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At least 271 records · Page 15

Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy

Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds.

09 BIOMASS FUELS↗

Conductivity and Transference Number Determination Protocols for Solid Oxide Cell Materials

To standardize materials and component characterization for next generation hydrogen production and energy generation solid oxide cell (SOC) technologies, test protocols are being established to facilitate comparison across the numerous laboratories and research institutions where SOC development for application in solid oxide fuel cells (SOFCs) and solid oxide electrolyzes cells (SOEC) is conducted. This paper proposes guiding protocols for fundamental electrical properties characterization of SOC materials, including temperature- and oxygen partial pressure (pO 2 )-dependent conductivity measurements, and use of the electromotive force for determining the transference numbers, or contributions of each charge carrier (i.e., ions and electrons), to the total conductivity. The protocol for Archimedes density measurements is also provided as an integral technique to both of these methods.

25 ENERGY STORAGE↗

Alphavirus Particles Can Assemble with an Alternate Triangulation Number

Alphaviruses are spherical, enveloped RNA viruses primarily transmitted by mosquitoes, and cause significant arthritogenic and neurotropic disease in humans and livestock. Previous reports have shown that—in contrast to prototypical icosahedral viruses—alphaviruses incorporate frequent defects, and these may serve important functions in the viral life cycle. We confirm the genus-wide pleomorphism in live viral particles and extend our understanding of alphavirus assembly through the discovery of an alternate architecture of Eastern equine encephalitis virus (EEEV) particles. The alternate T = 3 icosahedral architecture differs in triangulation number from the classic T = 4 icosahedral organization that typifies alphaviruses, but the alternate architecture maintains the quasi-equivalence relationship of asymmetric units. The fusion spike glycoproteins are more loosely apposed in the T = 3 form with corresponding changes in the underlying capsid protein lattice. This alternate architecture could potentially be exploited in engineering alphavirus-based particles for delivery of alphaviral or other RNA.

59 BASIC BIOLOGICAL SCIENCES↗

Particle number size distributions (10-461 nm) measured by SMPS at the ENA site during the AGENA campaign

During the Aerosol Growth in the Eastern North Atlantic (AGENA) campaign at the ENA site, particle number size distributions ranging from 10.6 to 461 nm were measured using SMPS with a time resolution of 4 minutes. The local pollution was identified and flagged by the “pollutionflag” field, where 0 denotes the clean periods and 1 denotes the polluted periods.

54 ENVIRONMENTAL SCIENCES↗

Cell-Type-Specific Proteomics Analysis of a Small Number of Plant Cells by Integrating Laser Capture Microdissection with a Nanodroplet Sample Processing Platform

Plant organs and tissues contain multiple cell types, which are well organized in 3-dimensional structure to efficiently perform physiological functions such as homeostasis, response to environmental perturbation, pathogen infection. It is critically important to perform molecular measurements at the cell-type-specific level to discover mechanisms and unique features of cell populations that govern differentiation and respond to external perturbations. Although mass spectrometry-based proteomics has been demonstrated as an enabling discovery tool to study plant physiology, conventional approaches require millions of cells to generate robust biological conclusions. Such requirements mask the cell-to-cell heterogeneities and limit the comprehensive profiling of plant proteins at spatially resolved and cell-type-specific resolutions. This protocol describes a recently-developed proteomics workflow for studying a small number of plant cells by integrating laser capture microdissection, microfluidic nanodroplet-based sample preparation, with ultrasensitive liquid chromatography-mass spectrometry. Using poplar as a model tree species, we provide detailed protocols, including plant tissue harvest, tissue preparation, cryosectioning, laser microdissection, protein digestion, mass spectrometry measurement, and data analysis. We show the workflow enables the precise identification and quantification of thousands of proteins from hundreds of isolated plant root and leaf cells.

59 BASIC BIOLOGICAL SCIENCES↗

Enhanced Carbon‐Carbon Coupling at Interfaces with Abrupt Coordination Number Changes

Abstract Cu‐catalyzed electrochemical CO 2 reduction reaction (CO 2 RR) produces multi‐carbon (C 2+ ) chemicals with considerable selectivities and activities, yet required high overpotentials impede its practical application. Here, we design interfaces with abrupt coordination number (CN) changes that greatly reduce the applied potential for achieving high C 2+ Faradaic efficiency (FE). Encouraged by the mechanistic finding that the coupling between *CO and *CO(H) is the most probable C−C bond formation path, we use Cu 2 O‐ and Cu‐phthalocyanine‐derived Cu (OD−Cu and PD−Cu) to build the interface. Using operando X‐ray absorption spectroscopy (XAS), we find that the Cu CN of OD−Cu is ~11, favoring CO* adsorption, while the PD−Cu has a COH*‐favorable CN of ~4. Operando Raman spectroscopy revealed that the interfaces with abrupt CN changes promote *OCCOH formation. As a result, the designed catalyst achieves a C 2+ FE of 85±2 % at 220 mA cm −2 in a zero‐gap CO 2 electrolyzer. An improvement of C 2+ FE by 3 times is confirmed at the low potential regime where the current density is 60–140 mA cm −2 , compared to bare OD−Cu. We report a 45‐h stable CO 2 RR operation at 220 mA cm −2 , producing a C 2+ product FE of ~80 %.

Chemistry↗

Number of sites-based solver for determining coverages from steady-state mean-field micro-kinetic models

Kinetic models parameterized by ab-initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady-state coverages and rates of mean-field micro-kinetic models. As implemented in the open-source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root-finding algorithm to determine the coverage of all intermediates through the steady-state expressions, constraining all coverages to be non-negative and to properly sum to unity. Though intuitive, this root-finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean-field steady-state micro-kinetic model in the space of number of sites instead of solving it in the space of coverages. Here, we transform the constrained root-finding problem to an unconstrained least-squares minimization problem, leading to significantly improved convergence in solving micro-kinetic models and thus enabling the efficient study of more complex catalytic reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhanced Hydrogen Evolution Catalysis of Pentlandite due to the Increases in Coordination Number and Sulfur Vacancy during Cubic‐Hexagonal Phase Transition

Abstract The search for new phases is an important direction in materials science. The phase transition of sulfides results in significant changes in catalytic performance, such as MoS 2 and WS 2 . Cubic pentlandite [cPn, (Fe, Ni) 9 S 8 ] can be a functional material in batteries, solar cells, and catalytic fields. However, no report about the material properties of other phases of pentlandite exists. In this study, the unit‐cell parameters of a new phase of pentlandite, sulfur‐vacancy enriched hexagonal pentlandite (hPn), and the phase boundary between cPn and hPn are determined for the first time. Compared to cPn, the hPn shows a high coordination number, more sulfur vacancies, and high conductivity, which result in significantly higher hydrogen evolution performance of hPn than that of cPn and make the non‐nano rock catalyst hPn superior to other most known nanosulfide catalysts. The increase of sulfur vacancies during phase transition provides a new approach to designing functional materials.

Chemistry↗

Characterizing the Number of Kinesin Motors Bound to Microtubules in the Gliding Motility Assay Using FLIC Microscopy [Book Chapter]

Intracellular transport by kinesin motors moving along their associated cytoskeletal filaments, microtubules, is essential to many biological processes. This active transport system can be reconstituted in vitro with the surface-adhered motors transporting the microtubules across a planar surface. In this geometry, the kinesin-microtubule system has been used to study active self-assembly, to power microdevices, and to perform analyte detection. Fundamental to these applications is the ability to characterize the interactions between the surface tethered motors and microtubules. Further, Fluorescence Interference Contrast (FLIC) microscopy can illuminate the height of the microtubule above a surface, which, at sufficiently low surface densities of kinesin, also reveals the number, locations, and dynamics of the bound motors.

59 BASIC BIOLOGICAL SCIENCES↗

Probing lepton number violation and Majorana nature of neutrinos at the LHC

Abstract Observation of lepton number (L) violation by two units at colliders would provide evidence for the Majorana nature of neutrinos. We study signals ofL-violation in the context of two popular models of neutrino masses, the type-II seesaw model and the Zee model, wherein small neutrino masses arise at the tree-level and one-loop level, respectively. We focus onL-violation signals at the LHC arising through the processpp→ℓ ± ℓ ′± + jets within these frameworks. We obtain sensitivity toL-violation in the type-II seesaw model for triplet scalar masses up to 700 GeV and in the Zee model for charged scalar masses up to 4.8 TeV at the high-luminosity LHC with an integrated luminosity of 3 ab −1 .

Physics↗

$t\overline{t}b\overline{b}$ at NLO precision in a variable flavor number scheme

Top-quark pair production in association with two $b$-jets is computed at next-to-leading order QCD precision, including effects of the $b$-quark mass, and matched to a $t\overline{t}$+jets simulation in a variable flavor number scheme. The Monte Carlo realization of this method, called fusing, consistently embeds the four-flavor calculation in a particle-level event generator. As a first phenomenological application, we present observables relevant to the data-driven estimation of irreducible backgrounds to $t\overline{t}H$ -production.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗

Three-step LES-C models for flows at high Reynolds numbers

Here, we investigate the need for the second correction step in the recently proposed LES-C models for fluid flows at high Reynolds numbers. These models use a predictor-corrector idea to enhance the efficiency of the existing Large Eddy Simulation models. Different three-step(one defect step, two corrections) LES-C models, based on the Leray-α, ADM and NS-ω LES models, are tested in three different situations. The new Leray-α-C2 model (C2 stands for two correction steps) is applied to the Navier–Stokes equations; the ADC2 is applied to the MagnetoHydroDynamic flow; and the NS-ω-C2 is used in the fluid-fluid interaction problem. We evaluate the effectiveness of the second correction step in all these settings, using qualitative and quantitative numerical tests.

97 MATHEMATICS AND COMPUTING↗

Increasing the Number of Installed Residential Heat Pump Water Heaters in the USA Through Improved Technology and Utility Programs

Heat pump water heaters (HPWHs) offer an efficient alternative to electric resistance water heaters, but they suffer from limited market penetration. This paper reviews the current state of HPWHs in the USA, focusing on technology developments and programs to increase adoption. What technological improvements are needed to make HPWHs a good fit in more homes? Additionally, what are the best ways of promoting HPWHs to consumers? Improved communication and controls will allow HPWHs to provide demand response and follow dynamic utility rates. To enable fuel-switching, HPWHs are being developed that only need a standard 120V outlet. Split HPWHs can alleviate the cooling and noise that limit the use of HPWHs in small homes, multifamily buildings, and cold climates. Additionally, new utility programs aim to train installers and provide incentives to distributors to increase the number of HPWHs installed. Some utilities offer novel incentive programs that have led to increased customer uptake. Those advances, coupled with improved controls for load shifting and new products such as split HPWHs and HPWHs that can run on 120V power, may finally move the market forward.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Accurate hydrodynamic force and torque correlations for prolate spheroids from Stokes regime to high Reynolds numbers

Detailed simulations of flow around various prolate spheroids are performed using multi-relaxation time lattice Boltzmann method (LBM). The simulations are performed in the Reynolds numbers range 0.1 ≤ Re ≤ 2000 at different incident angles 0°≤ φ ≤ 90° for various prolate spheroids of aspect ratios 1 ≤ λ ≤ 8. The Re is based on the volume equivalent sphere diameter. From the simulations, accurate correlations for average drag, lift and torque coefficients (C D , C L , and C T respectively) are proposed. The mean deviations between the correlations and the simulation data for C D , C L , and C T are 2%, 6.5%, and 4.7% respectively. Furthermore, the correlations are fitted with the physics in mind such that they can be extrapolated beyond the regimes simulated. The fits are designed such that correlations reduce to analytical (C D and C L ) solutions available for prolate spheroids in the limit Re ≈ 0. At high Re, the correlations mimic the asymptotic flattening of C D as for spheres and therefore, we expect the correlations to be valid until the critical Re (≈ 10 5 ). Furthermore, extensive comparison of our correlations with other high-fidelity simulations from literature demonstrate the accuracy of our work compared to other correlations from literature. This work enables researchers to perform accurate unresolved Euler-Lagrangian simulations for a wide range of elongated particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends

A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.

42 ENGINEERING↗

Flow regime and Reynolds number variation effects on the mixing behavior of parallel flows

The hydraulic single-phase mixing of three parallel rectangular channels is experimentally investigated at various Reynolds numbers (Re) and flow regime combinations. Particle Image Velocimetry results for seven mixing cases are presented and discussed with varying Re combinations ranging from 1,824 to 20,844. While all cases result in the same Re ratio of ~0.69 between the inner and outer flows, two cases represent multi-regime mixing with the inner-outer regime pair of laminar-transitional and transitional-turbulent, while the other 5 cases are all characteristic of turbulent mixing with varying levels of turbulence. The outer channels initially share characteristics with a backward facing step. The center channel is found to initially behave like a slot jet, but then sees a significant increase in velocity decay. This inner flow velocity decay increased dramatically in the laminar-transitional mixing case, whose centerline velocity decay was ~6 times larger than the decay in the turbulent mixing cases. Second order statistics revealed a consistent mixing layer thickness of ~0.1 hydraulic diameters for all the cases but showed more intense shearing in the multi-regime mixing cases. The combined point and thereby the mixing layer length is determined using centerline velocity decay profiles, which show a much more aggressive mixing in multi-regime flows. Multi-regime mixing demonstrated superior characteristics relative to turbulent mixing due to a more dramatic velocity decay in the inner flow and a shorter mixing length. The contributions of this work include communicating the benefits of multi-regime mixing and providing detailed characterization efforts that can serve future efforts for validating computational models. Here this research also lays the groundwork for future studies aimed at achieving high levels of mixing without a severe penalty in pressure drop.

42 ENGINEERING↗