SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A PIC code was used to simulate the formation of a chain of several virtual cathodes in a conical drift tube with its diameter increasing in the direction of motion of the electron beam. Their spatiotemporal dynamics was calculated.
The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.
The structure of the human gastrointestinal microbiota can change during pregnancy, which may influence gestational metabolism; however, a mechanism of action remains unclear. Here we observed that in wild-type (WT) mice the relative abundance of Actinobacteria and Bacteroidetes increased during pregnancy. Along with these changes, short-chain fatty acids (SCFAs), which are mainly produced through gut microbiota fermentation, significantly changed in both the cecum and peripheral blood throughout gestation in these mice. SCFAs are recognized by G protein-coupled receptors (GPCRs) such as free fatty acid receptor-2 (FFA2), and we have previously demonstrated that the fatty acid receptor-2 gene ( Ffar2 ) expression is higher in pancreatic islets during pregnancy. Using female Ffar2 -/- mice, we explored the physiological relevance of signaling through this GPCR and found that Ffar2-deficient female mice developed fasting hyperglycemia and impaired glucose tolerance in the setting of impaired insulin secretion compared with WT mice during, but not before, pregnancy. Insulin tolerance tests were similar in Ffar2 -/- and WT mice before and during pregnancy. Next, we examined the role of FFA2 in gestational β-cell mass, observing that Ffar2 -/- mice had diminished gestational expansion of β-cells during pregnancy. Interestingly, mouse genotype had no significant impact on the composition of the gut microbiome, but did affect the observed SCFA profiles, suggesting a functional difference in the microbiota. Altogether, these results suggest a potential link between increased Ffar2 expression in islets and the alteration of circulating SCFA levels, possibly explaining how changes in the gut microbiome contribute to gestational glucose homeostasis.
This paper evaluates the effect of climate change on daily rainfall, especially on the mean number of wet days and the mean rainfall intensity. Assuming that the mechanism of daily rainfall occurrences follows the first-order Markov chain model, the possible changes in the transition probabilities are estimated by considering the climate change scenarios. Also, the change of the stationary probabilities of wet and dry day occurrences and finally the change in the number of wet days are derived for the comparison of current (1x CO 2 ) and 2x CO 2 conditions. As a result of this study, the increase or decrease in the mean number of wet days was found to be not enough to explain all of the change in monthly rainfall amounts, so rainfall intensity should also be modified. The application to the Seoul weather station in Korea shows that about 30% of the total change in monthly rainfall amount can be explained by the change in the number of wet days and the remaining 70% by the change in the rainfall intensity. That is, as an effect of climate change, the increase in the rainfall intensity could be more significant than the increase in the wet days and, thus, the risk of flood will be much highly increased.
The model was developed to investigate the interactions between the cobalt and rare earth supply chains in supporting clean energy technologies such as electric vehicles, wind, etc. The model also serves as a platform for testing magnet substitution that uses less heavy rare earths and more cobalt. Users can also test different scenarios to see what needs to be done to meet an ambitious EV deployment goal.
SynthFirm is a large-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level.
Markov chain Monte Carlo sampler useful for a variety of problems for optimization, numerical integration, and generating draws from a probability distribution. They are often used in physical and mathematical problems, especially when it is difficult or impossible to use other approaches. This disclosure is for new bug fixes and enhancements to an existing open source library that is already in the public domain under the MIT license. https://github.com/jellis18/PTMCMCSampler
SynthFirm is a national-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level. The SynthFirm 2.0 version includes national commercial vehicle fleet generation, international trade simulation and automized model validation pipeline, which allows seemless deployment across the nation and build a comprehensive freight inventories at national scale or for selected region.
Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is introduced using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the further benefit of rigorously quantified uncertainty of these estimates.
Iron oxide nanoparticles are among the most widely used and characterized magnetic nanoparticles. However, metal alloys such as superparamagnetic iron-platinum particles (SIPPs), which have better magnetic properties, are receiving increased attention. Scalable techniques to routinely synthesize SIPPs in bulk need further study. Here, we focus on the role played by the fatty amine ligand in the formation of the bimetallic FePt nanocrystal. More specifically, we compare the effect of varying lengths of fatty amine ligands on the shape, structure, uniformity, composition, and magnetic properties of the SIPPs. We synthesized SIPPs by employing a ‘green’ thermal decomposition reaction using fatty amine ligands containing 12 to 18 carbons in length. Greater fatty amine chain length increased the polydispersity, particle concentration, iron concentration, and the stability of the SIPPs. Additionally, longer reflux times increased the diameter of the particles, but decreased the iron concentration, suggesting that shorter reaction times are preferable. Fourier transform infrared spectroscopy of the SIPPs indicates that the ligands are successfully bound to the FePt cores through the amine group. Superconducting quantum interference device magnetometry measurements suggest that all of the SIPPs were superparamagnetic at room temperature and that SIPPs synthesized using tetradecylamine had the highest saturation magnetization. Our findings indicate that the octadecylamine ligand, which is currently used for the routine synthesis of SIPPs, may not be optimal. Overall, we found that using tetradecylamine and a 30-min reflux reaction resulted in optimal particles with the highest degree of monodispersity, iron content, stability, and saturation magnetization.
Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a key role in regulating the levels of plasma low-density lipoprotein cholesterol (LDL-C). Here, we demonstrate that the compound PF-06446846 inhibits translation of PCSK9 by inducing the ribosome to stall around codon 34, mediated by the sequence of the nascent chain within the exit tunnel. We further show that PF-06446846 reduces plasma PCSK9 and total cholesterol levels in rats following oral dosing. Using ribosome profiling, we demonstrate that PF-06446846 is highly selective for the inhibition of PCSK9 translation. The mechanism of action employed by PF06446846 reveals a previously unexpected tunability of the human ribosome that allows small molecules to specifically block translation of individual transcripts.
The Escherichia coli protein BdcA (previously referred to as YjgI) plays a key role in the dispersal of cells from bacterial biofilms, and its constitutive activation provides an attractive therapeutic target for dismantling these communities. In order to investigate the function of BdcA at a molecular level, we integrated structural and functional studies. Our 2.05 Å structure of BdcA shows that it is a member of the NAD(P)(H)-dependent short-chain dehydrogenase/reductase (SDR) superfamily. Structural comparisons with other members of the SDR family suggested that BdcA binds NADP(H). This was demonstrated experimentally using thermal denaturation studies, which showed that BcdA binds specifically to NADPH. Subsequent ITC experiments further confirmed this result and reported a K d of 25.9 mM. Thus, BdcA represents the newest member of the limited number of oxidoreductases shown to be involved in quorum sensing and biofilm dispersal.
Objective: Neurofilament light chain (Nf-L) has been used to detect neuroaxonal damage in the brain caused by physical injury or disease. The purpose of this study was to determine if serum Nf-L could be used as a biomarker for pre-symptomatic detection of scrapie in sheep. Methods: Four sheep with prion protein genotype AVQQ were intranasally inoculated with the classical scrapie strain x124. Blood was collected every 4 weeks until 44 weeks post-inoculation, at which point weekly collection commenced. Serum was analyzed using single molecule array (Quanterix SR-X) to evaluate Nf-L concentrations. Results: Scrapie was confirmed in each sheep by testing homogenized brainstem at the level of the obex with a commercially available enzyme immunoassay. Increased serum Nf-L concentrations were identified above the determined cutoff during the last tenth of the respective incubation period for each sheep. Throughout the time course study, PrP Sc accumulation was not detected antemortem by immunohistochemistry in rectal tissue at any timepoint for any sheep. RT-QuIC results were inconsistently positive throughout the timepoints tested for each sheep; however, each sheep had at least one timepoint detected positive. When assessing serum Nf-L utility using receiver operator characteristic curves against different clinical parameters, such as asymptomatic and symptomatic (pruritus or neurologic signs), results showed that Nf-L was most useful at being an indicator of disease only late in disease progression when neurologic signs were present. Conclusion: Serum Nf-L concentrations in the cohort of sheep increased as disease progressed; however, serum Nf-L did not increase during the presymptomatic window. The levels increased substantially throughout the final 10% of the animals’ scrapie incubation period when other clinical signs were present. Serum Nf-L is not a reliable biomarker for pre-clinical detection of scrapie.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused. Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
We study the symmetries of closed Majorana chains in 1+1d, including the translation, fermion parity, spatial parity, and time-reversal symmetries. The algebra of the symmetry operators is realized projectively on the Hilbert space, signaling anomalies on the lattice, and constraining the long-distance behavior. In the special case of the free Hamiltonian (and small deformations thereof), the continuum limit is the 1+1d free Majorana CFT. Its continuum chiral fermion parity (-1)^{F_L} ( − 1 ) F L emanates from the lattice translation symmetry. We find a lattice precursor of its mod 8 ’t Hooft anomaly. Using a Jordan-Wigner transformation, we sum over the spin structures of the lattice model (a procedure known as the GSO projection), while carefully tracking the global symmetries. In the resulting bosonic model of Ising spins, the Majorana translation operator leads to a non-invertible lattice translation symmetry at the critical point. The non-invertible Kramers-Wannier duality operator of the continuum Ising CFT emanates from this non-invertible lattice translation of the transverse-field Ising model.
We study the entanglement entropy of the quantum trajectories of a free fermion chain under continuous monitoring of local occupation numbers. We propose a simple theory for entanglement entropy evolution from disentangled and highly excited initial states. It is based on generalized hydrodynamics and the quasi-particle pair approach to entanglement in integrable systems. We test several quantitative predictions of the theory against extensive numerics and find good agreement. In particular, the volume law entanglement is destroyed by the presence of arbitrarily weak measurement.
This research has shown that the microstructure of self-assembled copolymers can be decoupled from the polymer chemistry. The simplest polymer architecture, linear block copolymers, is valuable for a broad range of applications, including adhesives and coatings, medical devices, electronics and energy storage, because these block copolymers reproducibly self-assemble into microphase separated nanoscale domains. Unfortunately, the self-assembled microstructure is tuned by polymer composition, thus limiting the potential to simultaneously optimize chemical, mechanical, and transport properties for desired applications. To this end, much work was been put into manipulating block copolymer self-assembly independently of polymer composition. These efforts have included the use of additives or solvents to alter polymer chain conformation, the addition of a third monomer to produce ABC triblock terpolymers, architectures with mixed blocks, such as tapered/gradient polymers, and the synthesis of other nonlinear molecular architectures. This work has shown that the microstructures formed by linear ABC terpolymers can be altered by controlling the architecture of the polymer molecules at a constant monomer composition, so that the microstructure is tuned independently from the chemical properties.