Mass transport aspects of polymer electrolyte fuel cells under two-phase flow conditions

Die Visualisierung und Quantifizierung von Flüssigwasseransammlungen in Polymerelektrolytmembran-Brennstoffzellen konnte mittels Neutronenradiographie erreicht werden. Dank dieser neuartigen diagnostischen Methode konnte erstmals die Flüssigwasseransammlung in den porösen Gasdiffusionsschichten direkt nachgewiesen und quantifiziert werden. Die Kombination von Neutronenradiographie mit ortsaufgelösten Stromdichtemessungen bzw. lokaler Impedanzspektroskopie erlaubte die Korrelation des inhomogenen Flüssigwasseranfalls mit dem lokalen elektrochemischen Leistungsverhalten. Systematische Untersuchungen an Polymerelektrolyt- und Direkt-Methanol-Brennstoffzellen verdeutlichen sowohl den Einfluss von Betriebsbedingungen als auch die Auswirkung von Materialeigenschaften auf die Ausbildung zweiphasiger Strömungen

Towards wave extraction in numerical relativity: the quasi-Kinnersley frame

The Newman-Penrose formalism may be used in numerical relativity to extract coordinate-invariant information about gravitational radiation emitted in strong-field dynamical scenarios. The main challenge in doing so is to identify a null tetrad appropriately adapted to the simulated geometry such that Newman-Penrose quantities computed relative to it have an invariant physical meaning. In black hole perturbation theory, the Teukolsky formalism uses such adapted tetrads, those which differ only perturbatively from the background Kinnersley tetrad. At late times, numerical simulations of astrophysical processes producing isolated black holes ought to admit descriptions in the Teukolsky formalism. However, adapted tetrads in this context must be identified using only the numerically computed metric, since no background Kerr geometry is known a priori. To do this, this paper introduces the notion of a quasi-Kinnersley frame. This frame, when space-time is perturbatively close to Kerr, approximates the background Kinnersley frame. However, it remains calculable much more generally, in space-times non-perturbatively different from Kerr. We give an explicit solution for the tetrad transformation which is required in order to find this frame in a general space-time.Comment: 13 pages, 3 figure

Enabling IoT ecosystems through platform interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.Peer ReviewedPostprint (author's final draft

On the origin and application of the Bruggeman Correlation for analysing transport phenomena in electrochemical systems

The widely used Bruggeman equations correlate tortuosity factors of porous media with their porosity. Finding diverse application from optics to bubble formation, it received considerable attention in fuel cell and battery research, recently. The ability to estimate tortuous mass transport resistance based on porosity alone is attractive, because direct access to the tortuosity factors is notoriously difficult. The correlation, however, has limitations, which are not widely appreciated owing to the limited accessibility of the original manuscript. We retrace Bruggemans derivation, together with its initial assumptions, and comment on validity and limitations apparent from the original work to offer some guidance on its use.<br/

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness

On the validity of the bipolaron model for undoped and AlCl4- doped PEDOT

The conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) is one of the most highly researched materials, yet electronic structure investigations of conducting polymers are still uncommon. The bipolaron model has traditionally been the dominant attempt to explain the electronic structure of PEDOT. Though recent theoretical studies have begun to move away from this model, some aspects remain commonplace, such as the concepts of bipolarons or polaron pairs. In this work, we use density functional theory to investigate the electronic structure of undoped and AlCl4- doped PEDOT oligomers. By considering the influence of oligomer length, oxidation or doping level and spin state, we find no evidence for self-localisation of positive charges in PEDOT as predicted by the bipolaron model. Instead, we find that a single or twin peak structural distortion can occur at any oxidation or doping level. Rather than representing bipolarons or polaron pairs, these are electron distributions driven by a range of factors, which also disproves the concept of polaron pairs. Localisation of distortions does occur in the doped case, although distortions can span an arbitrary number of nearby anions. Furthermore, conductivity in conducting polymers has been experimentally observed to reduce at very high doping levels. We show that at high anion concentrations, the non-bonding orbitals of the anions cluster below the HOMO-LUMO gap and begin to mix into the HOMO of the overall system. We propose that this mixing of highly localised anionic orbitals into the HOMO reduces the conductivity of the polymer and contributes to the reduced conductivity previously observed

Cold Atmospheric Plasma in the Treatment of Osteosarcoma

Human osteosarcoma (OS) is the most common primary malignant bone tumor occurring most commonly in adolescents and young adults. Major improvements in disease-free survival have been achieved by implementing a combination therapy consisting of radical surgical resection of the tumor and systemic multi-agent chemotherapy. However, long-term survival remains poor, so novel targeted therapies to improve outcomes for patients with osteosarcoma remains an area of active research. This includes immunotherapy, photodynamic therapy, or treatment with nanoparticles. Cold atmospheric plasma (CAP), a highly reactive (partially) ionized physical state, has been shown to inherit a significant anticancer capacity, leading to a new field in medicine called “plasma oncology.” The current article summarizes the potential of CAP in the treatment of human OS and reviews the underlying molecular mode of action

Invariants of the Riemann tensor for Class B Warped Product Spacetimes

We use the computer algebra system \textit{GRTensorII} to examine invariants polynomial in the Riemann tensor for class $B$ warped product spacetimes - those which can be decomposed into the coupled product of two 2-dimensional spaces, one Lorentzian and one Riemannian, subject to the separability of the coupling: $ds^2 = ds_{\Sigma_1}^2 (u,v) + C(x^\gamma)^2 ds_{\Sigma_2}^2 (\theta,\phi)$ with $C(x^\gamma)^2=r(u,v)^2 w(\theta,\phi)^2$ and $sig(\Sigma_1)=0, sig(\Sigma_2)=2\epsilon (\epsilon=\pm 1)$ for class $B_{1}$ spacetimes and $sig(\Sigma_1)=2\epsilon, sig(\Sigma_2)=0$ for class $B_{2}$. Although very special, these spaces include many of interest, for example, all spherical, plane, and hyperbolic spacetimes. The first two Ricci invariants along with the Ricci scalar and the real component of the second Weyl invariant $J$ alone are shown to constitute the largest independent set of invariants to degree five for this class. Explicit syzygies are given for other invariants up to this degree. It is argued that this set constitutes the largest functionally independent set to any degree for this class, and some physical consequences of the syzygies are explored.Comment: 19 pages. To appear in Computer Physics Communications Thematic Issue on "Computer Algebra in Physics Research". Uses Maple2e.st