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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Directly Embedded Ni3S2/Co9S8@S-Doped Carbon Nanofiber Networks as a Free-Standing Anode for Lithium-Ion Batteries

Transition metal sulfides as electrode materials for lithium-ion batteries have attracted significant research attention due to their high theoretical capacity, excellent redox reversibility, and earth abundance. However, this material family still suffers from poor conductivity and experiences huge volume changes. Here, we demonstrate a facile and scalable electrospinning method to prepare Ni3S2 and Co9S8 nanoparticles embedded in sulfur doped carbon nanofiber networks as a free-standing anode material for lithium ion batteries. Similar to literature findings, the coupling of two different metal sulfides indeed synergistically promoted the electrochemical performance. Embedding them within individual carbon nanofibers not only enhances the intrinsic conductivity, but also provides a highly stable structure, which results in excellent battery performance. Furthermore, the individual carbon nanofibers intertwine with each other to form a free-standing 3D nanofiber network which acts as a freeway network for fast electron transfer and the pores between fibers allow easy penetration of the electrolyte, namely easy lithium ion access to active nanoparticles. When directly applied as the anode in lithium ion batteries, the free-standing nanofiber mat bypassed all slurry making steps and showed excellent cycling stability with a high specific capacity of 528 mA h g-1 after 200 cycles at a current density of 300 mA g-1. Good rate capability was also obtained. Additionally, the charge storage process analysis indicated that the pseudocapacitive behavior of the material is attributed to its good performance. This work introduces a facile strategy to simultaneously and in situ generate Co9S8 and Ni3S2 nanoparticles within a S-doped carbon fiber matrix via facile electrospinning followed by a one-step heating procedure. It is demonstrated that the free-standing transition bimetallic sulfide nanofibers prepared are very promising for light and small battery applications.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2024

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Methane Pyrolysis for CO2-free H2 and Carbon Nanomaterials (Abstract)

We propose to continue to develop a new process for producing CO2-free hydrogen (H2) from inexpensive and domestically-abundant natural gas (NG), while simultaneously reducing H2’s net production cost to $1.0/kg through the sale of valuable crystalline solid carbon co-product. Producing clean hydrogen at this price is a DOE Hydrogen Energy Earthshot goal. Cost effective production of clean H2 is also of commercial relevance to project partners Southern California Gas Company (SoCalGas) and startup company C4-MCP, who aim to further develop, demonstrate at scale, and ultimately deploy the new process technology developed on this project in order to meet regulatory demands in the State of California. In the current project we have focused on i) understanding the catalyst science for thermocatalytic decomposition of methane (TCD), which resulted in the development of a patent pending bimetallic catalyst offering favorable activity, stability, and selectivity under industrially relevant process conditions, ii) developing a novel, patent pending process to enable the separation of produced carbon and catalyst, and re-synthesis of the catalyst using recycled materials, iii) performing limited characterization of the produced carbon materials, and iv) performing detailed process modeling in order to perform techno economic assessment. The additional scope proposed here will accelerate the commercial deployment of TCD for CO2-free H2 and valuable solid carbon nanotubes (CNT) co-product, by i) scaling up the production of CNT co-product using a scalable, fluidized bed reactor (25 g catalyst scale versus the 1 g catalyst scale demonstrated to-date), ii) producing at least 40 g of CNT product, produced via multiple cycles of TCD, carbon-catalyst separation, and catalyst re-synthesis, to enable the production of sufficient quantities of solid carbon so as to explore its market potential, iii) understanding the quality of the co-product CNTs, produced at larger scale, through advanced characterization, and iv) beginning to explore multiple promising high volume carbon product applications (thermoplastics, automotive composites, battery, and cement reinforcement applications).

08 HYDROGEN↗
DOE OSTI2024

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Sim2Real Autonomous Robotic Exploration [Poster]

Autonomous robots offer promising solutions for exploration in environments that are inaccessible or hazardous to humans. Despite this, physical training of such robots is often constrained by safety risks, high cost or limited accessibility. This project presents an end-to-end simulation to reality pipeline leveraging Nvidia Isaac Sim and Boston Dynamics' Spot to enable autonomous navigation in indoor environments. A reinforcement learning policy is first trained using Nvidia Isaac Lab to establish Spot's locomotion pattern. Virtual LiDAR sensors are then integrated to perform SLAM-based navigation using simulated odometry. Finally, the simulated navigation scheme is transferred to a physical Spot robot to inspect and record images of a real-world room by repeating the learnt trajectory. The proposed framework highlights the potential of scalable training in simulation and reliable deployment in physical environments. Future directions include dynamic trajectory generation in unseen and challenging environments and integration of environmental sensing like temperature, radiation or humidity via sensor and material simulation.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Enhancing isoprenol production by systematically tuning metabolic pathways using CRISPR interference in E. coli

Regulation of metabolic gene expression is crucial for maximizing bioproduction titers. Recent engineering tools including CRISPR/Cas9, CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) have enabled effective knock-out, knock-down, and overexpression of endogenous pathway genes, respectively, for advanced strain engineering. CRISPRi in particular has emerged as a powerful tool for gene repression through the use of a deactivated Cas9 (dCas9) protein and target guide RNA (gRNA). By constructing gRNA arrays, CRISPRi has the capacity for multiplexed gene downregulation across multiple orthogonal pathways for enhanced bioproduction titers. In this study, we harnessed CRISPRi to downregulate 32 essential and non-essential genes in E. coli strains heterologously expressing either the original mevalonate pathway or isopentenyl diphosphate (IPP) bypass pathway for isoprenol biosynthesis. Isoprenol remains a candidate bioproduct both as a drop-in blend additive and as a precursor for the high-performance sustainable aviation fuel, 1,4-dimethylcyclooctane (DMCO). Of the 32 gRNAs targeting genes associated with isoprenol biosynthesis, a subset was found to vastly improve product titers. Construction of a multiplexed gRNA library based on single guide RNA (sgRNA) performance enabled simultaneous gene repression, yielding a 3 to 4.5-fold increase in isoprenol titer (1.82 ± 0.19 g/L) on M9-MOPS minimal medium. We then scaled the best performing CRISPRi strain to 2-L fed-batch cultivation and demonstrated translatable titer improvements, ultimately obtaining 12.4 ± 1.3 g/L isoprenol. Our strategy further establishes CRISPRi as a powerful tool for tuning metabolic flux in production hosts and that titer improvements are readily scalable with potential for applications in industrial bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation

This paper presents a methodology for using LLVM-based tools to tune the DCA++ (dynamical cluster approximation) application that targets the new ARM A64FX processor. The goal is to describe the changes required for the new architecture and generate efficient single instruction/multiple data (SIMD) instructions that target the new Scalable Vector Extension instruction set. During manual tuning, the authors used the LLVM tools to improve code parallelization by using OpenMP SIMD, refactored the code and applied transformation that enabled SIMD optimizations, and ensured that the correct libraries were used to achieve optimal performance. By applying these code changes, code speed was increased by 1.98× and 78 GFlops were achieved on the A64FX processor. The authors aim to automatize parts of the efforts in the OpenMP Advisor tool, which is built on top of existing and newly introduced LLVM tooling.

Huber, Joseph↗
DOE OSTI2024

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage

Industrial process heating (IPH) accounts for approximately 70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately 1500 TWht (approximately 60%) of IPH demand is in the temperature range of 100-300. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. TES development has historically been dominated by technologies suitable for deployment with concentrating solar power (CSP). State-of-the-art thermal storage deployed commercially with power tower CSP plants uses a 60%/40% NaNO3/KNO3 molten salt and operates between temperatures of approximately 280 degrees Celsius and 570 degrees Celsius using a two-tank configuration. However, these nitrate salts are unsuitable for operation outside of this temperature range due to a high freezing point of approximately 220 degrees Celsius, and limits on high-temperature salt stability and corrosion resistance of containment alloys. Other materials being investigated for TES include those based on: (1) sensible energy storage (various molten salt compositions, inert solid particles, rocks or pebble beds, sulfur, water, concrete, graphite, etc.), (2) latent energy storage in materials that undergo solid-liquid phase change at relevant temperatures (organic materials for low-temperature applications, inorganic salts and/or metals for high-temperature applications), or (3) thermochemical energy storage (hydrides, hydroxides, carbonates, metal oxides, etc.). The application temperature and challenges pertaining to storage material and/or containment cost, energy density, long-term thermal and cyclic stability, and charge/discharge heat transfer effectiveness drive material selection for a given IPH or electricity generation application. Sulfur is a cheap commodity at $80/ton compared to $1100 - 1300/ton for conventional salts. When using a metric of storage cost per kWh, sulfur costs around 2-3 $/kWh. Previous sulfur TES development focused on high temperature (>600 degrees) concentrated solar power applications with sulfur encapsulated in pipes and flow of gaseous HTF (air) in the shell side. However, for lower-temperature IPH applications in the range of approximately 100-300 degrees Celsius Element 16 adopted a compact and scalable TES design with molten sulfur in the shell and HTF pipes submerged in the molten sulfur bath. The low-cost molten sulfur TES for dispatchable IPH has deployment potential for broad applications. The spatial and temporal evolution of the HTF and sulfur temperature is critical to the TES system performance, and thus detailed modeling can improve understanding of the performance and facilitate design improvements. Using high performance computing and computational fluid dynamics (CFD) a low-cost molten sulfur thermal energy storage (TES) system for industrial process heating (IPH) applications was developed. The unique challenges in CFD modeling of sulfur TES are the sharp property changes of sulfur relevant to the working temperatures. Above 159, liquid sulfur undergoes polymerization, and the viscosity of sulfur rapidly increases by several orders of magnitude between 159 degrees Celsius and 188 degrees Celsius, followed by a decrease in viscosity beyond 188 degrees Celsius due to thermal bound dissociation. In addition, various concentrations of H2S impurities can also modify sulfur viscosity. This numerical challenge is especially relevant to transient simulation of the sulfur TES charging and discharging processes as the extreme property variations limit the applicability of traditional heat transfer correlations. Transient CFD simulations including the temperature-dependent sulfur properties and geometric complexity of the TES design were used to predict the effect of natural convection during charging and discharging on the heat transfer process, sulfur temperature uniformity, charge/discharge rates, and performance of the storage devices. The CFD model was validated with experimental results for a full charge and discharge cycle. The work will show 3D and 2D simulation comparisons aimed to facilitate rapid design iterations and a machine learning based design optimization approach.

CFD↗
DOE OSTI2023

Feasibility Evaluation of a Solid Phase Scalable HEA Cladding Manufacturing Route

First of a kind development result on two low-energy solid-phase processes applied on an irradiation-resistant alloy, NiCoFeCrCu 0.12 , are achieved and demonstrate moderate feasibility of successful tube fabrication using shear assisted processing and extrusion (ShAPE™) and friction stir layer deposition as a bulk manufacturing process. The scope of the work is performed in four phases: 1) direct tube manufacturing of the irradiation-resistant high-entropy alloy (HEA) composite with increased strength, 2) co-shear lining manufacturing process for the increased strength and corrosion-resistant, irradiation-tolerant HEAs, 3) ShAPE of the radially gradient corrosion resistance alloy, and 4) alloy development and fabrication enabled through friction stir additive manufacturing processes among others. This report describes the development activities from April to December 2023 to manufacture a direct customizable thin-walled tubular product from irradiation-tolerant composite high-entropy alloys (C-HEAs) while the overall project is continuing in 2024.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fabrication of Piezoelectric Polymer and Metal–Organic Framework Composite Thin Films Using Solution Shearing

Polymer-metal–organic framework (polymer-MOF) composites have garnered significant interest as polymers can enhance the processability and industrial applicability of MOFs. Thin films of these composites are particularly attractive for applications in sensing, separations, and flexible electronics. Solution shearing, a meniscus-guided coating technique, has emerged as a scalable process for fabricating thin films of MOFs, and can produce large-area films within minutes. In this study, we utilized solution shearing to fabricate composite thin films of a MOF UiO-66 and a piezoelectric polymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)), investigating how polymer concentration during MOF synthesis and composite formation influences thin film properties, including crystallinity, surface coverage, and piezoelectric performance. Additionally, solid-state NMR spectroscopy was utilized to probe the interactions between P(VDF-TrFE) and UiO-66 in the composite. Evidence from solid-state NMR indicated polymer-MOF interactions, suggesting that the polymer strands are in close proximity to the UiO-66 pores, supporting a mixed surface coating and pore infiltration model. Furthermore, incorporating P(VDF-TrFE) enhanced the film’s areal coverage from 70% to 100%. While the thermal conductivity remained essentially unchanged, the composite film showed an improved piezoelectric effect. The composite with 91 wt % P(VDF-TrFE) exhibited the highest output voltage of 9.1 V and a sensitivity of 0.26 V/N under applied pressure. This work demonstrates the potential of solution shearing as a scalable technique for fabricating polymer-MOF composite thin films.

P(VDF-TrFE)↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2021

Non-Solvent Induced Phase Separation Enables Designer Redox Flow Battery Electrodes

Porous carbonaceous electrodes are performance-defining components in redox flow batteries (RFBs), where their properties impact the efficiency, cost, and durability of the system. The overarching challenge is to simultaneously fulfill multiple seemingly contradictory requirements—i.e., high surface area, low pressure drop, and facile mass transport—without sacrificing scalability or manufacturability. Here, non-solvent induced phase separation (NIPS) is proposed as a versatile method to synthesize tunable porous structures suitable for use as RFB electrodes. The variation of the relative concentration of scaffold-forming polyacrylonitrile to pore-forming poly(vinylpyrrolidone) is demonstrated to result in electrodes with distinct microstructure and porosity. Tomographic microscopy, porosimetry, and spectroscopy are used to characterize the 3D structure and surface chemistry. Flow cell studies with two common redox species (i.e., all-vanadium and Fe2+/3+) reveal that the novel electrodes can outperform traditional carbon fiber electrodes. It is posited that the bimodal porous structure, with interconnected large (>50 µm) macrovoids in the through-plane direction and smaller (<5 µm) pores throughout, provides a favorable balance between offsetting traits. Although nascent, the NIPS synthesis approach has the potential to serve as a technology platform for the development of porous electrodes specifically designed to enable electrochemical flow technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Synthesis of Hyperbranched Polymers via PET-RAFT Self-Condensing Vinyl Polymerization in a Flow Reactor

Photoinduced polymerization techniques have been used in preparing various polymeric materials as they share several advantages with thermally-triggered processes using mild conditions, low catalyst concentrations, and easy-to-perform experimental conditions. By combining photoinduced eletrotransfer reversible addition–fragmentation chain transfer (RAFT) polymerization with self-condensing vinyl polymerization (SCVP), in this work, a series of hyperbranched polymers is prepared in a flow reactor. Considered to be an alternative powerful method in improving scalability, reliability, and efficiency, the use of a flow reactor allowed us to synthesize hyperbranched poly(poly(ethylene glycol methyl ether)acrylate) (PPEGMEA) under extremely mild conditions–room temperature, open vessel, and use of LED light and water/methanol mixed solutions. Finally, the method also demonstrates considerable controllability in molecular weight and branching density by adjusting the feeding ratio of monomer to transmer, leading to the synthesis of block copolymers.

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

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
DOE OSTI2025

Laser activation of single group-IV colour centres in diamond

Abstract Spin-photon interfaces based on group-IV colour centres in diamond offer a promising platform for quantum networks. A key challenge in the field is realising precise single-defect positioning and activation, which is crucial for scalable device fabrication. Here we address this problem by demonstrating a two-step fabrication method for tin vacancy (SnV − ) centres that uses site-controlled ion implantation followed by local femtosecond laser annealing with in-situ spectral monitoring. The ion implantation is performed with sub-50 nm resolution and a dosage that is controlled from hundreds of ions down to single ions per site, limited by Poissonian statistics. Using this approach, we successfully demonstrate site-selective creation and modification of single SnV − centres. Our in-situ spectral monitoring opens a window onto materials tuning at the single defect level, and provides new insight into defect structures and dynamics during the annealing process. While demonstrated for SnV − centres, this versatile approach can be readily generalised to other implanted colour centres in diamond and wide-bandgap materials.

Science & Technology - Other Topics↗
DOE OSTI2022

A Tri-Port Current-Source Soft-Switching Medium-Voltage String Inverter for Large-Scale Solar-Plus-Storage Farms

This article presents a tri-port current-source soft-switching medium-voltage string inverter (TMVSI) to reduce the Levelized cost of energy (LCOE) of large-scale solar-plus-storage (SPS) farms. Throughout this work, the topology, operating principle, circuit simulation, control, and hardware design of the TMVSI are introduced. In addition, three key challenges compromising the performance of the TMVSI are addressed in this article. First, a new feed-forward compensation for model predictive control of the TMVSI is proposed to compensate for sampling and computational delay and high dc-link ripple with low computational cost and high scalability. Second, the use of laminated permanent magnets is proposed to reduce the losses of the MV medium-frequency transformer (MFT) in the TMVSI while increasing its saturation current. Finally, an improved U-shape winding pattern is proposed to decrease the leakage inductance of the MV MFT and reduce the voltage stress across semiconductor switches without adding cost or complexity. Here, the effectiveness of the TMVSI is validated by experiments at up to 20kW/500V in different test cases with custom-built prototypes. The contributions of this work make the TMVSI a viable solution to SPS farms and unleash its potential for LCOE reduction.

14 SOLAR ENERGY↗
DOE OSTI2021

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Generation and characterization of ultrabroadband polarization–frequency hyperentangled photons

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530–1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks.

42 ENGINEERING↗