Search NASA⌕ Search

SEARCH · Search NASA

Results for “Rep”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydrogen generation using a fuel cell system with an REP

A fuel cell system includes a fuel cell having an anode and a cathode configured to output cathode exhaust. The fuel cell is configured to generate waste heat. The fuel cell system further includes a reformer configured to partially reform a feed gas using the waste heat and output a hydrogen-containing stream. The fuel cell system further includes a reformer-electrolyzer-purifier (“REP”) having an REP anode configured to receive a first portion of the hydrogen-containing stream and an REP cathode.

Jahnke, Fred C.↗
DOE OSTI2024

Self-duality under gauging a non-invertible symmetry

Abstract We discuss two-dimensional conformal field theories (CFTs) which are invariant under gauging a non-invertible global symmetry. At every point on the orbifold branch ofc= 1 CFTs, it is known that the theory is self-dual under gauging a ℤ 2 × ℤ 2 symmetry, and has Rep(H 8 ) and Rep(D 8 ) fusion category symmetries as a result. We find that gauging the entire Rep(H 8 ) fusion category symmetry maps the orbifold theory at radiusRto that at radius 2/R. AtR=$$ \sqrt{2} $$ 2 , which corresponds to two decoupled Ising CFTs (Ising 2 in short), the theory is self-dual under gauging the Rep(H 8 ) symmetry. This implies the existence of a topological defect line in the Ising 2 CFT obtained from half-space gauging of the Rep(H 8 ) symmetry, which commutes with thec= 1 Virasoro algebra but does not preserve the fully extended chiral algebra. We bootstrap its action on thec= 1 Virasoro primary operators, and find that there are no relevant or marginal operators preserving it. Mathematically, the new topological line combines with the Rep(H 8 ) symmetry to form a bigger fusion category which is a ℤ 2 -extension of Rep(H 8 ). We solve the pentagon equations including the additional topological line and find 8 solutions, where two of them are realized in the Ising 2 CFT. Finally, we show that the torus partition functions of the Monster 2 CFT and Ising×Monster CFT are also invariant under gauging the Rep(H 8 ) symmetry.

Physics↗
DOE OSTI2022

Watson-Crick Base-Pairing Requirements for ssDNA Recognition and Processing in Replication-Initiating HUH Endonucleases

Replication-initiating HUH endonucleases (Reps) are sequence-specific nucleases that cleave and rejoin single-stranded DNA (ssDNA) during rolling-circle replication. These functions are mediated by covalent linkage of the Rep to its substrate post cleavage. Here, we describe the structures of the endonuclease domain from the Muscovy duck circovirus Rep in complex with its cognate ssDNA 10-mer with and without manganese in the active site. Structural and functional analyses demonstrate that divalent cations play both catalytic and structural roles in Reps by polarizing and positioning their substrate. Further structural comparisons highlight the importance of an intramolecular substrate Watson-Crick (WC) base pairing between the -4 and +1 positions. Subsequent kinetic and functional analyses demonstrate a functional dependency on WC base pairing between these positions regardless of the pair’s identity (i.e., A·T, T·A, G·C, or C·G), highlighting a structural specificity for substrate interaction. Finally, considering how well WC swaps were tolerated in vitro, we sought to determine to what extent the canonical -4T·+1A pairing is conserved in circular Rep-encoding single-stranded DNA viruses and found evidence of noncanonical pairings in a minority of these genomes. Altogether, our data suggest that substrate intramolecular WC base pairing is a universal requirement for separation and reunion of ssDNA in Reps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Synthetic Tuning of Exciton–Phonon Coupling in Janus WS 2(1-x) Se 2x Monolayers Revealed by Resonant Raman Excitation Spectroscopy for Optoelectronic Applications

Janus monolayers, such as WSSe, have broken out-of-plane symmetry and an intrinsic dipole moment, impacting exciton transport, lifetime, and phonon interactions while imbuing piezoelectric, photocatalytic, and Rashba spin-splitting properties to transition metal dichalcogenides (TMDs). The new properties of this atomically thin material can be used for optoelectronic device applications. As TMDs are converted into Janus monolayers, e.g., top selenization of WS2 to WSSe, the bandgap and structure smoothly evolve, impacting not only the formation of excitons but also their complex interactions with different phonon modes. Resonant Raman excitation profiles (REPs) are uniquely well-suited to reveal both excitonic transitions and exciton–phonon coupling. Here, the resonant REPs of $A^{'}_{1}$ WS 2 and A 1 WSSe modes are measured to understand the strength of their coupling with the A, B, and C excitonic bands of a WS2 monolayer throughout its stepwise transformation into Janus WSSe by pulsed laser deposition (PLD) of energetic selenium species. In situ Raman spectroscopy during deposition is used to controllably prepare stable intermediate Janus structures, WS 2(1-x) Se 2x (0 ≤ x ≤ 0.5), for ex situ measurement of their resonant REPs. As x increases, REPs reveal not only pronounced excitonic bands that gradually shift toward lower photon energies but also strong, mode-selective exciton–phonon coupling. First-principles resonant Raman simulations independently predict this spectral behavior and are shown capable of matching the spectrally broadened, experimentally observed REP profiles in this model system, indicating their strong predictive capability for future experiments. The combination of controlled synthesis, REP characterization, and predictive theory employed here demonstrates a powerful pathway to understand and ultimately tune exciton–phonon interactions for future quantum optical devices.

Janus monolayers↗
DOE OSTI2023

Next steps in high-repetition-rate laser development for Thomson scattering

Design of a next generation high-rep-rate laser system is underway, aiming for a maximum rep rate of 100 kHz for 1 ms. This will be a "pulse-burst" laser, which is a type of heat-capacity laser. Heat-capacity laser operation is characterized by a burst of pulses of limited duration, with burst length ≤100 ms and pulse rep rate ≥1 kHz. Waste heat accumulates in the laser rod during the burst. This heat is deposited evenly throughout the rod volume, with very little heat removed during the burst, such that temperature rises evenly across the rod radius. Thus beam distortion due to thermal gradients is small. Heat is removed from the rod after the burst, with typically tens of seconds between bursts. Pulse-burst operation of flashlamp pumped Nd:YAG lasers is a cost-effective route to high-rep-rate capability. Pulse-burst laser systems with "fast burst" rep rates in the range of 10–20 kHz have been built for the Thomson scattering diagnostics on MST, NSTX-U, and LHD. For Thomson scattering diagnostic application, the typical requirements are 1064 nm, 1–2 J/pulse, ≤30 ns FWHM pulse, with a top-hat beam profile. A major requirement for this next generation laser system is flexibility in burst sequence programs, ranging from 1 kHz for 100 ms to 100 kHz for 1 ms, and a variety of scenarios in between so that operation can be tailored to plasma experiment requirements. Flashlamp pumping will be used for this next generation laser because it is inexpensive and flexible. A new switch-regulated flashlamp driver will provide improved flashlamp control at lower cost. Additional design issues such as the optimum flashlamp pumping spectrum and optimum Nd doping will be addressed. The use of commercial off-the-shelf components will be maximized in this laser system so that pulse-burst systems can be developed and built by others for new applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Identification of recG genetic interactions in Escherichia coli by transposon sequencing

ABSTRACT Maintaining the integrity of the genome is of utmost importance for cell division and propagation. In Escherichia coli , the RecG protein has been implicated in processing branched recombination intermediates during DNA repair processes, but the primary cellular role(s) of RecG and the repair pathways in which it acts have been difficult to define. To gain additional insight into RecG function, we employed transposon sequencing to identify recG genetic interactions and reveal complementary or redundant functions. The strongest hits from the screen were the dam , uvrD , rnhA , radA , and rep genes. The conditional importance of these five genes in cells lacking recG was confirmed using a plasmid-based assay, revealing synthetic lethal interactions for most double deletion strains. Several of the synthetic lethal gene combinations (with uvrD, rep, and radA , but not rnhA or dam ) were suppressed by deletion of recF or recO , indicating that their genetic relationships involved roles in post-replication gap repair. Additionally, loss of the RecG/SSB interaction phenocopied a recG deletion when combined with dam , uvrD , radA , or rnhA deletions but not with rep . The results reinforce the idea of RecG as a general genome guardian. RecG has at least two functions. It plays an important role in the resolution of joint molecules behind the fork, formed during post-replication gap repair. RecG is also required to suppress genome over-replication caused by unscheduled replication initiation at R-loops, at double-strand breaks caused by dam inactivation, and during replication termination. IMPORTANCE DNA damage and subsequent DNA repair processes are mutagenic in nature and an important driver of evolution in prokaryotes, including antibiotic resistance development. Genetic screening approaches, such as transposon sequencing (Tn-seq), have provided important new insights into gene function and genetic relationships. Here, we employed Tn-seq to gain insight into the function of the recG gene, which renders Escherichia coli cells moderately sensitive to a variety of DNA-damaging agents when they are absent. The reported recG genetic interactions can be used in combination with future screens to aid in a more complete reconstruction of DNA repair pathways in bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

(SPT-)LSM theorems from projective non-invertible symmetries

Projective symmetries are ubiquitous in quantum lattice models and can be leveraged to constrain their phase diagram and entanglement structure. In this paper, we investigate the consequences of projective algebras formed by non-invertible symmetries and lattice translations in a generalized 1+1 1 + 1 D quantum XY model based on group-valued qudits. This model is specified by a finite group G G and enjoys a projective \mathsf{Rep}(G)× Z(G) 𝖱 𝖾 𝗉 ( G ) × Z ( G ) and translation symmetry, where symmetry operators obey a projective algebra in the presence of symmetry defects. For invertible symmetries, such projective algebras imply Lieb-Schultz-Mattis (LSM) anomalies. However, this is not generally true for non-invertible symmetries, and we derive a condition on G G for the existence of an LSM anomaly. When this condition is not met, we prove an SPT-LSM theorem: any unique and gapped ground state is necessarily a non-invertible weak symmetry protected topological (SPT) state with non-trivial entanglement, for which we construct an example fixed-point Hamiltonian. The projectivity also affects the dual symmetries after gauging \mathsf{Rep}(G)× Z(G) 𝖱 𝖾 𝗉 ( G ) × Z ( G ) sub-symmetries, giving rise to non-Abelian and non-invertible dipole symmetries, as well as non-invertible translations. We complement our analysis with the SymTFT, where the projectivity causes it to be a topological order non-trivially enriched by translations. Throughout the paper, we develop techniques for gauging \mathsf{Rep}(G) 𝖱 𝖾 𝗉 ( G ) symmetry and inserting its symmetry defects on the lattice, which are applicable to other non-invertible symmetries.

Pace, Salvatore D. (ORCID:0000000306093335)↗
DOE OSTI2023

Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations

We introduce and investigate, for finite groups G, G-invariant deep neural network (GDNN) architectures with ReLU activation that are densely connected- i.e., include all possible skip connections. In contrast to other G-invariant architectures in the literature, the preactivations of theG-DNNs presented here are able to transform by signed permutation representations (signed perm-reps) of G. Moreover, the individual layers of the G-DNNs are not required to be G-equivariant; instead, the preactivations are constrained to be G-equivariant functions of the network input in a way that couples weights across all layers. The result is a richer family of G-invariant architectures never seen previously. We derive an efficient implementation of G-DNNs after a reparameterization of weights, as well as necessary and sufficient conditions for an architecture to be "admissible"- i.e., nondegenerate and inequivalent to smaller architectures. We include code that allows a user to build a G-DNN interactively layer-by-layer, with the final architecture guaranteed to be admissible. We show that there are far more admissible G-DNN architectures than those accessible with the "concatenated ReLU" activation function from the literature. Finally, we apply G-DNNs to two example problems--(1) multiplication in --1, 1} (with theoretical guarantees) and (2) 3D object classification--finding that the inclusion of signed perm-reps significantly boosts predictive performance compared to baselines with only ordinary (i.e., unsigned) perm-reps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Projective Representations, Bogomolov Multiplier, and Their Applications in Physics

We present a pedagogical review of projective representations of finite groups and their physical applications in quantum many-body systems. Some of our physical results are new. We begin with a self-contained introduction to projective representations, highlighting the role of group cohomology, representation theory, and classification of irreducible projective representations. We then focus on a special subset of cohomology classes, known as the Bogomolov multiplier, which consists of cocycles that are symmetric on commuting pairs but remain nontrivial in group cohomology. Such cocycles have important physical implications: they characterize (1+1)D SPT phases that cannot be detected by string order parameters and give rise, upon gauging, to distinct gapped phases with completely broken non-invertible Rep(G) symmetry. We construct explicit lattice models for these phases and demonstrate how they are distinguished by the fusion rules of local order parameters. We show that a pair of completely broken Rep(G) SSB phases host nontrivial interface modes at their domain walls. As an example, we construct a lattice model where the ground state degeneracy on a ring increases from 32 without interfaces to 56 with interfaces.

Bogomolov multiplier↗
DOE OSTI2026

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Medrano Sandonas, Leonardo [Technische Universität↗
DOE OSTI2024

Superfast quarks in deuterium

An extension to our previous study on nuclear parton distribution functions (nPDFs) [Kim and Miller, Phys. Rev. C 106, 055202 (2022)] using light-front holographic quantum chromodynamics (LFHQCD) [Brodsky, de Teramond, Dosch, and Erlich, Phys. Rep. 584, 1 (2015)] is presented. We apply the effects of nucleon motion inside the nucleus (Fermi motion/smearing) to deuterium, extending our deuterium nPDFs to the superfast , 𝑥 > 1, region [Frankfurt and Strikman, Phys. Rep. 160, 235 (1988)] where we estimate our results to be reasonable up to 𝑥 ≈ 1.7. We utilize four different deuteron wave functions (AV18, NijmI, NijmII, Nijm93). We find that our model, with no additional new parameters, shows very good agreement with deuterium EMC ratio data obtained from the BONuS experiment [Fenker et al., Nucl. Instrum. Methods Phys. Res. A 592, 273 (2008); Baillie et al. (CLAS Collaboration), Phys. Rev. Lett. 108, 142001 (2012); Phys. Rev. Lett. 108, 199902(E) (2012); Tkachenko et al. (CLAS Collaboration), Phys. Rev. C 89, 045206 (2014); Phys. Rev. C 90, 059901(E) (2014); Griffioen et al., Phys. Rev. C 92, 015211 (2015)]. Looking beyond conventional nuclear physics, and in anticipation of 12 GeV experiments at Jefferson Lab, we use a LFHQCD ansatz to predict the contributions of an exotic six-quark state to the deuteron 𝐹 2 structure function, 𝐹$^D_2$, in the superfast region. We find that the effects of using other potentials are about the same magnitude as six-quark effects—both have small effects in 𝑥 < 1, but have significant contributions at 𝑥 > 1.

Physics↗
DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗