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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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Synergistic design of an application-oriented sparse directory on many-core embedded systems
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Poly(ionic liquid) embedded particles as efficient solid phase microextraction phases of polar and aromatic analytes
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Case study: Targeted RNA-sequencing of aged formalin-fixed paraffin-embedded samples for understanding chemical mode of action
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Perovskite solar cells with embedded homojunction via nonuniform metal ion doping
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Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.
The Biochemical Impact of Extracting an Embedded Adenylate Kinase Domain Using Circular Permutation
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Eu 5 Al 3 Sb 6 : Al 4 Tetrahedra Embedded in a Rock-Salt-Like Structure
The new Eu 5 Al 3 Sb 6 phase has been successfully synthesized as a pure phase through Sn flux methods yielding large, high-quality crystals. This structure type features disordered Al clusters that appear in the form of dual tetrahedra. It crystallizes in the monoclinic C2/m space group exhibiting a rock-salt-like Eu–Sb framework with [Al 4 ] tetrahedra replacing some of the cationic Eu atoms (space group: C2/m, a = 8.151(1) Å, b = 14.181(2) Å, c = 8.145(1) Å, β = 109.577(2)°). The structure models the [Al 4 ] as dual tetrahedra with the Al atom sites 37.5% occupied along with Eu present on the central site at 8% occupancy and the remainder of the site being vacant. The presence of the [Al 4 ] cluster is further supported by HRTEM. Electronic structure calculations show that this material is a semimetal with observed band crossings close to the Fermi level. Strong Al–Sb antibonding interactions were found from COHP calculations close to the Fermi level and provide the rationale for the deficiency of the Al cluster. Mössbauer spectroscopy on Eu-151 and Sb-121 provides oxidation states of 2+ and 3– along with the local environment. Magnetic susceptibility measurements can be described well with a Curie–Weiss law where an effective moment of 7.80 μB/mol Eu is obtained, consistent with Eu 2+ , and show canted antiferromagnetic behavior below 10 K. Temperature dependent resistivity shows a Kondo-like low-temperature upturn caused by enhanced scattering of the itinerant electrons with the 4f orbitals of Eu.
Surface Lattice-Embedded Pt Single-Atom Catalyst on Ceria-Zirconia with Superior Catalytic Performance for Propane Oxidation
Tuning the metal–support interaction and coordination environment of single-atom catalysts can help achieve satisfactory catalytic performance for targeted reactions. Herein, via the facile control of calcination temperatures for Pt catalysts on pre-stabilized Ce 0.9 Zr 0.1 O 2 (CZO) support, Pt single atoms (Pt 1 ) with different strengths of Pt–CeO 2 interaction and coordination environment were successfully constructed. With the increase in calcination temperature from 350 to 750 °C, a stronger Pt–CeO 2 interaction and higher Pt-O-Ce coordination number were achieved due to the reaction between PtO x and surface Ce 3+ species as well as the migration of Pt 1 into the surface lattice of CZO. The Pt/CZO catalyst calcined at 750 °C (Pt/CZO-750) exhibited a surprisingly higher C 3 H 8 oxidation activity than that calcined at 550 °C (Pt/CZO-550). Here, through systematic characterizations and reaction mechanism study, it was revealed that the higher concentration of surface Ce 3+ species/oxygen vacancies and the stronger Pt–CeO 2 interaction on Pt/CZO-750 could better facilitate the activation of oxygen to oxidize C 3 H 8 into reactive carbonate/carboxyl species and further promote the transformation of these intermediates into gaseous CO 2 . The Pt/CZO-750 catalyst can be a potential candidate for the catalytic removal of hydrocarbons from vehicle exhaust.
Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families
In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.
Slow Magnetic Relaxation of Co( II ) Single Chains Embedded within Metal–Organic Superstructures
We report two coordination polymers of the type Co(BPDC)(N-ox), with BPDC being 4,4'-biphenyldicarboxylate and N-ox being pyridine N-oxide (PNO) or isoquinoline N-oxide (IQNO), have been synthesized and characterized. The compounds feature 2D and 3D metal–organic networks that encapsulate Co(II)-based chains in a rigid superstructure. The dc and ac magnetic properties of these Co(BPDC)(N-ox) materials have been investigated alongside those of a related Co(BDC)(PNO) compound (where BDC is 1,4-benzenedicarboxylate), which contains a smaller dicarboxylate linker. These Co(II)-containing coordination polymers exhibit slow magnetic relaxation, as observed by ac susceptibility measurements. The observed magnetic behavior of all compounds is consistent with an antiferromagnetic interaction between canted Co spins along the 1D skeleton, resulting in single-chain magnet behavior. In the case of Co(BPDC)(IQNO), weak interchain magnetic interactions yield 3D antiferromagnetic order while the inherent magnetic behavior stemming from the chain component is maintained. The combination of these effects in this material puts it at the frontier between single-chain magnets and classical bulk antiferromagnets. This work contributes to the limited group of materials featuring the organization of single-chain magnets within a coordination polymer superstructure.
A Practical Guide to Density Matrix Embedding Theory in Quantum Chemistry
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