulic cell clamping. As the hydraulic medium entirely surrounds the active cell components, it is also used to control cell temperature resulting in even temperature distribution. The PEMWE single-cell test system based on hydraulic compression offers a 25 cm2 active surface area (5.0 × 5.0 cm) and can be operated up to 80°C and 6.0 A/cm2. Construction details and material selection for the designed test cell are given in this document. Furthermore, findings related to pressure distribution analyzed by utilizing a pressure-sensitive foil, the cell performance indicated by polarization curves, and the reproducibility of results are described. Experimental data indicate the applicability of the presented testing device for relevant PEMWE component testing and material analysis.
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 this study, a novel design concept for PEMFC (polymer electrolytemembrane fuel cell) stacks is presented with singlecells inserted in pockets surrounded by a hydraulic medium. Thehydraulic pressure introduces necessary compression forces to themembrane electrode assembly of each cell within a stack. Moreover, homogeneous cell cooling is achieved by this medium. First,prototypes presented in this work indicate that, upscaling of cells for the novelstack design is possible without significantperformancelosses. Due to its modularity and scalability, this stackdesign meets the requirements for large PEMFC units.
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 this experimental work polymer electrolyte membrane fuel cell (PEMFC) electrodes are analysed, which are prepared by the use of two sorts of carbon nano fibres (CNF) serving as support material for platinum nano particles. Those CNFs, which are heat treated subsequently to their production, have a higher graphitisation degree than fibres as produced. The improved graphitisation degree leads to higher electrical conductivity, which is favourably for the use in PEMFC electrodes. Samples have been analysed, in order to determine graphitisation degree, electrical conductivity, as well as morphology and loading of the prepared electro catalyst. Membrane electrode assemblies manufactured from prepared electrodes are analysed in-situ in a PEM fuel cell test environment. It has been determined that power output for samples containing CNFs with higher graphitisation degree is increased by about 13.5%.
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.
In the polymer electrolyte membrane fuel cells (PEMFC) state of the art, rare and expensive platinum group metals (PGM) or PGM alloys are used as catalyst material. Reduction of PGMs in PEMFC electrodes is strongly required to reach cost targets for this technology. An optimal catalyst utilization is achieved in case of nano-structured particles supported on carbon material with a large specific surface area. In this study, graphitic material, in form of carbon nanofibers (CNF), is decorated with Pt particles, serving as catalyst material for PEMFC electrodes with low Pt loading. As a novelty, the effect of oxygen plasma treatment of CNFs previously to platinum particle deposition has been studied. Electrodes are investigated in respect of the optimal morphology, microstructure as well as electrochemical properties. Therefore, samples are characterized by means of scanning electron microscopy combined with energy dispersive X-ray analysis, transmission electron microscopy, thermogravimetry, X-ray diffraction as well as X-ray fluorescence analysis. In order to determine the electrochemical active surface area of catalyst particles, cyclic voltammetry has been performed in 0.5 M sulphuric acid. Selected samples have been investigated in a PEMFC test bench according to their polarization behavior.
Studies on Pulse Electrodeposition of Pt-Ni binary Alloy For Electrochemical Cell Applications
(2018)
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.
This work deals with the preparation and investigation of polymer electrolyte membrane fuel cell (PEMFC) electrodes, which are obtained using gas diffusion layers coated with graphene related material (GRM) serving as a catalyst support for platinum nanoparticles. PEMFC electrocatalysts have been prepared by pulsed electrochemical deposition of platinum particles from hexachloroplatinic acid. Prior to GRM decoration with platinum, the graphene structures are functionalized by oxygen plasma treatment. This leads to oxygen containing functional groups on the GRM outer surface, providing an improved hydrophilic behavior, thus favoring the Pt deposition process. Membrane electrode assemblies (MEAs) with the so prepared electrodes are investigated in-situ in our fuel cell test system. Polarization plots (in-situ cell performance) using these MEAs have been tested under different operational conditions.
This report gives a brief overview to the state of the art of PEM fuel cell technology and a description of a newly developed fuel cell stack concept. One main research activity at the Westphalian Energy Institute of the Westphalian University of Applied Sciences is the development of PEM fuel cells, for which a range of different materials have been investigated for fuel cell pole plate construction. Whereas graphite is a material which has suitable properties concerning conductivity as well as manufacturing e.g. for milling, stainless steel foils are suitable for economical hydroforming processes. However, with steel coating is necessary to increase corrosion resistance as well as electrical conductivity. A new fuel cell stack design is currently under development using separated single fuel cells with hydraulic cell compression. The advantages of this stack concept are modularity, effective heat exchanging and constant, uniform cell compression which are further described 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.
For this experimental work gas diffusion electrodes (GDE) with low platinum loading are prepared for the application as anodes in polymer electrolyte membrane fuel cells. As catalyst support material, carbon nano fibres (CNF) are investigated due to high specific surface area as well as high graphitisation degree. Optimisation is achieved by an economic and environmental friendly pre-treatment process in oxygen plasma. For electrode preparation an ink is used containing oxygen plasma activated CNFs as well as hydrophilic polymer. After spray coating of this CNF ink on a graphitic substrate, platinum is deposited by pulse plating method. Preliminary results established that the plasma activation improves considerably CNF dispersibility as well as the amount, respectively, the morphology of the deposited platinum. Morphology and microstructure are observed by electron microscopy. Platinum loading is determined by thermogravimetric analysis to be in the range of 0.010 to 0.016 mg cm-2. Furthermore, MEAs are prepared from these GDEs and testing is performed in a novel modular test stack based on hydraulic compression. Technical information about the test stack design and functions are given in this work. In this test environment maximum specific power output of 182 mW cm-2 has been obtained under robust operation conditions.
Cancer is a leading cause of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer related deaths in 2012 [1]. Moreover, the global cancer burden is expected to exceed 20 million new cancer cases by 2025. Understanding the spatial and temporal behaviour of cancer is a crucial precondition to achieve a successful treatment. Because no two cancer cases are the same, every patient should receive a treatment plan designed specifically for her case, in order to improve the patient’s survival chances.
When an open quantum system is driven by an external time-dependent force, the coupling of the driving to the central system is usually included, whereas the impact of the driving field on the bath is neglected. We investigate the effect of a quantum bath of linearly driven harmonic oscillators on the relaxation dynamics of a quantum two-level system which is not directly driven. In particular, we calculate the frequency-dependent response of the system when the bath is subject to Dirac and Gaussian driving pulses. We show that a time-retarded effective force on the system is induced by the driven bath which depends on the full history of the perturbation and the spectral characteristics of the underlying bath. In particular, when a structured Ohmic bath with a pronounced Lorentzian peak is considered, the dynamical response of the system to a driven bath is qualitatively different than that of the undriven bath. Specifically, additional resonances appear which can be directly associated with a Jaynes-Cummings-like effective energy spectrum.
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.
The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public.
Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture
(2024)
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. To evaluate the model's performance, a benchmark was performed using a real-world dataset of hand-drawn chemical structures. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture
(2024)
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.