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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Characterization of the Entry Steps in Diterpenoid Alkaloid Biosynthesis

The diterpenoid alkaloids are a group of specialized metabolites where the terpenoid and alkaloid classes intersect, and which are found primarily within the Aconitum (Wolf’s-Bane) and Delphinium (Larkspur) genera. While there is considerable interest in these compounds for their wide range of bioactivities, their structural complexity poses a significant challenge for chemical synthesis. Biosynthesis offers an appealing alternative for production, however, little progress has been made towards elucidation of their biosynthetic pathways. Here, we employ a comparative transcriptomics approach to identify six enzymatic steps in the biosynthesis of atisinium, conserved across both Delphinium grandiflorum and Aconitum plicatum . Key to this pathway is a reductase which selectively incorporates ethanolamine over ethylamine into the diterpenoid scaffold. While the majority of diterpenoid alkaloids contain an ethylamine moiety, we demonstrate through isotope labeling in Aconitum callus cultures and a computational metabolomics approach that ethanolamine is, unintuitively, the preferred source of nitrogen for these metabolites. Identification of these enzymes and production of a key intermediate in a heterologous host paves the way for biosynthetic production of this group of metabolites with promise for medicinal applications.

biosynthesis↗
DOE OSTI2025

Catalytic Difunctionalization of Cyclic Dienes: Direct Entry to Novel ROMP Monomers

We developed a catalytic platform to convert simple hydrocarbon feedstocks into valuable, tunable materials by leveraging nickel-catalyzed difunctionalization of cyclic dienes to access a novel class of cyclic alkene monomers. These monomers undergo ring-opening metathesis polymerization (ROMP) to yield sequence-controlled polymers with defined stereochemistry. Through mechanistic studies and catalyst optimization, we established a scalable, gram-level synthesis for selective diarylation, and expanded the reaction scope to include arylalkylation through rationally tuning the organoboron coupling partner. The resulting polymers were systematically studied to understand how steric, electronic, and stereochemical features influence polymerization behavior and bulk material properties. Functionalized derivatives bearing sulfonated groups were explored as proton-exchange membranes, and chemical recycling pathways were developed to recover monomers from the final materials. This work bridges small-molecule catalysis and macromolecular design, enabling access to tunable, recyclable polymers from abundant hydrocarbon starting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Drone Fleet Summary: NNLEMS UxS Rolodex entry for Sandia

Sandia’s UAS Aviation Operations Unit (UAOU) was established in 2019 to be the single entity at Sandia conducting UAS Ops in support the labs Uncrewed Aircraft Systems (UAS) activities. The UAOU currently consists of >330 FAA Registered UAS with a large variety of primarily Class 1&2 UAS: fixed wing (>90), multi-rotor (>230), hybrids, VTOLs, jets, and balloons. Many of these are threat vehicles presented as targets to Counter-UAS (CUAS) systems as part of performance tests, with the remainder in support of other projects across Sandia often with custom payload needs. The UAOU has ~15 primary pilots and reach back to another ~45 FAA Certified Remote Pilots across Sandia. The team conducts flight and CUAS operations at many test locations, including OCONUS. Sandia was awarded the 2024 DOE Federal Aviation Safety Program Award.

42 ENGINEERING↗
DOE OSTI2025

Sign Problem in Tensor-Network Contraction

We investigate how the computational difficulty of contracting tensor networks depends on the sign structure of the tensor entries. Using results from computational complexity, we observe that the approximate contraction of tensor networks with only positive entries has lower computational complexity as compared to tensor networks with general real or complex entries. This raises the question of how this transition in computational complexity manifests itself in the hardness of different tensor-network-contraction schemes. We pursue this question by studying random tensor networks with varying bias toward positive entries. First, we consider contraction via Monte Carlo sampling and find that the transition from hard to easy occurs when the tensor entries become predominantly positive; this can be understood as a tensor-network manifestation of the well-known negative-sign problem in quantum Monte Carlo. Second, we analyze the commonly used contraction based on boundary tensor networks. The performance of this scheme is governed by the number of correlations in contiguous parts of the tensor network (which by analogy can be thought of as entanglement). Remarkably, we find that the transition from hard to easy—i.e., from a volume-law to a boundary-law scaling of entanglement—already occurs for a slight bias of the tensor entries toward a positive mean, scaling inversely with the bond dimension D , and thus the problem becomes easy the earlier the larger D occurs. This is in contrast both to expectations and to the behavior found in Monte Carlo contraction, where the hardness at fixed bias increases with the bond dimension. To provide insight into this early breakdown of computational hardness and the accompanying entanglement transition, we construct an effective classical statistical-mechanical model that predicts a transition at a bias of the tensor entries of 1 / D , confirming our observations. We conclude by investigating the computational difficulty of computing expectation values of tensor-network wave functions (projected entangled-pair states, PEPSs) and find that in this setting, the complexity of entanglement-based contraction always remains low. We explain this by providing a local transformation that maps PEPS expectation values to a positive-valued tensor network. This not only provides insight into the origin of the observed boundary-law entanglement scaling but also suggests new approaches toward PEPS contraction based on positive decompositions. Published by the American Physical Society 2025

Chen, Jielun (ORCID:0000000178411545)↗