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At least 757 records · Page 42

Urban wash-off of tire wear particles

Tire wear particles (TWPs) are an important class of microplastics due to their toxicity and abundance. Because most TWPs are generated on impervious road surfaces, urban wash-off is the critical first phase of waterborne transport from their zone of production to stormwater drainage. However, little is known about the driving factors behind their mobilization. In this study, we use a rainfall simulator to investigate how surface roughness, rainfall intensity, and surface slope affect wash-off behaviors of TWPs. We also analyze how the size and shape of mobilized TWPs change over the course of simulated storm events. We found that low surface roughness, high rainfall intensity (most significant factor), and low slope result in the most rapid conveyance of TWP load. On average, large particles (>1000 µm) travelled faster than small particles (<125 µm). Particle shape explained a very small amount of variance in TWP wash-off velocity but was found to be more important under higher surface roughness conditions. In addition to wash-off velocity, we found similar conditions controlled the percent mobilization of TWPs. Low surface roughness and high rainfall intensity resulting in higher TWP wash-off rates is consistent with mineral sediment wash-off behavior. Conversely, low surface slope and large particle size leading to faster conveyance is directly opposed to mineral sediment wash-off. Our findings suggest drag-dominated flow and that sufficient runoff depth is the most important parameter governing TWP wash-off. These findings are important first steps to understanding wash-off behaviors of TWPs and informing future modeling efforts and mitigation strategies.

13 HYDRO ENERGY↗

Grain2mesh: A Python and cubit mesh generator from unprocessed mesoscale images

Predicting bulk behavior from microscale features constitutes a key objective in multiscale modeling research, often involving numerical models composed of finite elements that capture the diversity of constituent phases, shapes, and orientations within the material. The Grain2mesh toolbox allows the user to input unprocessed mesoscopic images for automatic segmentation, pre-processing, quality control, and numerical mesh generation. The numerical mesh generation incorporates Cubit routines to generate robust multi-phase mesh structure for use in computational mechanics solvers. The python classes developed contain detailed documentation and examples to support standard usage and case-specific alternative options.

58 GEOSCIENCES↗

Interface Defect Engineering for Improved Graphene-Oxide-Semiconductor Junction Photodetectors

The deeply depleted graphene-oxide-semiconductor (D 2 GOS) junction detector provides an effective architecture for photodetection, enabling direct readout of photogenerated charge. Because of an inherent gain mechanism proportional to graphene’s high mobility (μ), this detector architecture exhibits large responsivities and signal-to-noise ratios (SNR). The ultimate sensitivity of the D2GOS junction detector may be limited, however, because of the generation of dark charge originating from interface states at the semiconductor/dielectric junction. Here, we examine the performance limitations caused by dark charge and demonstrate its mitigation via the creation of low interface defect junctions enabled by surface passivation. The resulting devices exhibit responsivities exceeding 10 000 A/W—a value which is 10× greater than that of analogous devices without the passivating thermal oxide. Finally, with cooling of the detector, the responsivity further increases to over 25 000 A/W, underscoring the impact of surface generation on performance and thus the necessity of minimizing interfacial defects for this class of photodetector.

2D detector↗

Diversity-oriented synthesis of polymer membranes with ion solvation cages

Microporous polymers feature shape-persistent free volume elements (FVEs), which are permeated by small molecules and ions when used as membranes for chemical separations, water purification, fuel cells, and batteries. It remains a significant challenge to identify FVEs with analyte specificity, due to difficulties in generating microporous polymer libraries with sufficient diversity for screening their properties. Here, we describe a diversity-oriented synthetic (DOS) strategy for microporous polymer membranes from which we identified those whose FVEs serve as solid solvation cages for lithium ions (Li + ). Furthermore, key elements of our strategy included diversification of bis(catechol)-type monomers via multi-component Mannich reactions to introduce Li+-coordinating functionality within individual FVEs, topology-enforcing polymerizations for generating macromolecular skeletal diversity for networking FVEs into different pore architectures, and several classes of on-polymer reactions for diversifying pore geometries and dielectric properties. Lead candidate polymer membranes featuring explicit ion solvation cages exhibited both higher ionic conductivity and higher cation transference number than control membranes where FVEs were aspecific, which indicates conventional bounds for membrane permeability and selectivity for ion transport can be overcome.4 These advantages are tied to enhanced Li + partitioning from the electrolyte when the cages are present, higher diffusion barriers for anions within the pores, and network-enforced restrictions on the number of solvent molecules bound to Li+ by comparison to the bulk electrolyte, which reduces the effective mass of the working ion. Such membranes show promise as anode-stabilizing interlayers in high-voltage lithium-metal batteries for electric mobility.

42 ENGINEERING↗

Inverse design of nanoporous crystalline reticular materials with deep generative models

Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. Here, we show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.

36 MATERIALS SCIENCE↗

A long-pulse repetitive operation magnetically insulated transmission line oscillator

The improved magnetically insulated transmission line oscillator (MILO) is a gigawatt-class L-band high power microwave tube. It has allowed us to generate 3.1 GW pulse of 40 ns duration in the single-pulse operation and 500 MW pulse of 25 ns duration in the repetition rate operation. However, because of the severe impedance mismatch, the power conversion efficiency is only about 4% in the repetition rate operation. In order to eliminate the impedance mismatch and obtain repetitive long-pulse high-power microwave (HPM), a series of experiments are carried out and the recent progress is presented in this paper. In the single-pulse operation, when the diode voltage is 466 kV and current is 41.6 kA, the radiated microwave power is above 2.2 GW, the pulse duration is above 102 ns, the microwave frequency is about 1.74 GHz, and the power conversion efficiency is about 11.5%. In the repetition rate operation, under the condition of the diode voltage about 400 kV, beam current about 38 kA, the radiated microwave power is about 1.0 GW, the pulse duration is about 85 ns. Moreover, the radiated microwave power and the pulse duration decline little by little when the shot numbers increase gradually. The experimental results show that the impedance matching is a vital factor for HPM systems and one of the major technical challenges is to improve the cathode for the repetition rate operation MILO.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

RING-APODIZED VORTEX CORONAGRAPHS FOR OBSCURED TELESCOPES. I. TRANSMISSIVE RING APODIZERS

The vortex coronagraph (VC) is a new generation small inner working angle (IWA) coronagraph currently offered on various 8 m class ground-based telescopes. On these observing platforms, the current level of performance is not limited by the intrinsic properties of actual vortex devices, but by wavefront control residuals and incoherent background (e.g., thermal emission of the sky), or the light diffracted by the imprint of the secondary mirror and support structures on the telescope pupil. In the particular case of unfriendly apertures (mainly large central obscuration) when very high contrast is needed (e.g., direct imaging of older exoplanets with extremely large telescopes or space-based coronagraphs), a simple VC, like most coronagraphs, cannot deliver its nominal performance because of the contamination due to the diffraction from the obscured part of the pupil. Here, we propose a novel yet simple concept that circumvents this problem. We combine a vortex phase mask in the image plane of a high-contrast instrument with a single pupil-based amplitude ring apodizer, tailor-made to exploit the unique convolution properties of the VC at the Lyot-stop plane. We show that such a ring-apodized vortex coronagraph (RAVC) restores the perfect attenuation property of the VC regardless of the size of the central obscuration, and for any (even) topological charge of the vortex. More importantly, the RAVC maintains the IWA and conserves a fairly high throughput, which are signature properties of the VC.

79 ASTRONOMY AND ASTROPHYSICS↗

A viable logarithmic f(R) model for inflation

Inflation in the framework of f(R) modified gravity is revisited. We study the conditions that f(R) should satisfy in order to lead to a viable inflationary model in the original form and in the Einstein frame. Based on these criteria we propose a new logarithmic model as a potential candidate for f(R) theories aiming to describe inflation consistent with observations from Planck satellite (2015). The model predicts scalar spectral index 0.9615<n{sub s}<0.9693 in agreement with observation and tensor-to-scalar ratio r of order 10{sup −3}. Furthermore, we show that for a class of models, a natural coupling between inflation and a scalar boson is generated through the minimal coupling between gravity and matter fields and a reheating temperature less that 10{sup 9} GeV is obtained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Performance of the ATLAS RPC detector and Level-1 muon barrel trigger at √(s)=13 TeV

The ATLAS experiment at the Large Hadron Collider (LHC) employs a trigger system consisting of a first-level hardware trigger (L1) and a software-based high-level trigger. The L1 muon trigger system selects muon candidates, assigns them to the correct LHC bunch crossing and classifies them into one of six transverse-momentum threshold classes. The L1 muon trigger system uses resistive-plate chambers (RPCs) to generate the muon-induced trigger signals in the central (barrel) region of the ATLAS detector. The ATLAS RPCs are arranged in six concentric layers and operate in a toroidal magnetic field with a bending power of 1.5 to 5.5 Tm. The RPC detector consists of about 3700 gas volumes with a total surface area of more than 4000 m2. This paper reports on the performance of the RPC detector and L1 muon barrel trigger using 60.8 fb-1 of proton-proton collision data recorded by the ATLAS experiment in 2018 at a centre-of-mass energy of 13 TeV. Detector and trigger performance are studied using Z boson decays into a muon pair. Measurements of the RPC detector response, efficiency, and time resolution are reported. Measurements of the L1 muon barrel trigger efficiencies and rates are presented, along with measurements of the properties of the selected sample of muon candidates. Measurements of the RPC currents, counting rates and mean avalanche charge are performed using zero-bias collisions. Finally, RPC detector response and efficiency are studied at different high voltage and front-end discriminator threshold settings in order to extrapolate detector response to the higher luminosity expected for the High Luminosity LHC.

47 OTHER INSTRUMENTATION↗

Scaling universality at the dynamic vortex Mott transition

The cleanest way to observe a dynamic Mott insulator-to-metal transition (DMT) without the interference from disorder and other effects inherent to electronic and atomic systems, is to employ the vortex Mott states formed by superconducting vortices in a regular array of pinning sites. Here, we report the critical behavior of the vortex system as it crosses the DMT line, driven by either current or temperature. We find universal scaling with respect to both, expressed by the same scaling function and characterized by a single critical exponent coinciding with the exponent for the thermodynamic Mott transition. We develop a theory for the DMT based on the parity reflection-time reversal (PT) symmetry breaking formalism and find that the nonequilibrium-induced Mott transition has the same critical behavior as the thermal Mott transition. Our findings demonstrate the existence of physical systems in which the effect of a nonequilibrium drive is to generate an effective temperature and hence the transition belonging in the thermal universality class.

74 ATOMIC AND MOLECULAR PHYSICS↗

Power Profile Monitoring and Tracking Evolution of System-Wide HPC Workloads

The power & energy demands of HPC machines have grown significantly. Modern exascale HPC systems require tens of megawatts of combined power for computing resources and cooling facilities at full capacity. The current energy trend is not sustainable for future HPC systems, and there is a need to work toward the energy efficiency aspect of HPC performance. Energy awareness of the HPC applications at the job level is essential for running an efficient HPC system. This work aims to develop a pipeline to provide a production-level system-wide overview of the HPC workloads' power profile while handling evolving workloads exhibiting new power trends. We developed an open-set classification model for HPC jobs based on the properties of power profiles to continuously provide a system-wide holistic view of recently completed jobs. The pipeline helps continuously monitor the job-level power usage pattern of HPC and enables us to capture the new trends in applications' power behavior. We employed a comprehensive set of techniques to generate job-level data, custom-designed feature extraction methods to extract critical features from jobs' power profiles, clustering techniques powered by generative modeling, and open-set classification for identifying job profiles into known classes or an unknown set. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. We provide an analysis of the resulting clusters that characterize the power profile landscape of the Summit supercomputer from more than 60K jobs executed in a year. The open-set classification classifies the known data sets into known classes with high accuracy and identifies unknown data noints with over 85% accuracy.

Karimi, Ahmad Maroof↗

Microgrid Stability Definitions, Analysis, and Examples

This document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid Stability Definitions, Analysis, and Modeling [1], which defines concepts and identifies relevant issues related to stability in microgrids. In this paper, definitions and classification of microgrid stability are presented and discussed, considering pertinent microgrid features such as voltage-frequency dependency, unbalancing, low inertia, and generation intermittency. A few examples are also presented, highlighting some of the stability classes defined in the paper. Further examples, along with discussions on microgrid components modeling and stability analysis tools can be found in the TF report.

Farrokhabadi, Mostafa↗

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchical clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.

60 APPLIED LIFE SCIENCES↗

Structural and Preclinical Studies of Computationally Designed Non-Nucleoside Reverse Transcriptase Inhibitors for Treating HIV infection

The clinical benefits of HIV-1 non-nucleoside reverse transcriptase (RT) inhibitors (NNRTIs) are hindered by their unsatisfactory pharmacokinetic (PK) properties along with the rapid development of drug-resistant variants. However, the clinical efficacy of these inhibitors can be improved by developing compounds with enhanced pharmacological profiles and heightened antiviral activity. We used computational and structure-guided design to develop two next-generation NNRTI drug candidates, compounds I and II, which are members of a class of catechol diethers. We evaluated the preclinical potential of these compounds in BALB/c mice because of their high solubility (510 µg/ml for compound I and 82.9 µg/ml for compound II), low cytotoxicity, and enhanced antiviral activity against wild-type (WT) HIV-1 RT and resistant variants. Additionally, crystal structures of compounds I and II with WT RT suggested an optimal binding to the NNRTI binding pocket favoring the high anti-viral potency. A single intraperitoneal dose of compounds I and II exhibited a prolonged serum residence time of 48 hours and concentration maximum (Cmax) of 4000- to 15,000-fold higher than their therapeutic/effective concentrations. These Cmax values were 4- to 15-fold lower than their cytotoxic concentrations observed in MT-2 cells. Compound II showed an enhanced area under the curve (0–last) and decreased plasma clearance over compound I and efavirenz, the standard of care NNRTI. Hence, the overall (PK) profile of compound II was excellent compared with that of compound I and efavirenz. Furthermore, both compounds were very well tolerated in BALB/c mice without any detectable acute toxicity. Taken together, these data suggest that compounds I and II possess improved anti-HIV-1 potency, remarkable in vivo safety, and prolonged in vivo circulation time, suggesting strong potential for further development as new NNRTIs for the potential treatment of HIV infection.

59 BASIC BIOLOGICAL SCIENCES↗

Supercritical Reforming of Wet Ethanol for High Efficiency Direct-injection Heavy-duty Compression-ignition Engines

The purpose of the research was to investigate the potential of using a mature bio-fuel in heavy-duty compression ignition engines. By co-optimizing both the fuel characteristics and engine system the potential for a superior outcome was demonstrated. The use of wet bio-ethanol eliminates the majority of the energy intensive distilling and dehydrating fuel production processes, which moves the fuel towards carbon neutral and also lowers the fuel costs. The relatively high water content of the resulting fuel is leveraged in the proposed novel combustion system by incorporating an integrated high efficiency exhaust waste heat recovery system. This results in significantly higher thermal efficiency. In addition the combustion system features low criteria pollutant emissions and the potential to reduce the initial cost of the engine system hardware. Substantial societal benefits are demonstrated through the co-optimization of the fuel and engine system. A computational proof-of-concept study has been performed to demonstrate the potential benefits of a novel wet ethanol heavy-duty compression ignition combustion system featuring integrated exhaust waste heat recovery. A combined in-cylinder closed cycle 3D computational fluid dynamics (CFD) - 1D engine system simulation approach was used. The models were validated to baseline engine data using diesel fuel and then applied to the wet ethanol study. The original concept was to maximize exhaust waste heat recovery through supercritical reforming of the wet ethanol fuel. Phase I simulation results indicated that the optimal solution for maximum engine efficiency gains were realized through maximizing thermo-mechanical recuperation with negligible fuel reformation. The results show the potential to achieve impressive gains in brake thermal efficiency (BTE) over the base diesel engine. The potential to increase BTE up to 20.9% over the base diesel engine was demonstrated, with even larger gains possible through reduced in-cylinder heat transfer losses. The majority of the efficiency gains were realized through integrated high efficiency exhaust waste heat recovery. The concept also has the potential to achieve future ultra-low NO x emissions standards and negligible engine-out soot emissions. The mixing controlled combustion of high temperature wet ethanol features relatively low engine-out NO x emissions without the need for exhaust gas recirculation (EGR). The soot free combustion enabled by the relatively high oxygen content of ethanol also allows for the use of stoichiometric mixing controlled combustion, which is not practical with diesel fuel. When stoichiometric combustion is used a simple passive 3-way catalyst can be used for exhaust emissions after-treatment and near zero tailpipe emissions. The Phase I simulations results have defined the system layout and requirements in preparation for the Phase II experimental proof-of-concept study. The potential applications of the research include most current applications of diesel engines. The Phase I study focused on heavy-duty on-highway class 8 trucks. However, virtually any application that requires highly efficient clean power generation would benefit from the novel engine system proposed. The results indicate substantial fuel cost savings and reduced greenhouse gas emissions with similar or reduced initial system hardware costs compared to modern diesel engine systems.

09 BIOMASS FUELS↗

Interface-Driven Chiral Magnetism in Ultrathin Metallic Ferromagnets: Towards Skyrmion Spintronics

Magnetic skyrmions are localized chiral magnetic textures in the form of nanoscale vortices or bubbles that are topologically protected from being ‘unwound’. Their topological nature gives rise to rich behaviors including ordered lattice formation, emergent electrodynamics and robust current-driven displacement at remarkably low current densities. However, magnetic skyrmions have so far been restricted to just a few materials and observed only at low temperatures, limiting the experimental accessibility and technological application of these unique topological objects. This project aimed to realize magnetic skyrmions at room temperature in a new class of engineered chiral ferromagnets that exploit broken mirror symmetry at interfaces to generate helical magnetic order. Ultrathin magnetic heterostructures were engineered and patterned into laterally-confined nanostructures in which skyrmions were stabilized, manipulated, and detected for the first time under ambient conditions. Static and dynamic properties were examined using advanced x-ray imaging to provide a fundamental understanding of topological magnetic phases. The interactions between magnetic skyrmions and electron charge and spin currents were studied to identify fundamental new physics and enable device applications. These experimental studies, supported by detailed modeling, will help launch a new subfield of skyrmion-based spintronics in which individual skyrmions can be used to encode, store, and transport information in high-performance, low power memory and logic devices.

36 MATERIALS SCIENCE↗

IDAES-PSE Software Tools for Optimizing Energy Systems and Market Interactions

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-eco-nomic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.

Laky, Daniel↗

Biological particle analysis by mass spectrometry

An instrument that analyzes the chemical composition of biological particles in aerosol or hydrosol form was developed. Efforts were directed toward the acquisition of mass spectra from aerosols of biomolecules and bacteria. The filament ion source was installed on the particle analysis by mass spectrometry system. Modifications of the vacuum system improved the sensitivity of the mass spectrometer. After the modifications were incorporated, detailed mass spectra of simple compounds from the three major classes of biomolecules, proteins, nucleic acids, and carbohydrates were obtained. A method of generating bacterial aerosols was developed. The aerosols generated were collected and examined in the scanning electron microscope to insure that the bacteria delivered to the mass spectrometer were intact and free from debris.

Vilker, V. L.↗