Vast Parameter Research Articles

Use of a Segmented Cell for the Development of PGM-Free Cathode Catalyst Layers for Polymer Electrolyte Fuel Cells

To reduce the Pt content in polymer electrolyte fuel cells (PEFC), the development of highly active and stable platinum group metal (PGM)-free catalysts for oxygen reduction reaction (ORR) is crucial. In the last decade a significant amount of work has been published focusing on PGM-free catalyst synthesis and characterization, and excellent RDE performances were recently achieved.1,2 A smaller, but still considerable number of work showing H2/air and/or H2/O2 PEFC polarization curves data is available.3–5 However, only few papers have been published that report on systematic durability studies,6,7 and electrode layer fabrication optimization to maximize the PEFC performance.8 Durability assessment and electrode fabrication optimization are demanding tasks in terms of time and effort for execution of the experiments because of the high number of variables that can influence the performance. Examples for such variables are the ionomer-to-catalyst ratio, catalyst loading, catalyst deposition method, ink concentration and composition in terms of solvents. Furthermore, each catalyst may have a different optimum electrode fabrication condition. This means that a separate optimization is necessary for each newly developed PGM-free catalyst showing good potential to perform well in PEFCs. Due to this vast parameter space, no systematic studies about the catalyst layer optimization related to the final PEFC performance, have been published so far. Recently, NREL’s fuel cell group has upgraded its segmented cell with improved electrical signal measuring features, and an innovative segmented parallel straight channel flow field. The segmented cell hardware consists of a 50 cm2 square electrode active area, divided into a total of 121 segments (11 x 11, each segment = 0.413 cm2).9 Each single segment has a current collector and a voltage sensor, which enables the separate measurement of current density and potential. The upgraded segmented cell can operate in both active (each segment connected to a single load duty) and passive (applying a single electric load and collecting the signals through each single segment) modes. With these features, the segmented cell becomes an attractive tool to implement a fast and systematic screening for PGM-free catalyst layers that were fabricated differently or with different material sets. Screening and optimization entails simultaneously testing pieces of electrodes synchronously in the same cell. BOL performance and the durability behavior of different catalyst layers can also be assessed in parallel. In this work, we report on the utilization of NREL’s segmented cell for screening PGM-free cathode catalyst. The results demonstrate the effectiveness of the segmented cell in accelerating preliminary optimization studies. First, we demonstrate segmented cell operation under differential cell conditions, eliminating the spatial effects due to the position of the segments along the flow field. In particular, we demonstrate homogeneous current distribution in two sets of seven segments located along a straight channel section of the cell. The current densities match those of a 5 cm2 differential single cell with an identical PGM-free electrode. We further demonstrate the parallel screening of (i) two different PGM-free catalysts and (ii) catalyst layers with different ionomer contents (15 – 30 – 45 %). The results for (i) reveal significantly different behavior of the two catalysts in terms of activation time and short-term durability. The results for (ii) indicate a clearly increasing performance trend with increasing ionomer content, i.e. performance increases in the order 45 > 30 > 15 % ionomer content. The data establish the segmented cell as a useful tool for PGM-free electrode optimization and durability screening. More details, and further applications of the segmented cell to PGM-free electrode optimization and durability will be given in the Meeting presentation. Figure 1. a) Polarization curves measured on 7 segments in one row of the segmented cell, compared to the polarization curve measured on a single 5 cm2 differential cell. Electrodes are fabricated in the same way using the same PGM-free catalyst. b) H2/air polarization curves measured in the segmented cell for electrodes fabricated using the same PGM-free catalyst with different ionomer wt. %.

Application of Parameter Optimization to Search for Oscillatory Mass-Action Networks Using Python

Biological systems can be described mathematically to model the dynamics of metabolic, protein, or gene-regulatory networks, but locating parameter regimes that induce a particular dynamic behavior can be challenging due to the vast parameter landscape, particularly in large models. In the current work, a Pythonic implementation of existing bifurcation objective functions, which reward systems that achieve a desired bifurcation behavior, is implemented to search for parameter regimes that permit oscillations or bistability. A differential evolution algorithm progressively approximates the specified bifurcation type while performing a global search of parameter space for a candidate with the best fitness. The user-friendly format facilitates integration with systems biology tools, as Python is a ubiquitous programming language. The bifurcation–evolution software is validated on published models from the BioModels Database and used to search populations of randomly-generated mass-action networks for oscillatory dynamics. Results of this search demonstrate the importance of reaction enrichment to provide flexibility and enable complex dynamic behaviors, and illustrate the role of negative feedback and time delays in generating oscillatory dynamics.

Facile Room‐Temperature Anion Exchange Reactions of Inorganic Perovskite Quantum Dots Enabled by a Modular Microfluidic Platform

AbstractIn an effort to produce the materials of next‐generation photoelectronic devices, postsynthesis halide exchange reactions of perovskite quantum dots are explored to achieve enhanced bandgap tunability. However, comprehensive understanding of the multifaceted halide exchange reactions is inhibited by their vast relevant parameter space and complex reaction network. In this work, a facile room‐temperature strategy is presented for rapid halide exchange of inorganic perovskite quantum dots. A comprehensive understanding of the halide exchange reactions is provided by isolating reaction kinetics from precursor mixing rates utilizing a modular microfluidic platform, Quantum Dot Exchanger (QDExer). The effects of ligand composition and halide salt source on the rate and extent of the halide exchange reactions are illustrated. This fluidic platform offers a unique time‐ and material‐efficient approach for studies of solution phase‐processed colloidal nanocrystals beyond those studied here and may accelerate the discovery and optimization of next‐generation materials for energy technologies.

Tripled yield in direct-drive laser fusion through statistical modelling.

Focusing laser light onto a very small target can produce the conditions for laboratory-scale nuclear fusion of hydrogen isotopes. The lack of accurate predictive models, which are essential for the design of high-performance laser-fusion experiments, is a major obstacle to achieving thermonuclear ignition. Here we report a statistical approach that was used to design and quantitatively predict the results of implosions of solid deuterium-tritium targets carried out with the 30-kilojoule OMEGA laser system, leading to tripling of the fusion yield to its highest value so far for direct-drive laser fusion. When scaled to the laser energies of the National Ignition Facility (1.9 megajoules), these targets are predicted to produce a fusion energy output of about 500 kilojoules-several times larger than the fusion yields currently achieved at that facility. This approach could guide the exploration of the vast parameter space of thermonuclear ignition conditions and enhance our understanding of laser-fusion physics.

Parametric Modal Decomposition of Dynamic Stall

The chordwise, unsteady pressure difference field for harmonically oscillating airfoils operating in the attached flow, light dynamic stall, and deep dynamic stall regimes has been modally decomposed to identify well-defined modal structures that persist across a vast parameter space of pitching parameters (i.e., reduced frequency, mean angle of attack, and oscillation amplitude). The pressure difference fields were acquired at a chord Reynolds number and Mach number of and , respectively, demonstrating results applicable to rotorcraft flight conditions. Notably, only four mode shapes were required to reconstruct the aerodynamic loads anywhere within the parameter space. Likewise, the same mode shapes showed a remarkable ability to reconstruct the aerodynamic loads of other (non-native) airfoil geometries with a similar precision. The parametric modal decomposition outlined provides a foundation to elucidate the physics of the dynamic stall phenomenon as well as reduced-order modeling techniques for the aerodynamic loading.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

AbstractThere are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work. However, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. A combination of density functional theory (DFT) and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiments. As a proof of concept, this methodology is applied to predict the interlayer distance and band gap of bilayer heterostructures. The methods quickly and accurately predict these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of vdW heterostructures to identify new hybrid materials with useful and interesting properties.

Comprehensive Study of Ejecta-companion Interaction for Core-collapse Supernovae in Massive Binaries

Abstract We carry out a comprehensive study of supernova ejecta-companion interaction in massive binary systems. Our aim is to physically understand the kinematics of the interaction and predict observational signatures. To do this, we perform simulations over a vast parameter space of binary configurations, varying the masses of the progenitor and companion, structure of the companion, explosion energy, and orbital separation. Our results were not so consistent with the classical models developed by Wheeler et al. (1975), sometimes deviating by an order of magnitude. Therefore, we construct an alternative simple model that explains the simulated results reasonably well and that can be used to estimate impact velocities for arbitrary explosion profiles and companion star structures. We then investigate the long term evolution after the supernova, where the companion can be inflated by the energy injected into the star. We find that the companion can become more than an order of magnitude overluminous immediately after the supernova but quickly fades away after ∼10 years and returns to its original luminosity in about a thermal timescale of the star. Finally, we also discuss the possible surface contamination of heavy elements from the slower ejecta.

Achieving Reproducibility and Closed-Loop Automation in Biological Experimentation with an IoT-Enabled Lab of the Future

A robotic cloud laboratory driven by a state-of-the-art unified laboratory operating system integrates automated hardware, humans, and sensors. This lab of the future system enables researchers to transparently and collaboratively create, optimize, and organize biological experiments to achieve more reproducible results, perform around-the-clock experimentation, and more efficiently navigate the vast parameter space of biology.

VBBinaryLensing: a public package for microlensing light-curve computation

The computation of microlensing light curves represents a bottleneck for the modeling of planetary events, making broad searches in the vast parameter space of microlensing extremely time-consuming. The release of the first version of VBBinaryLensing (based on the advanced contour integration method presented in Bozza(2010)) has represented a considerable advance in the field, with the birth of several analysis platforms running on this code. Here we present the version 2.0 of VBBinaryLensing, which contains several upgrades with respect to the first version, including new decision trees that introduce important optimizations in the calculations.

Task-based design of a synthetic-collimator SPECT system used for small animal imaging.

In traditional multipinhole SPECT systems, image multiplexing - the overlapping of pinhole projection images - may occur on the detector, which can inhibit quality image reconstructions due to photon-origin uncertainty. One proposed system to mitigate the effects of multiplexing is the synthetic-collimator SPECT system. In this system, two detectors, a silicon detector and a germanium detector, are placed at different distances behind the multipinhole aperture, allowing for image detection to occur at different magnifications and photon energies, resulting in higher overall sensitivity while maintaining high resolution. The unwanted effects of multiplexing are reduced by utilizing the additional data collected from the front silicon detector. However, determining optimal system configurations for a given imaging task requires efficient parsing of the complex parameter space, to understand how pinhole spacings and the two detector distances influence system performance. In our simulation studies, we use the ensemble mean-squared error of the Wiener estimator (EMSEW ) as the figure of merit to determine optimum system parameters for the task of estimating the uptake of an 123 I-labeled radiotracer in three different regions of a computer-generated mouse brain phantom. The segmented phantom map is constructed by using data from the MRM NeAt database and allows for the reduction in dimensionality of the system matrix which improves the computational efficiency of scanning the system's parameter space. To contextualize our results, the Wiener estimator is also compared against a region of interest estimator using maximum-likelihood reconstructed data. Our results show that the synthetic-collimator SPECT system outperforms traditional multipinhole SPECT systems in this estimation task. We also find that image multiplexing plays an important role in the system design of the synthetic-collimator SPECT system, with optimal germanium detector distances occurring at maxima in the derivative of the percent multiplexing function. Furthermore, we report that improved task performance can be achieved by using an adaptive system design in which the germanium detector distance may vary with projection angle. Finally, in our comparative study, we find that the Wiener estimator outperforms the conventional region of interest estimator. Our work demonstrates how this optimization method has the potential to quickly andefficiently explore vast parameter spaces, providing insight into the behavior of competing factors, which are otherwise very difficult to calculate and study using other existing means.

A particle swarm approach for tuning washout algorithms in vehicle simulators

The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms. These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fidelity indicators, so that the process can be automated. A genetic algorithm (GA) has been recently proposed to perform this process, with promising results. Following this approach, this paper proposes applying Particle Swarm Optimization (PSO) to solve the MCA tuning problem. The PSO-based proposed solution is assessed using the classical washout MCA, comparing its performance, convergence and correctness against the GA-based solution. Results show that a PSO-based tuning of MCA can provide better results and converges faster than a GA-based one. In addition, PSO is easier to set-up than GA, since only one parameter of the optimization algorithm itself (the number of particles) needs to be set-up, instead of a minimum of four in the case of the GA.

Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR

The mechanistic Target of Rapamycin (mTOR) signalling network is an evolutionarily conserved network that controls key cellular processes, including cell growth and metabolism. Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the mTOR network harbours complex interactions and feedback loops. The DEP domain-containing mTOR-interacting protein (DEPTOR) was recently identified as an endogenous inhibitor of both mTORC1 and 2 through direct interactions, and is in turn degraded by mTORC1/2, adding an extra layer of complexity to the mTOR network. Yet, the dynamic properties of the DEPTOR-mTOR network and the roles of DEPTOR in coordinating mTORC1/2 activation dynamics have not been characterised. Using computational modelling, systems analysis and dynamic simulations we show that DEPTOR confers remarkably rich and complex dynamic behaviours to mTOR signalling, including abrupt, bistable switches, oscillations and co-existing bistable/oscillatory responses. Transitions between these distinct modes of behaviour are enabled by modulating DEPTOR expression alone. We characterise the governing conditions for the observed dynamics by elucidating the network in its vast multi-dimensional parameter space, and develop strategies to identify core network design motifs underlying these dynamics. Our findings provide new systems-level insights into the complexity of mTOR signalling contributed by DEPTOR.

Void growth in single crystal Copper-an atomistic modeling and statistical analysis study

One of the failure mechanisms in ductile materials is growth and coalescence of pre-existing voids. In view of this, we attempt to obtain atomistic insights into the prevailing mechanisms of void growth in a representative ductile material, namely Copper, using molecular dynamics simulations. In addition to shedding light on the observed length scale effects and dislocation mechanisms, we also elucidate how atomistic simulations can inform continuum-based models of failure and provide fodder for bridging different length scales. By performing a series of over 150 molecular dynamics simulations, we also try to decode the interplay between mechanical properties and void growth, and investigate the role of heterogeneity in void distribution (in terms of void size and placement) in affecting the strength of the material. Coupled with a comprehensive global sensitivity analysis technique, we explore configuration–property relationships in a subset of vast parameter space and highlight the importance of random nature of void distribution (along with some critical statistical parameters) in any successful theory of fracture.

Automated design of multi junction solar cells by genetic approach: Reaching the [formula omitted] efficiency target

The proper design of the multi-junction solar cell (MJSC) requires the optimisation search through the vast parameter space, with parameters for the proper operation quite often being constrained, like the current matching throughout the cell. Due to high complexity number of MJSC device parameters might be huge, which makes it a demanding task for the most of the optimising strategies based on gradient algorithm. One way to overcome those difficulties is to employ the global optimisation algorithms based on the stochastic search. We present the procedure for the design of MJSC based on the heuristic method, the genetic algorithm, taking into account physical parameters of the solar cell as well as various relevant radiative and non-radiative losses. In the presented model, the number of optimising parameters is 5M+1 for a series constrained M-junctions solar cell. Diffusion dark current, radiative and Auger recombinations are taken into account with actual ASTM G173-03 Global tilted solar spectra, while the absorption properties of individual SCs were calculated using the multi band k·p Hamiltonian. We predicted the efficiencies in case of M=4 to be 50.8% and 55.2% when all losses are taken into account and with only radiative recombination, respectively.

THE MATHEMATICAL EVALUATION OF THE ELECTROCHEMICAL DETERMINATION OF DIQUAT ON CONDUCTING POLYMER, MODIFIED BY VO2/ VO2+ OXIDATION PAIR

The possibility of diquat electrochemical detection, realized by conducting polymer electrode, doped by vanadium dioxide, was analyzed by theoretical point of view. It was shown, that the conducting polymer composite with VO 2 may serve as an excellent electrode modifier for Diquat electrochemical detection. The steady-state may be maintained stable in the vast parameter region. The possibility for electrochemical instabilities in this system has also been studied.

Design principles for wave plate metasurfaces using plasmonic L-shaped nanoantennas

Plasmonic L-shaped antennas are an important building block of metasurfaces and have been used to fabricate ultra-thin wave plates. In this work we present principles that can be used to design wave plates at a wavelength of choice and for diverse application requirements using arrays of L-shaped plasmonic antennas. We derive these design principles by studying the behavior of the vast parameter space of these antenna arrays. We show that there are two distinct regimes: a weak inter-particle coupling and a strong inter-particle coupling regime. We describe the behavior of the antenna array in each regime with regards to wave plate functionality, without resorting to approximate theoretical models. Our work is the first to explain these design principles and serves as a guide for designing wave plates for specific application requirements using plasmonic L-shaped antenna arrays.

Probing a classically conformal B−L model with gravitational waves

We study the cosmological history of the classical conformal $B-L$ gauge extension of the standard model, in which the physical scales are generated via the Coleman-Weinberg-type symmetry breaking. Especially, we consider the thermal phase transition of the U$(1)_{B-L}$ symmetry in the early universe and resulting gravitational-wave production. Due to the classical conformal invariance, the phase transition tends to be a first-order one with ultra-supercooling, which enhances the strength of the produced gravitational waves. We show that, requiring (1) U$(1)_{B-L}$ is broken after the reheating, (2) the $B-L$ gauge coupling does not blow up below the Planck scale, (3) the thermal phase transition completes in almost all the patches in the universe, the gravitational wave spectrum can be as large as $\Omega_{\rm GW} \sim 10^{-8}$ at the frequency $f \sim 0.01$-$1$Hz for some model parameters, and a vast parameter region can be tested by future interferometer experiments such as eLISA, LISA, BBO and DECIGO.

Natural NMSSM after LHC Run I and the Higgsino dominated dark matter scenario

We investigate the impact of the direct searches for SUSY at LHC Run I on the naturalness of the Next-to-Minimal Supersymmetric Standard Model (NMSSM). For this end, we first scan the vast parameter space of the NMSSM to get the region where the fine tuning measures $\Delta_Z$ and $\Delta_h$ at the electroweak scale are less than about 50, then we implement by simulations the constraints of the direct searches on the parameter points in the region. Our results indicate that although the direct search experiments are effective in excluding the points, the parameter intervals for the region and also the minimum reaches of $\Delta_Z$ and $\Delta_h$ are scarcely changed by the constraints, which implies that the fine tuning of the NMSSM does not get worse after LHC Run I. Moreover, based on the results we propose a natural NMSSM scenario where the lightest neutralino $\tilde{\chi}_1^0$ as the dark matter (DM) candidate is Higgsino-dominated. In this scenario, $\Delta_Z$ and $\Delta_h$ may be as low as 2 without conflicting with any experimental constraints, and intriguingly $\tilde{\chi}_1^0$ can easily reach the measured DM relic density due to its significant Singlino component. We exhibit the features of the scenario which distinguish it from the other natural SUSY scenario, including the properties of its neutralino-chargino sector and scalar top quark sector. We emphasize that the scenario can be tested either through searching for $3 l + E_T^{miss}$ signal at 14 TeV LHC or through future DM direct detection experiments.

Testing the light dark matter scenario of the MSSM at the LHC

In the light dark matter (DM) scenario of the MSSM, the DM relic density puts non-trivial requirements on the spectrum of supersymmetric particles. As a result, the direct search for multi-lepton signals at the LHC has great impact on the scenario. In this work, we concentrate on the searches for sleptons and electroweak-inos at the LHC, and investigate the constraints on the light DM scenario from the 8 TeV LHC data as well as the capability of the 14 TeV LHC to test the scenario. We first get the samples of the scenario by scanning the vast parameter space of the MSSM and considering some easily available constraints, such as those from the DM relic density, the LUX experiment and the Higgs searches at colliders. Then for the surviving samples, we simulate the $2l+E_T^{miss}$ signal from slepton pair production process and the $2l+E_T^{miss}$ and $3l+E_T^{miss}$ signals from chargino and neutralino associated production processes at both the 8 TeV LHC and the 14 TeV LHC. Our simulations indicate that the 8 TeV LHC has excluded a sizable portion of the samples, and the 14 TeV LHC can be even much more powerful in testing the scenario. For example, in case that no multi-lepton signals are observed at the 14 TeV LHC, most samples of the light DM scenario will be excluded, especially a lower limit on the lightest neutralino mass will be set at 42 GeV and 44 GeV with 30 $fb^{-1}$ and 100 $fb^{-1}$ data respectively, and this limit can be further pushed up to $55\ {\rm GeV}$ with 300 $fb^{-1}$ data.

TheHubble Space TelescopeUV Legacy Survey of Galactic Globular Clusters – V. Constraints on formation scenarios

We build on the evidence provided by our Legacy Survey of Galactic globular clusters (GC) to submit to a crucial test four scenarios currently entertained for the formation of multiple stellar generations in GCs. The observational constraints on multiple generations to be fulfilled are manifold, including GC specificity, ubiquity, variety, predominance, discreteness, supernova avoidance, p-capture processing, helium enrichment and mass budget. We argue that scenarios appealing to supermassive stars, fast rotating massive stars and massive interactive binaries violate in an irreparable fashion two or more among such constraints. Also the scenario appealing to AGB stars as producers of the material for next generation stars encounters severe difficulties, specifically concerning the mass budget problem and the detailed chemical composition of second generation stars. We qualitatively explore ways possibly allowing one to save the AGB scenario, specifically appealing to a possible revision of the cross section of a critical reaction rate destroying sodium, or alternatively by a more extensive exploration of the vast parameter space controlling the evolutionary behavior of AGB stellar models. Still, we cannot ensure success for these efforts and totally new scenarios may have to be invented to understand how GCs formed in the early Universe.