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The adsorption of water on r-TiO2(110) has been investigated with thermal desorption spectroscopy (TDS) and helium atom scattering. Conventional TDS using a mass spectrometer and He-TDS monitoring reflected He beam intensity consistently show the existence of a structurally well-defined monolayer as well as a highly ordered second layer of water and a disordered multilayer phase. He diffraction patterns recorded along the high symmetry [001], equation image, and equation image directions reveal a well-ordered superstructure with (1x1) symmetry, providing strong evidence for the absence of a partially dissociated monolayer on the perfect parts of the substrate. No changes in the diffraction patterns are observed after irradiation with UV-light.
Ultrafast Energy Transfer in Excitonically Coupled Molecules Induced by a Nonlocal Peierls Phonon
(2019)
Molecular vibration can influence exciton transfer via either a local (intramolecular) Holstein or a nonlocal (intermolecular) Peierls mode. We show that a strong vibronic coupling to a nonlocal mode dramatically speeds up the transfer by opening an additional transfer channel. This Peierls channel is rooted in the formation of a conical intersection of the excitonic potential energy surfaces. For increasing Peierls coupling, the electronically coherent transfer for weak coupling turns into an incoherent transfer of a localized exciton through the intersection for strong coupling. The interpretation in terms of a conical intersection intuitively explains recent experiments of ultrafast energy transfer in photosynthetic and photovoltaic molecular systems.
We show that strong non-Markovian effects can be revealed by the steady-state two-dimensional (2D) photon echo spectra at asymptotic waiting times. For this, we use a simple dimer toy model that is strongly coupled to a harmonic bath with parameters typical for photoactive biomolecules. We calculate the 2D photon echo spectra employing both the numerically exact hierarchy equation of motion and the quasiadiabatic path integral approach and compare these results with approximate results from a time-nonlocal quantum master equation approach. While the latter correctly reproduces the exact population dynamics at long times, it fails at the same time to correctly describe the 2D photon echo spectra at long waiting times. The differences show that non-Markovian effects are much more important for the steady-state 2D photon echoes than for the equilibrium populations. Thus, accurate theoretical descriptions of the energy transfer dynamics in biomolecular complexes have to be based on numerically exact simulations of the environmental fluctuations when nonlinear response functions are analyzed.
Third-party tracking is a common and broadly used technique on the Web. Different defense mechanisms have emerged to counter these practices (e.g. browser vendors that ban all third-party cookies). However, these countermeasures only target third-party trackers and ignore the first party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g. analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the first party but utilized by a third party to circumvent standard tracking preventing techniques. We visit the top 15,000 websites to analyze first-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place first-party cookies. Using this data, we show that 76% of sites effectively utilize such tracking techniques. In a long-running analysis, we show that the usage of such cookies increased by more than 50% over 2021.
For proton exchange membrane water electrolysis (PEMWE) to become competitive, the cost of stack components, such as bipolar plates (BPP), needs to be reduced. This can be achieved by using coated low-cost materials, such as copper as alternative to titanium. Herein we report on highly corrosion-resistant copper BPP coated with niobium. All investigated samples showed excellent corrosion resistance properties, with corrosion currents lower than 0.1 µA cm−2 in a simulated PEM electrolyzer environment at two different pH values. The physico-chemical properties of the Nb coatings are thoroughly characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). A 30 µm thick Nb coating fully protects the Cu against corrosion due to the formation of a passive oxide layer on its surface, predominantly composed of Nb2O5. The thickness of the passive oxide layer determined by both EIS and XPS is in the range of 10 nm. The results reported here demonstrate the effectiveness of Nb for protecting Cu against corrosion, opening the possibility to use it for the manufacturing of BPP for PEMWE. The latter was confirmed by its successful implementation in a single cell PEMWE based on hydraulic compression technology.
The printing variable least addressed in previous research aiming to reveal the effect of the FFF process parameters on the printed PLA part’s quality and properties is the filament color. Moreover, the color of the PLA, as well as its manufacturer, are rarely mentioned when the experimental conditions for the printing of the samples are described, although current existing data reveal that their influence on the final characteristics of the print should not be neglected. In order to point out the importance of this influential parameter, a natural and a black-colored PLA filament, produced by the same manufacturer, were selected. The dimensional accuracy, tensile strength, and friction properties of the samples were analyzed and compared for printing temperatures ranging from 200 C up to 240 C. The experimental results clearly showed different characteristics depending on the polymer color of samples printed under the same conditions. Therefore, the optimization of the FFF process parameters for the 3D-printing of PLA should always start with the proper selection of the type of the PLA material, regarding both its color and the fabricant.
Among the FDM process variables, one of the less addressed in previous research is the filament color. Moreover, if not explicitly targeted, the filament color is usually not even mentioned.
Aiming to point out if, and to what extent, the color of the PLA filaments influences the dimensional precision and the mechanical strength of FDM prints, the authors of the present research carried out experiments on tensile specimens. The variable parameters were the layer height (0.05 mm, 0.10 mm, 0.15 mm, 0.20 mm) and the material color (natural, black, red, grey). The experimental results clearly showed that the filament color is an influential factor for the dimensional accuracy as well as for the tensile strength of the FDM printed PLA parts. Moreover, the two way ANOVA test performed revealed that the strongest effect on the tensile strength was exerted by the PLA color (2 = 97.3%), followed by the layer height (2 = 85.5%) and the interaction between the PLA color and the layer height (2 = 80.0%). Under the same printing conditions, the best dimensional accuracy was ensured by the black PLA (0.17% width deviations, respectively 5.48% height deviations), whilst the grey PLA showed the highest ultimate tensile strength values (between 57.10 MPa and 59.82 MPa).
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.
A systematic method for obtaining a novel electrode structure based on PtCoMn ternary alloy catalyst supported on graphitic carbon nanofibers (CNF) for hydrogen evolution reaction (HER) in acidic media is proposed. Ternary alloy nanoparticles (Co0.6Mn0.4 Pt), with a mean crystallite diameter under 10 nm, were electrodeposited onto a graphitic support material using a two-step pulsed deposition technique. Initially, a surface functionalisation of the carbon nanofibers is performed with the aid of oxygen plasma. Subsequently, a short galvanostatic pulse electrodeposition technique is applied. It has been demonstrated that, if pulsing current is employed, compositionally controlled PtCoMn catalysts can be achieved. Variations of metal concentration ratios in the electrolyte and main deposition parameters, such as current density and pulse shape, led to electrodes with relevant catalytic activity towards HER. The samples were further characterised using several physico-chemical methods to reveal their morphology, structure, chemical and electrochemical properties. X-ray diffraction confirms the PtCoMn alloy formation on the graphitic support and energy dispersive X-ray spectroscopy highlights the presence of the three metallic components from the alloy structure. The preliminary tests regarding the electrocatalytic activity of the developed electrodes display promising results compared to commercial Pt/C catalysts. The PtCoMn/CNF electrode exhibits a decrease in hydrogen evolution overpotential of about 250 mV at 40 mA cm−2 in acidic solution (0.5 M H2SO4) when compared to similar platinum based electrodes (Pt/CNF) and a Tafel slope of around 120 mV dec−1, indicating that HER takes place under the Volmer-Heyrovsky mechanism.
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.
Tunneling two-level systems (TLSs) are ubiquitous in amorphous solids, and form a major source of noise in systems such as nano-mechanical oscillators, single electron transistors, and superconducting qubits. Occurance of defect tunneling despite their coupling to phonons is viewed as a hallmark of weak defect-phonon coupling. This is since strong coupling to phonons results in significant phonon dressing and suppresses tunneling in two-level tunneling defects effectively. Here we determine the dynamics of a tunneling defect in a crystal strongly coupled to phonons incorporating the full 3D geometry in our description. Wefind that inversion symmetric tunneling is not dressed by phonons whereas other tunneling pathways are dressed by phonons and, thus, are suppressed by strong defect-phonon coupling. We provide the linear acoustic and dielectric response functions for a tunneling defect in a crystal for strong defect-phonon coupling. This allows direct experimental determination of the defect-phonon coupling. The singling out of inversion-symmetric tunneling states in single tunneling defects is complementary to their dominance of the low energy excitations in strongly disordered solids as a result of inter-defect interactions for large defect concentrations. This suggests that inversion symmetric TLSs play a unique role in the low energy properties of disordered solids.
We study the nonequilibrium dynamics of a quantum system under the influence of two noncommuting fluctuation sources, i.e., purely dephasing fluctuations and relaxational fluctuations. We find that increasing purely dephasing fluctuations suppress increasing relaxation in the quantum system. This effect is further enhanced when both fluctuation sources are fully correlated. These effects arise for medium to strong primary fluctuations already when the secondary fluctuations are weak due to their noncommuting coupling to the quantum system. Dephasing, in contrast, is increased by increasing any of the two fluctuations. Fully correlated fluctuations result in overdamping at much lower system-bath coupling than uncorrelated noncommuting fluctuations. In total, we observe that treating subdominant secondary environmental fluctuations perturbatively leads, as neglecting them, to erroneous conclusions.
The two-state two-path model is introduced as a minimized model to describe the quantum dynamics of an electronic wave packet in the vicinity of a conical intersection. It involves two electronic potential energy surfaces each of which hosts a pair of quasi-classical trajectories over which the wave packet is assumed to be delocalized. When both trajectories evolve dynamically either diabatically or adiabatically, the full wave packet dynamics shows only features of the dynamics around avoided level crossings in the vicinity of the conical intersection. When one trajectory evolves adiabatically whereas the other trajectory follows a diabatic evolution, quantum mechanical interference of the wave packet components on each path generates Stueckelberg oscillations in the transition probability. These are surprisingly robust against a dissipative environment and, thus, should be a marker for conical intersections.
The two-state two-path model is introduced as a minimized model to describe the quantum dynamics of an electronic wave packet in the vicinity of a conical intersection. It involves two electronic potential energy surfaces each of which hosts a pair of quasi-classical trajectories over which the wave packet is assumed to be delocalized. When both trajectories evolve dynamically either diabatically or adiabatically, the full wave packet dynamics shows only features of the dynamics around avoided level crossings in the vicinity of the conical intersection. When one trajectory evolves adiabatically whereas the other trajectory follows a diabatic evolution, quantum mechanical interference of the wave packet components on each path generates Stueckelberg oscillations in the transition probability. These are surprisingly robust against a dissipative environment and, thus, should be a marker for conical intersections.
Studies on Pulse Electrodeposition of Pt-Ni binary Alloy For Electrochemical Cell Applications
(2018)
Geometries, stabilities, electronic properties and NMR-shielding of cucurbit[6]uril–spermine host-ligand complexes are investigated with DFT calculations and compared to experimental results. Cucurbit[6]uril and spermine can form complexes with two different minimum energy geometries and corresponding characteristic differences in NMR shielding. The energetically preferred complex geometry has a perfect inversion symmetry and its proton NMR shielding agrees very well with experimental results. The cucurbit[6]uril host molecule shows a distinct geometrical flexibility in ligand binding which allows an induced fit of the spermine ligand. The energetic barrier for the rotation of spermine in the favourable complex is approximated to be in the order of a few kilocalories per mole.
Streptavidin-coated TiO2 surfaces are biologically inert: Protein adsorption and osteoblast adhesion
(2012)
Non‐fouling TiO2 surfaces are attractive for a wide range of applications such as biosensors and medical devices, where biologically inert surfaces are needed. Typically, this is achieved by controlled surface modifications which prevent protein adsorption. For example, polyethylene glycol (PEG) or PEG‐derived polymers have been widely applied to render TiO2 surfaces biologically inert. These surfaces have been further modified in order to achieve specific bio‐activation. Therefore, there have been efforts to specifically functionalize TiO2 surfaces with polymers with embedded biotin motives, which can be used to couple streptavidin for further functionalization. As an alternative, here a streptavidin layer was immobilized by self‐assembly directly on a biotinylated TiO2 surface, thus forming an anti‐adhesive matrix, which can be selectively bio‐activated. The anti‐adhesive properties of these substrates were analyzed by studying the interaction of the surface coating with fibronectin, lysozym, and osteoblast cells using surface plasmon resonance spectroscopy, atomic force microscopy, and light microscopy. In contrast to non‐modified TiO2 surfaces, streptavidin‐coated TiO2 surfaces led to a very biologically inert substrate, making this type of surface coating a promising alternative to polymer coatings of TiO2 surfaces.
Streptavidin is a 58 kDa tetrameric protein with the highest known affinity to biotin with a wide range of applications in bionanotechnology and molecular biology. Dissolved streptavidin is stable at a broad range of temperature, pH, proteolytic enzymes and exhibits low non‐specific binding. In this study, a streptavidin monolayer was assembled directly on a biotinylated TiO2‐surface to investigate its stability against proteolytic digestion and its suppression of initial bacterial adsorption of Escherichia coli, Bacillus subtilis, and Streptococcus intermedius. In contrast to nonmodified TiO2 surfaces, streptavidin‐coated substrates showed only a negligible non‐specific protein adsorption at physiological protein concentrations as well as a significantly reduced bacterial adhesion. The antiadhesive properties were demonstrated to be the main reason for the suppression of bacterial adhesion, which makes this approach a promising option for future surface biofunctionalization applications.
The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses.
While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious
exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.
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.
When a hydrophilic solute in water is suddenly turned into a hydrophobic species, for instance, by photoionization, a layer of hydrated water molecules forms around the solute on a time scale of a few picoseconds. We study the dynamic buildup of the hydration shell around a hydrophobic solute on the basis of a time-dependent dielectric continuum model. Information about the solvent is spectroscopically extracted from the relaxation dynamics of a test dipole inside a static Onsager sphere in the nonequilibrium solvent. The growth process is described phenomenologically within two approaches. First, we consider a time-dependent thickness of the hydration layer that grows from zero to a finite value over a finite time. Second, we assume a time-dependent complex permittivity within a finite layer region around the Onsager sphere. The layer is modeled as a continuous dielectric with a much slower fluctuation dynamics. We find a time-dependent frequency shift down to the blue of the resonant absorption of the dipole, together with a dynamically decreasing line width, as compared to bulk water. The blue shift reflects the work performed against the hydrogen-bonded network of the bulk solvent and is a directly measurable quantity. Our results are in agreement with an experiment on the hydrophobic solvation of iodine in water.
We propose a quantum-mechanical model to calculate the nonlinear differential conductance of a single molecular junction immersed in a solvent, either in pure form or as a binary mixture with varying volume fraction. The solvent mixture is captured by a dielectric continuum model for which the resulting spectral density is determined within the Gladstone-Dale approach. The conductance of the molecular junction is calculated by a real-time diagrammatic technique. We find a strong variation of the conductance maximum for varying volume fraction of the solvent mixture. Importantly, the calculated molecular nonlinear conductance shows a very good agreement with experimentally measured data for common molecular junctions in various polar solvent mixtures.
As vaccination campaigns are in progress in most countries, hopes to win back more normality are rising. However, the exact path from a pandemic to an endemic virus remains uncertain. While in the pre-vaccination phase many critical indoor situations were avoided by strict control measures, for the transition phase a certain mitigation of the effect of indoor situations seems advisable.
To better understand the mechanisms of indoor airborne transmissions, we present a new time-discrete model to calculate the level of exposure towards infectious SARS-CoV-2 aerosol and carry out a sensitivity analysis for the level of SARS-CoV-2 aerosol exposure in indoor settings. Time limitations and the use of any kind of masks were found to be strong mitigation measures, while how far the effort for a strict use of professional face pieces instead of simple masks can be justified by the additional reduction of the exposure dose remains unclear. Very good ventilation of indoor spaces is mandatory. The definition of sufficient ventilation in regard to airborne SARS-CoV-2 transmission follows other rules than the standards in ventilation design. This means that especially smaller rooms most likely require a significantly greater fresh air supply than usual. Further research on 50% group models in schools is suggested. The benefits of a model in which the students come to school every day, but for a limited time, should be investigated. In terms of window ventilation, it has been found that many short opening periods are not only thermally beneficial, they also reduce the exposure dose. The fresh air supply is driven by the temperature gradient and wind speed. However, the sensitivity towards these parameters is not very high and in times of low wind and temperature gradients, there are no arguments against keep windows open in order to make up for the reduced air flow rate. Long total opening periods and large window surfaces will strongly reduce the exposure. Additionally, the results underline the expectable fact that exposure doses will increase when hygiene and control measures are reduced. It seems advisable to investigate what this means for the infection rate and the fatality of infections in populations with partial immunity. Very basic considerations suggest that the value of aerosol reduction measures may be reduced with very infectious variants such as delta.
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 purpose of the paper is to contribute to the inner workings of transformational leadership in the context of organizational change. According to the organizational role theory, role conflict is proposed as a mediator between transformational leadership and affective commitment to change and irritation. Cross-sectional data were collected in a German company in the textiles sector, undergoing a pervasive IT-related change. Confirmatory factor analysis and structural equation modeling was performed for validity and hypothesis testing. The findings suggest that role conflict acts as a full mediator in the relationship between transformational leadership and affective commitment to change, as well as irritation. Transformational leadership is often discussed in terms of change-oriented leadership. Surprisingly, only a few studies have examined the specific impact of transformational leadership on attitudinal outcomes during change processes, yet. Consequently, research on the underlying psychological mechanisms of the relationship is scarce, too.
Cone-Beam computed tomography (CBCT) has become the most important component of modern radiotherapy for positioning tumor patients directly before treatment. In this work we investigate alternations to standard acquisition protocol, called preset, for patients with a tumor in the thoracic region. The effects of the changed acquisition parameters on the image quality are evaluated using the Catphan Phantom and the image analysis software Smári. The weighted CT dose index (CTDIW) is determined in each case and the effects of the different acquisition protocols on the patient dose are classified accordingly. Additionally, the clinical suitability of alternative presets is tested by investigating correctness of image registration using the CIRS thorax phantom. The results show that a significant dose reduction can be achieved. It can be reduced by 51% for a full rotation by adjusting the gantry speed.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
Quantum systems are typically subject to various environmental noise sources. Treating these environmental disturbances with a system-bath approach beyond weak coupling, one must refer to numerical methods as, for example, the numerically exact quasi-adiabatic path integral approach. This approach, however, cannot treat baths which couple to the system via operators, which do not commute. We extend the quasi-adiabatic path integral approach by determining the time discrete influence functional for such non-commuting fluctuations and by modifying the propagation scheme accordingly. We test the extended quasi-adiabatic path integral approach by determining the time evolution of a quantum two-level system coupled to two independent baths via non-commuting operators. We show that the convergent results can be obtained and agreement with the analytical weak coupling results is achieved in the respective limits.
Protraction Effects in a Stochastic Cell-Cycle Tumor Model Exposed to Fractionated Radiotherapy
(2013)
Based on the fact that titanium and titanium alloys have poor fretting fatigue resistance and poor tribological properties, it is necessary to apply some surface engineering methods in order to increase the exploitation characteristics of these materials. One may either implement some surface treatment technologies or even deposit overlay coatings by thermal spraying.
The present study is focused on the achieved properties of the ceramic coatings (Al2O3 + 13 wt.% TiO2) deposited onto a titanium substrate using high velocity oxygen fuel (HVOF) and plasma spraying (APS) respectively.
The effect of the deposition method on the microstructure, phase constituents, and mechanical properties of the ceramic coatings was investigated by means of scanning electron microscopy (SEM), X-ray diffraction technique (XRD) and nanoindentation tests. The sliding wear performances of the Al2O3–TiO2 coatings were tested using a pin on disk wear tester.
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.
Preparation, catalytical activity and crystal structure of a heptanuclear zinc acetate cluster
(2017)
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.
Advanced Persistent Threats (APTs) are one of the main challenges in modern computer security. They are planned and performed by well-funded, highly-trained and often state-based actors. The first step of such an attack is the reconnaissance of the target. In this phase, the adversary tries to gather as much intelligence on the victim as possible to prepare further actions. An essential part of this initial data collection phase is the identification of possible gateways to intrude the target.
In this paper, we aim to analyze the data that threat actors can use to plan their attacks. To do so, we analyze in a first step 93 APT reports and find that most (80 %) of them begin by sending phishing emails to their victims. Based on this analysis, we measure the extent of data openly available of 30 entities to understand if and how much data they leak that can potentially be used by an adversary to craft sophisticated spear phishing emails. We then use this data to quantify how many employees are potential targets for such attacks. We show that 83 % of the analyzed entities leak several attributes of uses, which can all be used to craft sophisticated phishing emails.
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.
Due to high power density and superior efficiency, polymer electrolyte membrane fuel cells (PEMFC) are believed to play a significant role for carbon dioxide emissions free electrical energy systems in the future. Unlike in Carnot processes, chemical energy in the form of hydrogen and oxygen is converted directly into electrical energy without a further process step. One issue in the development of PEMFCs for mobile or stationary applications is the utilization of rare and expensive catalyst material like platinum within the membrane electrode assembly (MEA) see figure 1. In addition, the objective is to reduce production costs and to increase the lifetime of PEMFC. One approach to improve PEMFCs is the development of intelligent electrode architectures. However, cost effective high performance materials are necessary to reach the development targets.
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.
Patient specific simulation of brain stimulation using the unfitted discontinuous galerkin method
(2015)
Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed.
Optimization of the laser remelting process for HVOF-sprayed Stellite 6 wear resistant coatings
(2016)
Cobalt base alloys are used in all industrial areas due to their excellent wear resistance. Several studies have shown that Stellite 6 coatings are suitable not only for protection against sliding wear, but also in case of exposure to impact loading. In this respect, a possible application is the protection of hydropower plant components affected by cavitation. The main problem in connection with Stellite 6 is the deposition procedure of the protective layers, both welding and thermal spraying techniques requesting special measures in order to prevent the brittleness of the coating. In this study, Stellite 6 layers were HVOF thermally sprayed on a martensitic 13-4 stainless steel substrate, as usually used for hydraulic machinery components. In order to improve the microstructure of the HVOF-sprayed coatings and their adhesion to the substrate, laser remelting was applied, using a TRUMPF Laser type HL 124P LCU and different working parameters. The microstructure of the coatings, obtained for various remelting conditions, was evaluated by light microscopy, showing the optimal value of the pulse power, which provided a homogenous Stellite 6 layer with good adhesion to the substrate.
Platinum nanoparticles electrodeposition on carbon nanofibers (CNF) support has been performed with the purpose to obtain electrodes that can be further used especially in a polymer electrolyte membrane fuel cell (PEMFC). A pretreatment of CNF is required in order to enhance the surface energy, which simultaneously improves handling and wettability as well as interaction with the platinum cations. This step was performed using oxygen plasma functionalization. To produce CNF supported Pt catalysts, an electrochemical method was applied and the deposition parameters were adjusted to obtain nanosized platinum particles with a good distribution onto the graphitic surface. The morphology and structure of the obtained particles were investigated by scanning electron microscopy combined with energy dispersive X-Ray spectroscopy. The amount of deposited platinum was established using thermogravimetrical measurements. Cyclic voltammetry performed in 0.5 M H2SO4 solution was applied for determining the electrochemical surface area (ECSA) of the obtained electrodes.The functionalization degree of the CNF outer surface has a strong influence on the structure, distribution and amount of platinum particles. Moreover, the current densities, which were set for the deposition process influenced not only the particles size but also the platinum amount. Applying an oxygen plasma treatment of 80 W for 1800 s, the necessary degree of surface functionalization is achieved in order to deposit the catalyst particles. The best electrodes were prepared using a current density of 50 mA cm-2 during the deposition process that leads to a homogenous platinum distribution with particles size under 80 nm and ECSA over 6 cm2
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
Welding and joining of components processed by additive manufacturing (AM) to other AMas well as conventionally produced components is of high importance for industry as thisallows to combine advantages of either technique and to produce large-scale structures,respectively. One of the key influencing factors with respect to weldability and mechanicalproperties of AM components was found to be the inherent microstructural anisotropy ofthese components. In present work, the precipitation-hardenable AleSi10Mg was fabri-cated in different build orientations using selective laser melting (SLM) and subsequentlyjoined by friction stir welding (FSW) in different combinations. Microstructural analysisshowed considerable grain refinement in the friction stir zone, however, pronouncedsoftening occurred in this area. The latter can be mainly attributed to changes in themorphology and size of Si particles. Upon combination of different build orientations aremarkable influence on the tensile strength of FSW joints was seen. Cyclic deformationresponses of SLM and FSW samples were examined in depth. Fatigue properties of thisalloy in the low-cycle fatigue (LCF) regime imply that SLM samples with the building di-rection parallel to the loading direction show superior performance under cyclic loading ascompared to the other conditions and the FSW joints. From results presented solid process-microstructure-property relationships are drawn.
Metallic implants in magnetic resonance imaging (MRI) are a potential safety risk since the energy absorption may increase temperature of the surrounding tissue. The temperature rise is highly dependent on implant size. Numerical examinations can be used to calculate the energy absorption in terms of the specific absorption rate (SAR) induced by MRI on orthopaedic implants. This research presents the impact of titanium osteosynthesis spine implants, called spondylodesis, deduced by numerical examinations of energy absorption in simplified spondylodesis models placed in 1.5 T and 3.0 T MRI body coils. The implants are modelled along with a spine model consisting of vertebrae and disci intervertebrales thus extending previous investigations [1], [2]. Increased SAR values are observed at the ends of long implants, while at the center SAR is significantly lower. Sufficiently short implants show increased SAR along the complete length of the implant. A careful data analysis reveals that the particular anatomy, i.e. vertebrae and disci intervertebrales, has a significant effect on SAR. On top of SAR profile due to the implant length, considerable SAR variations at small scale are observed, e.g. SAR values at vertebra are higher than at disc positions.
Metallic implants in magnetic resonance imaging (MRI) are a potential safety risk since the energy absorption may increase temperature of the surrounding tissue. The temperature rise is highly dependent on implant size. Numerical examinations can be used to calculate the energy absorption in terms of the specific absorption rate (SAR) induced by MRI on orthopaedic implants. This research presents the impact of titanium osteosynthesis spine implants, called spondylodesis, deduced by numerical examinations of energy absorption in simplified spondylodesis models placed in 1.5 T and 3.0 T MRI body coils. The implants are modelled along with a spine model consisting of vertebrae and disci intervertebrales thus extending previous investigations [1, 2]. Increased SARvalues are observed at the ends of long implants, while at the center SAR is significantly lower. Sufficiently short implants show increased SAR along the complete length of the implant. A careful data analysis reveals that the particular anatomy, i.e. vertebrae and disci intervertebrales, has a significant effect on SAR. On top of SAR profile due to the implant length, considerable SAR variations at small scale are observed, e.g. SAR values at vertebra are higher than at disc positions.
Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide’s charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.
The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow’s S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle–particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.
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.
Environmental noise leads to dephasing and relaxation in a quantum system. Often, a rigorous treatment of multiple noise sources within a system-bath approach is not possible. We discuss the influence of environmental fluctuations on a quantum system whose dynamics is dephasing already due to a phenomenologically treated additional noise source. For this situation, we develop a path-integral approach, which allows us to treat the system-environment coupling in a numerically exact way, and additionally we extend standard perturbative approaches. We observe strong deviations between the numerically exact and the perturbative results even for weak system-bath coupling. This shows that standard perturbative approaches fail for additional, even weak, system-bath couplings if the system dynamics is already dissipative.
Commonly, nanosystems are characterized by their response to time-dependent external fields in the presence of inevitable environmental fluctuations. The direct impact of the external driving on the environment is generally neglected. While this approach is satisfactory for macroscopic systems, on the nanoscale, an interaction of external fields with the environment is often unavoidable on principle. We extend the standard linear response theory of quantum dissipative systems to strongly driven baths. Significant modifications are found for two paradigm examples. First, we evaluate the polarizability of a molecule immersed in a strongly polarizable medium that responds to terahertz radiation. We find an increase of the molecular polarizability by about 30%. Second, we determine the response of a semiconductor quantum dot in close proximity to a metallic nanoparticle. Both are placed in a polarizable medium and exposed to electromagnetic irradiation. We show that the response of the quantum dot is qualitatively modified by the driven nanoparticle, including the generation of an additional channel of stimulated emission.
The diffusion of hydrogen adsorbed inside layered MoS2 crystals has been studied by means of quasi- elastic neutron scattering, neutron spin-echo spectroscopy, nuclear reaction analysis, and X-ray photoelectron spectroscopy. The neutron time-of-flight and neutron spin-echo measurements demonstrate fast diffusion of hydrogen molecules parallel to the basal planes of the two dimensional crystal planes. At room temperature and above, this intra-layer diffusion is of a similar speed to the surface diffusion that has been observed in earlier studies for hydrogen atoms on Pt surfaces. A significantly slower hydrogen diffusion was observed perpendicular to the basal planes using nuclear reaction analysis.
Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the rules. This requires a computational workflow with data import, fragmentation algorithm integration, and result visualisation. The described workflow is normally unavailable for a new algorithm and must be set up individually. This work presents an open Java rich client Graphical User Interface (GUI) application to support the development of new in silico molecule fragmentation algorithms and make them readily available upon release. The MORTAR (MOlecule fRagmenTAtion fRamework) application visualises fragmentation results of a set of molecules in various ways and provides basic analysis features. Fragmentation algorithms can be integrated and developed within MORTAR by using a specific wrapper class. In addition, fragmentation pipelines with any combination of the available fragmentation methods can be executed. Upon release, three fragmentation algorithms are already integrated: ErtlFunctionalGroupsFinder, Sugar Removal Utility, and Scaffold Generator. These algorithms, as well as all cheminformatics functionalities in MORTAR, are implemented based on the Chemistry Development Kit (CDK).
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.
The European General Data Protection Regulation (GDPR), which went into effect in May 2018, brought new rules for the processing of personal data that affect many business models, including online advertising. The regulation’s definition of personal data applies to every company that collects data from European Internet users. This includes tracking services that, until then, argued that they were collecting anonymous information and data protection requirements would not apply to their businesses.
Previous studies have analyzed the impact of the GDPR on the prevalence of online tracking, with mixed results. In this paper, we go beyond the analysis of the number of third parties and focus on the underlying information sharing networks between online advertising companies in terms of client-side cookie syncing. Using graph analysis, our measurement shows that the number of ID syncing connections decreased by around 40 % around the time the GDPR went into effect, but a long-term analysis shows a slight rebound since then. While we can show a decrease in information sharing between third parties, which is likely related to the legislation, the data also shows that the amount of tracking, as well as the general structure of cooperation, was not affected. Consolidation in the ecosystem led to a more centralized infrastructure that might actually have negative effects on user privacy, as fewer companies perform tracking on more sites.
We derive a Magnus expansion for a frequency chirped quantum two-level system. We obtain a time-independent effective Hamiltonian which generates a stroboscopic time evolution. At lowest order the according dynamics is identical to results from using a rotating wave approximation. We determine, furthermore, also the next higher-order corrections within our expansion scheme in correspondence to the Bloch-Siegert shifts for harmonically driven systems. Importantly, our scheme can be extended to more complicated systems, i.e., even many-body systems.