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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Abstraction of Hydride from Alkanes and Dihydrogen by the Perfluorotrityl Cation

Abstract Lewis acids play a central role in a large variety of chemical transformations. The reactivity of the strongest Lewis acids is typically studied in the context of affinity towards hard bases, such as fluoride or oxygenous species. Carbocations can be viewed as soft Lewis acids, possessing significant affinity for softer bases, such as hydride. This work presents the ambient‐temperature isolation of salts of the perfluorotrityl cation ((C 6 F 5 ) 3 C + or F 15 Tr + ) in combination with halogenated carborane anions. The F 15 Tr + cation exhibits remarkable hydride affinity, illustrated by the observation of hydride abstraction from dihydrogen, and of the rapid abstraction of hydride from −CH 2 −groups in alkanes. Theoretical studies support the favorability of hydride abstraction from dihydrogen, and indicate that the hydride abstraction from alkanes proceeds via a concerted hydride transfer process that is sensitive to steric effects.

Leong, Derek W. [Department of Chemistry Texas A&a↗
DOE OSTI2025

Abstraction of Hydride from Alkanes and Dihydrogen by the Perfluorotrityl Cation

Abstract Lewis acids play a central role in a large variety of chemical transformations. The reactivity of the strongest Lewis acids is typically studied in the context of affinity towards hard bases, such as fluoride or oxygenous species. Carbocations can be viewed as soft Lewis acids, possessing significant affinity for softer bases, such as hydride. This work presents the ambient‐temperature isolation of salts of the perfluorotrityl cation ((C 6 F 5 ) 3 C + or F 15 Tr + ) in combination with halogenated carborane anions. The F 15 Tr + cation exhibits remarkable hydride affinity, illustrated by the observation of hydride abstraction from dihydrogen, and of the rapid abstraction of hydride from −CH 2 −groups in alkanes. Theoretical studies support the favorability of hydride abstraction from dihydrogen, and indicate that the hydride abstraction from alkanes proceeds via a concerted hydride transfer process that is sensitive to steric effects.

Leong, Derek W. [Department of Chemistry Texas A&a↗
DOE OSTI2025

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2026

Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications

This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Scalable Computation of Topological Abstractions for Scalar Data

Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set‐based, graph‐based and complex‐based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

CRADA Abstract 2025-01U-JWS01

CRADA abstract for collaboration with Washington State University on shock physics research. Public abstract at time of execution is required per DOE O 483.1B.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

CRADA Abstract - 2023-01U-JWS04

Abstract for CRADA joint work statement 4 with UNLV to support the BUILTT for DOE project. CRADA abstract is required to be submitted to OSTI after execution of agreement per DOE O 483.1B.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

CRADA Abstract - 2023-01U-JWS03

CRADA abstract 2023-01U-JWS03 with UNLV. Public abstract after execution is required per DOE O 483.1B

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Fortran mimetic abstraction language (Formal) v0.1.

The Fortran mimetic abstraction language ("Formal") is a domain-specific language (DSL) embedded in Fortran 202Y [1]. Formal provides novel software abstractions for simulating phenomena governed by the partial differential equations (PDEs) of vector and tensor calculus. Such equations model an extremely broad set of physical phenomena, ranging from atmospheric winds to light propagation. Formal's data structures and algorithms mimic in form and behavior continuous functions and operators. Formal supports these mathematical constructs using mimetic discretizations that define a discrete calculus satisfying various tensor calculus theorems, thereby ensuring high-fidelity representations of the physics being modeled. [2] Formal 0.1.0 also lays a foundation for the future use of Fortran 202Y type-safe templates to facilitate the formal verification of tensor contractions in computational physics and artificial intelligence [3]. [1] "Fortran 202Y" is Fortran standard committee's informal designation for the next Fortran revision, which will likely be "Fortran 2028". [2] Corbino, J. and Castillo, J. (2020) Journal of Computational and Applied Mathematics, https://doi.org/10.1016/j.cam.2019.06.042. [3] Haveraaen, M., Järvi, J., & Rouson, D. (2019). Reflecting on Generics for Fortran. https://j3-fortran.org/doc/year/19/19-188.pdf.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2025

2024-03U-JWS01 - Initial Abstract

Abstract for recently approved CRADA joint work statement 2024-03U-JWS01 with University of Nevada Reno. Publication of abstract at time of approval satisfies requirements of DOE O 483.1B.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

CRADA Abstract - 2025-03M

CRADA abstract for publication as required by DOE O 483.1B

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.

Kim, Haseong↗