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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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Accuracy of wind energy forecasts in Great Britain and prospects for improvement
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Molecular layer deposition (MLD) modified SSZ-13 membrane for greatly enhanced H 2 separation
Zeolite membranes with high thermal stability and well-fined pores are attractive for gas separation. However, the defects are almost inevitable in zeolite membranes. Moreover, fine-tuning zeolitic pores for precise molecular separation is challenging. In this study, molecular layer deposition (MLD) was employed to deposit ultrathin microporous coating on SSZ-13 zeolite membrane. The MLD modified SSZ-13 composite membrane was highly selective for H 2 separation. With optimized MLD cycles, H 2 /N 2 and H 2 /CH 4 ideal selectivities as high as 35.6 and 427, respectively, were obtained, in strong contrast with approximately 5 for base SSZ-13 membrane. Our results suggest that MLD is a very promising technology to precisely modify the pore size of zeolite membranes, while minimizing flow through non-selective defects, for gas separation.
Laser-structured spike surface shows great bone integrative properties despite infection in vivo.
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Ca 14 AlBi 11 —a new Zintl phase from earth-abundant elements with a great potential for thermoelectric energy conversion
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Fine scale variability in Green Vegetation Fraction Over the Southern Great Plains using Sentinel-2 satellite: A case study
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Additive manufacturing of three-dimensional (3D)-architected CoCrFeNiMn high- entropy alloy with great energy absorption
In this study, we developed an approach to additive manufacturing of three-dimensional (3D)-architected CoCrFeNiMn high-entropy alloys by direct ink writing combined with thermal sintering. The 3D-architected CoCrFeNiMn lattices exhibit the outstanding energy absorption capacity that expands the envelope of existing architected materials. Such a high-energy absorption ability originates from the bend-dominated deformation mode of the 3D-architecture as well as the fully-annealed homogeneous microstructure of equiaxed grains, which collectively lead to remarkable strain hardening upon deformation. Our study provides a new avenue for 3D-printing advanced architected materials with extreme mechanical energy absorption for a myriad of structural applications.
Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water
Experiment Directed Simulations (EDS) is a method within a class of techniques seeking to improve molecular simulations by minimally biasing the system Hamiltonian to reproduce certain experimental observables. In a previous application of EDS to ab initio molecular dynamics (AIMD) simulation based on electronic density functional theory (DFT), the AIMD simulations of water were biased to reproduce its experimentally derived solvation structure. In particular, by solely biasing the O-O pair correlation functions, other structural and dynamical properties that were not biased were improved. In this work, the hypothesis is tested that directly biasing the OH pair correlation (and hence the H-O∙∙∙H hydrogen bonding), will provide an even better improvement of DFT-based water properties in AIMD simulations. The logic behind this hypothesis is that for most electronic DFT descriptions of water the hydrogen bonding is known to be deficient due to anomalous charge transfer and over polarization in the DFT. Using recent advances to the EDS learning algorithm, we thus train a minimal bias on AIMD water that reproduces the O-H radial distribution function derived from the highly accurate MB-pol model of water. Finally, it is then confirmed that biasing the O-H pair correlation alone can lead to improved AIMD water properties, with structural and dynamical properties in even closer to experiment than the previous EDS-AIMD model.
Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics
Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest-scale systems. Furthermore, the situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a 2 orders of magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.
Deuteration and Polymers: Rich History with Great Potential
Deuterium, a stable hydrogen isotope, has been playing important roles in many scientific areas, including polymer science. The developments of deuteration science and polymer science have been interwoven for the past 60 years. The unique characteristics of the scattering lengths for hydrogen isotopes (protium and deuterium) result in the dramatic differences of scattering length densities for protiated and deuterated compounds omnipresent in polymers, rendering neutron scattering a powerful tool for the investigation of polymeric structures and dynamics that are inaccessible to other tools. While the role of deuteration in neutron scattering of polymeric materials can hardly be overstated, it has also enabled the addressing of many questions using other approaches, such as NMR and vibrational spectra. In this Perspective, we provide a brief account of deuterium in the synthesis, properties, and application of various polymers. We also offer an outlook of the potentials in this ever-evolving and exciting area.
A Recipe for a Great Meal: A Benchtop Route from Elemental Se to Superior Thermoelectric β-Ag 2 Se
The low-temperature modification of β-Ag 2 Se has proven to be useful as a near-room-temperature thermoelectric material. Over the past years, research has been devoted to interstitial, vacancy, and substitutional doping into the parent β-Ag 2 Se structure, aiming at tuning the material’s charge and heat transport properties to enhance thermoelectric performance. The transformation of β-Ag 2 Se into α-Ag 2 Se at ~134 °C and the low solubility of dopants are the main obstacles for the doping approach. Herein, we report a facile, safe, scalable, and cost-effective benchtop approach to successfully produce metal-doped β-Ag 2 Se. The doped materials display a remarkable enhancement of thermoelectric performance with a record-high peak zT of 1.30 at 120 °C and an average zT of ~1.15 in the 25–120 °C range for 0.2 at. % Zn-doped Ag 2 Se. The enhancement in zT is attributed to point defects created by Zn doping into Ag vacancies/interstitials, which enhances the scattering of phonons and tunes the charge carrier properties, leading to the significant suppression of thermal conductivity. In conclusion, the simplicity of the synthetic method developed herein and the high performance of the final products provide an avenue to produce high-quality Ag 2 Se-based thermoelectric materials.
Block Tectonics across Western Tibet and Multi-Millennial Recurrence of Great Earthquakes on the Karakax Fault
We report that fault slip rates are critical to quantify continental deformation. Those along the Karakax fault (northwestern Altyn Tagh Fault: ATF) have been debated, even though it is one of Tibet's most outstanding active faults. At Taersa, using LiDAR measurements of terrace and fan riser offsets (~6 to ~500 m) and 10 Be/ 26 Al dating of alluvial surfaces (<210 ka), we obtain a late Quaternary slip rate of ~2.5 ± 0.5 mm/yr. This doubles the ~2.6 ± 0.5 mm/yr rate time span found to the east and west. We interpret the ~150 km-long, free-faced rupture along the fault to be that of the M ~ 7.6 event felt in Hotan in 1882. Characteristic slip (~6 m) during four large earthquakes since ~10 ka implies a ~2500 ± 500 years return time. A ~3 mm/yr rate is consistent with the ~80 km offset of the Karakax river since uplift of the West Kunlun range and sediment deposition in the Tarim foreland accelerated, ~24 Ma ago. The faster slip rate (~10.5 mm/yr) on the central ATF matches the sum of those along the reactivated West Tibetan terrane boundaries (Karakax and Longmu-Gozha Co faults) at the Uzatagh triple junction (~36°N, 83°E). The abrupt termination and altitude drop of the Karakorum range where the Longmu Co and Karakorum faults meet (Angmong junction), also reflect triple junction kinematics. Such localized changes account for the rise of the Karakorum and West Kunlun ranges and support lithospheric block tectonics rather than diffusely distributed deformation.
Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks
The eddy covariance (EC) method is one of the most widely used approaches to quantify surface-atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land-cover type sites of corn, restored-prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land-cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (R eco ), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE, R eco , and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums of R eco and GPP fluxes without needing the constraint of similar land-cover-type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land-cover types across the landscape.
Climate Drives Modeled Forest Carbon Cycling Resistance and Resilience in the Upper Great Lakes Region, USA
Forests dominate the global terrestrial carbon budget, but their ability to continue doing so in the face of a changing climate is uncertain. A key uncertainty is how forests will respond (resistance) and recover from (resilience) to rising levels of disturbance of varying intensities. This knowledge gap can optimally be addressed by integrating manipulative field experiments with ecophysiological modeling. We used the ED-2.2 model to project carbon fluxes for a northern temperate deciduous forest subjected to a real-world disturbance severity manipulation experiment. ED-2.2 was run for 150 years, starting from near bare ground in 1900, and subjected to three disturbance treatments under an ensemble of climate conditions. Both disturbance severity and climate strongly affected carbon fluxes, and interacted with one another. We then calculated metrics of functional ecosystem stability. Modeled gross primary production exhibited a mean resistance of -0.13, -0.23, and -0.42 (unitless) in response to increasing disturbance severity of 45%, 65%, and 85% mortality, respectively; and resilience of 0.002, 0.004, 0.008 (yr-1) respectively. This pattern held true for other carbon fluxes, meaning that in general simulated GPP declined more initially but recovered more quickly from higher mortality. Notably, however, heterotrophic respiration responded more slowly to disturbance, and its highly variable response was affected by different drivers. This work provides insight into how future conditions might affect the functional stability of mature forests in this region under ongoing climate change and changing disturbance regimes.
Carbohydrates great and small, from dietary fiber to sialic acids: How glycans influence the gut microbiome and affect human health
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Rapid growth of anthropogenic organic nanoparticles greatly alters cloud life cycle in the Amazon rainforest
Aerosol-cloud interactions remain uncertain in assessing climate change. While anthropogenic activities produce copious aerosol nanoparticles smaller than 10 nanometers, they are too small to act as efficient cloud condensation nuclei (CCN). The mechanisms responsible for particle growth to CCN-relevant sizes are poorly understood. Here, we present aircraft observations of rapid growth of anthropogenic nanoparticles downwind of an isolated metropolis in the Amazon rainforest. Model analysis reveals that the sustained particle growth to CCN sizes is predominantly caused by particle-phase diffusion-limited partitioning of semivolatile oxidation products of biogenic hydrocarbons. Cloud-resolving numerical simulations show that the enhanced CCN concentrations in the urban plume substantially alter the formation of shallow convective clouds, suppress precipitation, and enhance the transition to deep convective clouds. The proposed nanoparticle growth mechanism, expressly enabled by the abundantly formed semivolatile organics, suggests an appreciable impact of anthropogenic aerosols on cloud life cycle in previously unpolluted forests of the world.