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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ethanol Monitoring Gas Sensor Based on Flower-Shaped Copper Sulfide by a Facile Hydrothermal Method for Marine Transportation

Flower-shaped CuS as a p-type gas-sensing material was synthesized by a facile hydrothermal method in this study. Morphology, structure, and chemical composition of the synthesized CuS gas-sensing material were analyzed by SEM, XRD, XPS, and N{sub 2} adsorption adsorption–desorption technique. Gas-sensing properties of the as-prepared CuS sensors were also investigated toward ethanol monitoring. The results showed that the flower-like CuS nanostructures consisted of interconnected nanosheets and exhibited good crystallinity. With the increase in ethanol concentration, the sensitivity of the CuS sensor significantly increased and indicated a roughly linear relationship at the optimal operating temperature of 260 °C. The ethanol-selective characteristics of the CuS sensor against other interfering gases including methanol, benzene, dichloromethane, and hexane were studied, and the gas response of the CuS sensor synthesized at 170 °C toward 100 ppm ethanol was 5.22, which was significantly higher than all the other gases. Moreover, 14-day continuous measurement further confirmed the excellent stability of the CuS sensor.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermal characterization of the build chamber in electron beam melting

Electron beam powder bed fusion, commonly termed electron beam melting (EBM), offers great versatility in multiple-part processes and can produce high quality as-built components due to low residual stresses. The EBM process is complex and requires careful thermal management, including uniform and consistent preheating of the powder bed, in order to ensure quality and consistency of the product. However, most of the simulations in the literature focus on the selective melting stage of the process. As of today, optimal conditions for initial pre-heating temperatures are only available for specific materials. Thus, in order to extend the EBM technology to other desired build materials, a much better understanding of the pre-heating stages is required. In this work, numerical and experimental approaches are combined in order to investigate the effects and sensitivities of heat removal from the build plate during EBM pre-heating stages. For this purpose, a carefully reconstructed numerical model of the build chamber of an ARCAM Q20 + machine is developed. It includes all main parts of the chamber and all relevant heat transfer mechanisms, whereas special attention is paid to radiation heat exchange between various bodies. In order to validate the model, dedicated experiments are performed, in which a system of thermocouples is installed in the build chamber, allowing direct measurement of the local temperatures of the start-plate and heat shields. A good agreement between the simulation and experimental findings is achieved, leading to a better understanding of the thermal processes characteristic to the pre-heating stages. This basic analysis is followed by a representative pre-heating case, where a powder bed is also considered. The energy required to obtain the desired pre-heating temperatures is evaluated, and the role of the powder bed in heat transfer within the chamber is assessed. The pre-heating stage, simulated in the present work, is supposed to create proper conditions for sintering and consequent melting of the powder, which are highly dependent on the local temperatures and heat transfer features. Thus, the findings of the reported work present a step towards a better understanding of the thermal processes that characterize EBM. The reported model can be further used to provide realistic boundary condition inputs for other meso- or macro-scale models as a function of time and geometry. The model can serve also for verification of machine settings, i.e., jump-safe and melt-safe ones which actually provide desired preheat, and for development of settings for new powders.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Formation of surface ionization waves in a plasma enhanced packed bed reactor for catalysis applications

Plasma enhanced packed bed reactors (PE-PBRs) intrinsically have complex geometries which makes it difficult to apply conventional scaling laws. For example, controlling the manner of discharge propagation between the micro-discharges (MDs) that occur between dielectrics in PE-PBRs and surface ionization waves (SIWs) that propagate along the dielectrics would aid in selectivity during plasma catalysis. An important parameter in that optimization is the pulse power format. In this work, we investigated the role of applied voltage amplitude and polarity on time resolved dynamics in an atmospheric pressure dielectric barrier discharge (DBD) operating in helium having surface topology resembling PE-PBRs using phase and space resolved optical emission spectroscopy (PROES). To enable systematic studies, the DBD uses an array of dielectric semi-spheres imaged through a top transparent electrode and imaged between the two electrodes from the side. The results were compared with 2-dimensional modeling. We identified three discharge mechanisms: filamentary micro-discharges (F-MDs) in the volume (the space between the surface of the dielectrics and the counter electrode), surface micro-discharges (S-MDs) between the dielectric semi-spheres near their contact points, and SIWs over the curved or flat dielectric surfaces. At the lowest voltages, only F-MDs are generated, once in each half cycle. At intermediate voltages, SIWs also appear which transform into S-MDs at the contact points. When voltage is further increased, several additional pulses are observed, which generate S-MDs at the contact points or a combination F-MDs and S-MDs. The voltage amplitude determines the frequency of these pulses. Modeling results show variations in plasma density and electron temperature for each of the three mechanisms which, in turn, impact production of reactive species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2020

CASSCF with Extremely Large Active Spaces Using the Adaptive Sampling Configuration Interaction Method

The complete active space self-consistent field (CASSCF) method is the principal approach employed for studying strongly correlated systems. However, exact CASSCF can only be performed on small active spaces of ~20 electrons in ~20 orbitals due to exponential growth in the computational cost. Here, we show that employing the Adaptive Sampling Configuration Interaction (ASCI) method as an approximate Full CI solver in the active space allows CASSCF-like calculations within chemical accuracy (<1 kcal/mol for relative energies) in active spaces with more than ~50 active electrons in ~50 active orbitals, significantly increasing the sizes of systems amenable to accurate multiconfigurational treatment. The main challenge with using any selected CI-based approximate CASSCF is the orbital optimization problem; they tend to exhibit large numbers of local minima in orbital space due to their lack of invariance to active–active rotations (in addition to the local minima that exist in exact CASSCF). We highlight methods that can avoid spurious local extrema as a practical solution to the orbital optimization problem. We employ ASCI-SCF to demonstrate a lack of polyradical character in moderately sized periacenes with up to 52 correlated electrons and compare against heat-bath CI on an iron porphyrin system with more than 40 correlated electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Tuning the Perfluorosulfonic Acid Membrane Morphology for Vanadium Redox-Flow Batteries

The microstructure of the perfluorinated sulfonic acid proton exchange membranes such as Nafion significantly affects their transport properties and performance in a vanadium redox flow battery (VRB). In this work, Nafion membranes with various equivalent weights (EW) ranging from 1000 to 1500 are prepared and the structure-property-performance relationship is investigated. Nuclear magnetic resonance (NMR) and small-angle X-ray scattering (SAXS) studies revealed their composition and morphology variances, which lead to major differences in key transport properties related to proton conduction and vanadium ion permeation. Their performances are further characterized as VRB membranes. Based on those understanding, a new perfluorosulfonic acid membrane is designed with optimal pore geometry and thickness, leading to higher ion selectivity and lower cost compared with the widely used Nafion® 115. Excellent VRB single-cell performance (89.3% energy efficiency at 50mA∙cm-2) was achieved along with a stable cyclical capacity over prolonged cycling.

25 ENERGY STORAGE↗
DOE OSTI2016

Carboxamide Spleen Tyrosine Kinase (Syk) Inhibitors: Leveraging Ground State Interactions To Accelerate Optimization

Optimization of a series of highly potent and kinome selective carbon-linked carboxamide spleen tyrosine kinase (Syk) inhibitors with favorable drug-like properties is described. A pervasive Ames liability in an analogous nitrogen-linked carboxamide series was obviated by replacement with a carbon-linked moiety. Initial efforts lacked on-target potency, likely due to strain induced between the hinge binding amide and solvent front heterocycle. Consideration of ground state and bound state energetics allowed rapid realization of improved solvent front substituents affording subnanomolar Syk potency and high kinome selectivity. These molecules were also devoid of mutagenicity risk as assessed via the Ames test using the TA97a Salmonella strain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Low-Temperature Activation and Coupling of Methane on MgO Nanostructures Embedded in Cu 2 O/Cu(111)

Here, the efficient conversion of methane into valuable hydrocarbons such as ethane and ethylene at relatively low temperatures without deactivation issues is crucial for advancing sustainable energy solutions. Herein, AP-XPS and STM studies show that MgO nanostructures (0.2-0.5 nm wide, 0.4-0.6 Å high) embedded in a Cu 2 O/Cu(111) substrate activate methane at room temperature, mainly dissociating it into CH x (x = 2 or 3) and H adatoms, with minimal conversion to C adatoms. These MgO nanostructures in contact with Cu 2 O/Cu(111) exhibit unique reactivity, enabling C-C coupling into ethane and ethylene at 500 K, a significantly lower temperature than that required for bulk MgO catalysts (>700 K), with negligible carbon deposition and no deactivation. DFT calculations corroborate these experimental findings. The CH 4,gas → *CH 3 +*H reaction is a downhill process on MgO/Cu 2 O/Cu(111) surfaces. The activation of methane is facilitated by an electron transfer from copper to MgO and the existence of Mg and O atoms with a low coordination number in the oxide nanostructures. The formation of O-CH 3 and O-H bonds overcomes the energy necessary for the cleavage of a C-H bond in methane. DFT studies reveal that smaller Mg 2 O 2 model clusters provide stronger binding and lower activation barriers for C-H dissociation in CH 4 , while larger Mg 3 O 3 clusters promote C-C coupling due to weaker *CH 3 binding. All these results emphasize the importance of size when optimizing the catalytic performance of MgO nanostructures in the selective conversion of methane.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cystine-knot peptide inhibitors of HTRA1 bind to a cryptic pocket within the active site region

Cystine-knot peptides (CKPs) are naturally occurring peptides that exhibit exceptional chemical and proteolytic stability. We leveraged the CKP carboxypeptidase A1 inhibitor as a scaffold to construct phage-displayed CKP libraries and subsequently screened these collections against HTRA1, a trimeric serine protease implicated in age-related macular degeneration and osteoarthritis. The initial hits were optimized by using affinity maturation strategies to yield highly selective and potent picomolar inhibitors of HTRA1. Crystal structures, coupled with biochemical studies, reveal that the CKPs do not interact in a substrate-like manner but bind to a cryptic pocket at the S1’ site region of HTRA1 and abolish catalysis by stabilizing a non-competent active site conformation. The opening and closing of this cryptic pocket is controlled by the gatekeeper residue V221, and its movement is facilitated by the absence of a constraining disulfide bond that is typically present in trypsin fold serine proteases, thereby explaining the remarkable selectivity of the CKPs. Our findings reveal an intriguing mechanism for modulating the activity of HTRA1, and highlight the utility of CKP-based phage display platforms in uncovering potent and selective inhibitors against challenging therapeutic targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

One-pot hydrodeoxygenation (HDO) of lignin monomers to C9 hydrocarbons co-catalysed by Ru/C and Nb 2 O 5

A physical mixture of Ru/C and Nb 2 O 5 is an effective catalyst for upgrading lignin monomers under low H 2 pressure at 250 °C to a clean cut of hydrocarbon liquid fuels. The reaction solvent is water with a small amount of methanol additive. Hydrodeoxygenation (HDO) was evaluated using dihydroeugenol (DHE) as an exemplary lignin monomer model. Under optimized conditions, 100% conversion of DHE and very high selectivity to propyl cyclohexane (C9 hydrocarbon) was achieved. Nb 2 O 5 was prepared at a low temperature (450 °C) and was shown to contain acid sites that enhance the production of fully deoxygenated products. The methanol additive serves as a hydrogen source for the Ru/C catalysed reduction of the aromatic ring. In addition, when a substrate mixture of DHE, isoeugenol and 4-allylsyringol simulating lignin products was employed, 100% conversion to propyl cyclohexane (76%) and propyl benzene (24%) was observed, thereby suggesting the general applicability of this catalyst system for funneling lignin monomers into a clean cut of hydrocarbon liquid fuels. This study sheds light on the function of each catalyst component and provides a simple and green utilization of biomass monomers as a feedstock for renewable hydrocarbon fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗
DOE OSTI2025

G-Mapper: Learning a Cover in the Mapper Construction

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset. However, the Mapper algorithm requires tuning several parameters in order to generate a “nice” Mapper graph. This paper focuses on selecting the cover parameter. We present an algorithm that optimizes the cover of a Mapper graph by splitting a cover repeatedly according to a statistical test for normality. Our algorithm is based on G-means clustering, which searches for the optimal number of clusters in 𝑘-means by iteratively applying the Anderson–Darling test. Our splitting procedure employs a Gaussian mixture model to carefully choose the cover according to the distribution of the given data. In conclusion, experiments for synthetic and real-world datasets demonstrate that our algorithm generates covers so that the Mapper graphs retain the essence of the datasets, while also running significantly faster than a previous iterative method.

G-means clustering↗
DOE OSTI2024

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗
DOE OSTI2020

High-speed fiber-based spectrometer for plasma Thomson scattering

Here we present a novel concept for a Thomson scattering diagnostic, based on a high-speed fiber optic spectrometer. The high-speed fiber optic spectrometer presented here translates a spectral measurement from the frequency domain into the time domain, thus requiring the use of only a single photodetector for spectral acquisition. The high temporal precision offered by the instrument gives rise to a number of advantages over traditional spectrometers, such as nearly background-free measurements and multiple uses of the same injected beam. Multiple uses of the same beam would enable greatly increased measurement rates, in the range of 10–100 MHz. The spectral range and resolution of the fiber spectrometer can be easily tailored to be optimized for the light source and experimental conditions by selecting different lengths of fiber, thus allowing for the proposed technique to exhibit high dynamic range when measuring many points simultaneously. Finally, due to the temporal separation of the background from the signal, these improvements are possible without the need for increased average input laser power.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

νSTORM - Neutrinos from STORed Muons: Letter of Intent to the Fermilab Physics Advisory Committee

The idea of using a muon storage ring to produce a high-energy (≃ 50 GeV) neutrino beam for experiments was first discussed by Koshkarev in 1974. A detailed description of a muon storage ring for neutrino oscillation experiments was first produced by Neuffer in 1980. In his paper, Neuffer studied muon decay rings with E μ of 8, 4.5 and 1.5 GeV. With his 4.5 GeV ring design, he achieved a figure of merit of ≃ 6 x 10 9 useful neutrinos per 3 x 10 13 protons on target. The facility we describe here (νSTORM) is essentially the same facility proposed in 1980 and would utilize a 3-4 GeV/c muon storage ring to study eV-scale oscillation physics and, in addition, could add significantly to our understanding of ν e and ν μ cross sections. In particular the facility can: (1) address the large Δm 2 oscillation regime and make a major contribution to the study of sterile neutrinos, (2) make precision ν e and $\bar{ν}$ e cross-section measurements, (3) provide a technology (μ decay ring) test demonstration and μ beam diagnostics test bed, and (4) provide a precisely understood ν beam for detector studies. The facility is the simplest implementation of the Neutrino Factory concept. In our case, 60 GeV/c protons are used to produce pions off a conventional solid target. The pions are collected with a focusing device (horn or lithium lens) and are then transported to, and injected into, a storage ring. The pions that decay in the first straight of the ring can yield a muon that is captured in the ring. The circulating muons then subsequently decay into electrons and neutrinos. We are starting with a storage ring design that is optimized for 3.8 GeV/c muon momentum. This momentum was selected to maximize the physics reach for both oscillation and the cross section physics. See Fig. 1 for a schematic of the facility.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Optimization of Window Positions for Wind-Driven Natural Ventilation Performance

This paper optimizes opening positions on building facades to maximize the natural ventilation’s potential for ventilation and cooling purposes. The paper demonstrates how to apply computational fluid dynamics (CFD) simulation results to architectural design processes, and how the CFD-driven decisions impact ventilation and cooling: (1) background: A CFD helps predict the natural ventilation’s potential, the integration of CFD results into design decision-making has not been actively practiced; (2) methods: Pressure data on building facades were obtained from CFD simulations and mapped into the 3D modeling environment, which were then used to identify optimal positions of two openings of a zone. The effect of the selected opening positions was validated with building energy simulations; (3) results: The cross-comparison study of different window positions based on different geographical locations quantified the impact on natural ventilation effectiveness; and (4) conclusions: The optimized window position was shown to be effective, and some optimal solutions contradicted the typical cross-ventilation strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity

Background: Viral metagenomics (viromics) is increasingly used to obtain uncultivated viral genomes, evaluate community diversity, and assess ecological hypotheses. While viromic experimental methods are relatively mature and widely accepted by the research community, robust bioinformatics standards remain to be established. Here we used in silico mock viral communities to evaluate the viromic sequence-to-ecological-inference pipeline, including (i) read pre-processing and metagenome assembly, (ii) thresholds applied to estimate viral relative abundances based on read mapping to assembled contigs, and (iii) normalization methods applied to the matrix of viral relative abundances for alpha and beta diversity estimates. Results: Tools specifically designed for metagenomes, specifically metaSPAdes, MEGAHIT, and IDBA-UD, were the most effective at assembling viromes. Read pre-processing, such as partitioning, had virtually no impact on assembly output, but may be useful when hardware is limited. Viral populations with 2–5 × coverage typically assembled well, whereas lesser coverage led to fragmented assembly. Strain heterogeneity within populations hampered assembly, especially when strains were closely related (average nucleotide identity, or ANI ≥97%) and when the most abundant strain represented <50% of the population. Viral community composition assessments based on read recruitment were generally accurate when the following thresholds for detection were applied: (i) ≥10 kb contig lengths to define populations, (ii) coverage defined from reads mapping at ≥90% identity, and (iii) ≥75% of contig length with ≥1 × coverage. Finally, although data are limited to the most abundant viruses in a community, alpha and beta diversity patterns were robustly estimated (±10%) when comparing samples of similar sequencing depth, but more divergent (up to 80%) when sequencing depth was uneven across the dataset. In the latter cases, the use of normalization methods specifically developed for metagenomes provided the best estimates. Conclusions: These simulations provide benchmarks for selecting analysis cut-offs and establish that an optimized sample-to-ecological-inference viromics pipeline is robust for making ecological inferences from natural viral communities. Continued development to better accessing RNA, rare, and/or diverse viral populations and improved reference viral genome availability will alleviate many of viromics remaining limitations.

59 BASIC BIOLOGICAL SCIENCES↗