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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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Methods identifying cost reduction potential for water electrolysis systems
The electrochemical reduction of water to form hydrogen is an emerging alternative for energy storage, where hydrogen can be used as a transportation fuel, combusted to generate electricity, utilized in a fuel cell, or used as a feedstock for chemical synthesis. Techno-economic analysis (TEA) is a valuable tool for understanding how to inform research directions that could make hydrogen from electrolysis cost competitive with that produced by conventional means like steam methane reforming. The current state of knowledge on TEA of electrolysis systems suggests cost reductions are likely to result from advances in system design and materials, scale-up of manufacturing processes, and learning by doing effects as electrolysis deployment increases. Future directions and opportunities for TEA of electrolysis systems include dispatchable operation in wholesale power markets, optimization of capital cost and system durability, and analysis of pathways for hydrogen to support economy-wide decarbonization.
An MINLP formulation to identify thermodynamically-efficient distillation configurations
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A workflow for identifying metabolically active chemicals to complement in vitro toxicity screening
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Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods
Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.
Identifying phases in protective scale formed during high temperature corrosion
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A new self-adaptive reconstruction method to identify defects through Wigner–Seitz approach
A new self-adaptive reconstruction method based on local atomic structure at any given molecular dynamics (MD) step has been developed in this article. The method can be used in Wigner–Seitz defect analysis approach to correctly and efficiently explore the information of both point defects and complex defect clusters (e.g. dislocation loops and voids) formed after a displacement cascade where the cascade interacts with grain boundaries and/or dislocations. The algorithm and validation are provided in detail. Results for identification of radiation defects during and after cascades interacting with a dislocation network show that the new method can well recognize all simple and complex defects and defect clusters. Thus, this new method provides a totally new way to explore the density and size of radiation defects at atomic scale after complex MD evolution processes, providing correct information to understand and predict radiation damage in materials through atomic simulations.
BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data
Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.
Variation in identifying children and adolescents with disability and developmental disability in population-based public health surveys
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Integrated OMICS platforms identify LAIR1 genetic variants as novel predictors of cross-sectional and longitudinal susceptibility to severe malaria and all-cause mortality in Kenyan children
Background: Severe malarial anaemia (SMA) is a leading cause of childhood mortality in holoendemic Plasmodium falciparum regions. Methods: To gain an improved understanding of SMA pathogenesis, whole genome and transcriptome profiling was performed in Kenyan children (n = 144, 3–36 months) with discrete non-SMA and SMA phenotypes. Leukocyte associated immunoglobulin like receptor 1 (LAIR1) emerged as a predictor of susceptibility to SMA (P b 1 × 10-2 , OR: 0.44–1.37), and was suppressed in severe disease (-1.69-fold, P = 0.004). To extend these findings, the relationship between LAIR1 polymorphisms [rs6509867 (16231CNA); rs2287827 (18835GNA)] and clinical outcomes were investigated in individuals (n = 1512, b5 years) at enrolment and during a 36-month longitudinal follow-up. Findings: Inheritance of the 16,231 recessive genotype (AA) increased susceptibility to SMA at enrolment (OR = 1.903, 95%CI: 1.252–2.891, P = 0.003), and longitudinally (RR = 1.527, 95%CI: 1.119–2.083, P = 0.008). Carriage of the 18,835 GA genotype protected against SMA cross-sectionally (OR = 0.672, 95%CI: 0.480–0.9439, P = 0.020). Haplotype carriage (C16231A/G18835A) also altered cross-sectional susceptibility to SMA: CG (OR = 0.717, 95%CI: 0.527–0.9675, P = 0.034), CA (OR = 0.745, 95%CI: 0.536–1.036, P = 0.080), and AG (OR = 1.641, 95%CI: 1.160–2.321, P = 0.005). Longitudinally, CA carriage was protective against SMA (RR = 0.715, 95%CI: 0.554–0.923, P = 0.010), while AG carriage had an additive effect on enhanced SMA risk (RR = 1.283, 95%CI: 1.057–1.557, P = 0.011). Variants that protected against SMA had elevated LAIR1 transcripts, while those with enhanced risk had lower expression (P b 0.05). Inheritance of 18,835 GA reduced all-cause mortality by 44.8% (HR = 0.552, 95%CI: 0.329–0.925, P = 0.024), while AG haplotype carriage increased susceptibility by 68% (HR = 1.680, 95%CI: 1.020–2.770, P = 0.040). Interpretation: These findings suggest LAIR1 is important for modulating susceptibility to SMA and all-cause childhood mortality.
Modeling autumn migration of a rare soaring raptor identifies new movement corridors in central Appalachia
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Complement Proteins Identify Rapidly Progressive Diabetic Kidney Disease
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Identifying the limiting electrode in lithium ion batteries for extreme fast charging
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Sensitivity analysis for identifying key parameters affecting energy consumption in early-stage building design
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Identifying key variables and interactions in statistical models of building energy consumption using regularization
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Identifying fatigue damage of mudded intercalations based on dynamic triaxial test
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Identifying institutional barriers and policy implications for sustainable energy technology adoption among large organizations in California
Large organizations wield considerable market power, their procurement activities can be leveraged to achieve social, economic and environmental goals by ‘pulling’ more desirable products into the market. However, while there is substantial research in individual consumer buying behavior and market barriers to sustainable technology adoption, less is known about large organizational buying behavior and the impact of institutional barriers in this area. To address these research gaps, in this work we conducted an exploratory study aimed at better understanding the process through which large organizations purchase sustainable energy technologies and what internal barriers they experience during that process. Additionally, we surveyed and interviewed 120 individuals involved in procurement from Californian organizations representing both public and private sectors. Survey results indicate the need to resolve the conflict between prioritizing lowest first cost and lowest life cycle cost, better engage multiple stakeholders involved in internal decision-making around purchasing, and improve existing procurement tools or offer new ones. We provide recommendations for how policymakers can apply our findings to increase the adoption of sustainable energy technologies in their own organizations and communities.