eagles-project/haero
A toolbox for constructing performance portable aerosol packages
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A toolbox for constructing performance portable aerosol packages
Frontal ring-opening metathesis polymerization (FROMP) is a rapid, low-energy manufacturing reaction that is useful for curing thermosetting materials. FROMP of dicyclopentadiene (DCPD) results in poly(dicyclopentadiene) (p(DCPD)), a tough thermoset with excellent mechanical performance and chemical stability. Like most thermosets, p(DCPD) cannot be reprocessed and is therefore difficult to recycle. Previous work demonstrated that the incorporation of a small quantity of cleavable units in the strand segments of p(DCPD) networks enables their deconstruction. Here, we report that a commercially available multifunctional comonomer, 2,3-dihydrofuran (DHF), both acts as a potent Grubbs catalyst inhibitor during FROMP and introduces acid cleavable units. The resulting materials retain high performance characteristics, including glass-transition temperatures ranging from 115 to 165 °C and ultimate strength ranging from 35 to 40 MPa. The addition of DHF above critical loading levels enables deconstructable thermosets. Here, we further demonstrate freeform three-dimensional (3D) printing of deconstructable thermosets via frontal polymerization.
The Hanford Site stores an estimated 56 million gallons of mixed radioactive and chemically hazardous waste in large underground tanks. In support of the Direct Feed Low-Activity Waste (DFLAW) Program for expediting Hanford tank waste supernate treatment, laboratory-scale ion exchange processing using prototypic unit operations was conducted on AW-105 tank waste at the Pacific Northwest National Laboratory Radiochemical Processing Laboratory. This report describes the small-scale ion exchange testing with 9.2 L of diluted and filtered supernate from Tank 241-AW-105 (hereafter referred to as AW-105) at 16 °C (62 °F). One of the waste acceptance criteria (WAC) for the Waste Treatment Plant (WTP) Low-Activity Waste Facility is that the waste must contain less than 3.18×10 -5 Ci 137 Cs per mole of Na. For the AW-105 tank waste to meet this criterion, only 0.225% of the influent 137 Cs concentration may be delivered to the WTP; this requires a Cs decontamination factor of 445. Testing with AW-105 matched current Tank Side Cesium Removal (TSCR) facility prototypic operations where a lead-lag configuration was used until the lag column reached the WAC limit, then a polish column was brought online for continued processing in a lead-lag-polish column configuration. Feed was processed at 1.9 bed volumes (BVs) per hour; the flowrate, in terms of contact time with the crystalline silicotitanate (CST) bed, matched the expected flowrate at TSCR. The Cs-decontaminated product was retained for vitrification testing (to be reported separately). The lead column reached 83% Cs breakthrough after processing ~1500 BVs of feed; the 50% Cs breakthrough was interpolated from the breakthrough data and occurred at 1041 BVs. Despite the AW-105 having a significantly higher K concentration (0.55 M compared to 0.10 M), testing compared to previous AP-107 ion exchange column testing at 16 °C showed no difference in BVs processed to reach the WAC on the lead column and only an approximate ~20 BV decrease in volume processed to reach the WAC limit on the lag column. The negligible differences in capacity despite the 5x concentration differences in K was determined to be due to the significantly lower NO3 concentration in the AW-105 supernate compared to the AP-107 tank waste matrix. A comparison in breakthrough curves for the two tests also indicated slightly faster kinetic behavior in the AW-105, with the variations in feed matrices (lower NO3 concentration) likely responsible for the deviation. The Cs effluent from the lag column reached the WAC limit after processing 772 BVs. Anticipating this breakthrough point, the polish column was preemptively installed around 675 BVs. Cs breakthrough from the lag column began at 300 BVs, reaching 1.10×10 1 µCi/mL, or 14.13 % Cs breakthrough, after processing all 1500 BVs of feed. The polish column processed nominally 830 BVs and reached 2.10×10 -1 µCi/mL, or 0.27 % Cs breakthrough at the conclusion of the test. Table S.1 and Figure S.1 summarize the observed column performance and relevant Cs loading characteristics.
Large-scale, low -cost hydrogen production can enable an economically competitive, secure, and environmentally beneficial future energy system across multiple sectors. Furthermore, clean hydrogen can address specific sectors that are hard to decarbonize (e.g., heavy-duty trucking, load-following electricity, iron, steel, and cement) and can help the U.S. meet the net zero carbon goal by 2050. To achieve this goal, tens of millions of metric tons of clean, reliable, and affordable hydrogen will be needed annually1. In 2021, the Hydrogen Energy Earthshot was launched, and its goal is to reduce the cost of clean hydrogen to $1 per $1 kilogram in 1 decade (1 1 1) 2. One very promising pathway for large-scale hydrogen production is water splitting. Water splitting technologies range from commercial technologies such as electrolyzers to approaches that are at a much earlier stage of development, such as photoelectrochemical (PEC) and thermochemical (TCH) processes. All these water splitting pathways offer diverse benefits in energy storage, grid services, and cross-sector emissions reductions while taking advantage of the diverse domestic resources. However, critical materials-, component- and system-level challenges must be addressed to improve efficiency and durability and reduce cost. To address these barriers and move these promising and high impact technologies forward, the HydroGEN Advanced Water Splitting Materials (AWSM) and the H2 from the Next-generation of Electrolyzers of Water (H2NEW) consortia were formed and supported by the Department of Energy (DOE) EERE Hydrogen and Fuel Cell Technologies Office (HFTO). HydroGEN (https://www.energy.gov/eere/h2awsm/) consortium, established in 2016, is an Energy Materials Network (EMN) that aims to accelerate the materials R&D of low technology readiness level (TRL) advanced water splitting (AWS) technologies. The consortium comprises five core national laboratories and focuses on four early-stage AWS pathways: alkaline exchange membrane (AEM) electrolysis, proton conducting solid oxide electrolysis (p-SOEC), photoelectrochemical, and thermochemical water splitting. Liquid alkaline and PEM electrolyzers are already commercial and significant advancements in oxygen conducting solid oxide electrolysis cells (o-SOECs) have been realized. Yet, these systems are still too expensive and not sufficiently durable for wide-scale commercialization. To enable high-volume manufacturing of affordable, durable, efficient electrolyzers, H2NEW (https://h2new.energy.gov/), another multi-lab consortium, was established in 2020. This comprehensive, concerted effort is focused on overcoming barriers related to components and materials integration and scale-up to achieve performance, durability, with an initial focus to achieve $2/kg H2 by 2026.
Optical cavities are widely used in modern science and technology to enable a wide range of both quantum and classical applications. Recently, the growing demand for miniaturization and high performance has fueled the exploration of new fabrication methods beyond traditional polishing techniques and macroscopic mirrors. Visible and near-infrared (NIR) wavelengths are particularly important for quantum applications, where achieving low-loss resonators is also more challenging than in the telecom range, presenting unique challenges and opportunities for microscopic cavity systems. Here, we present a fabrication method for making NIR microcavities using buckled dielectric membrane mirrors, achieving a record finesse of 0.9 million at 780 nm for microcavities. We demonstrated flexible device geometries—including singular mirrors and mirror arrays, featuring radii of curvature ranging from 1 to 10 mm. The fabrication process offers high uniformity, high yield, and robust performance across a wide range of cavity lengths. Additionally, we can produce easy-to-assemble microcavity packages, with a total volume of ~2(4) mm 3 , featuring optical modes with a linewidth of 5.16 MHz (570 kHz) and a free spectral range (FSR) of 3.18 THz (150 GHz). Our results extend the frontier of microcavity fabrication for classical and quantum photonic technologies.
This report describes a cloud-based implementation and field demonstration of the Eastern Interconnection Situational Awareness and Monitoring System (ESAMS). ESAMS was developed to support the detection and source localization of forced oscillations using synchrophasor measurements from tie-lines connecting areas served by different reliability coordinators (RCs), so that RCs could better coordinate their response to wide-area events. A previous effort had identified deployment barriers associated with hosting shared situational awareness tools at a single RC. To address these barriers, ESAMS was migrated to Amazon Web Services and evaluated in a six-month field demonstration. ISO New England (ISO-NE) and PJM streamed data to the platform using AWS Direct Connect and a site-to-site VPN, respectively. The resulting multi-utility measurement footprint enabled regional source localization across major portions of the U.S. Eastern Interconnection and supported routine identification of oscillation events. During the final three months of the trial, 24 events above 2 MW/MVAR were detected. The largest detected oscillation approached a 25 MW peak-to-peak amplitude, and the longest persisted intermittently for more than 11 hours. The demonstration also assessed operational considerations—including data transfer volumes, end-to-end latency, and cloud computing costs—and found that network and compute requirements were modest relative to typical cloud capabilities while providing performance comparable to prior on-premises deployments. Overall, the results indicate that cloud hosting can provide a practical path to shared interconnection-wide oscillation monitoring. The cloud ESAMS demonstration establishes a foundation for broader utility participation and for building future wide-area analytics that leverage measurements across organizational boundaries.
Semiconducting absorbers in high-performance short-wave infrared (SWIR) photodetectors and imaging sensor arrays are dominated by single-crystalline germanium and III-V semiconductors. However, these materials require complex growth and device fabrication procedures. In this work, thermally evaporated Se x Te 1- x alloy thin films with tunable bandgaps for the fabrication of high-performance SWIR photodetectors are reported. From absorption measurements, it is shown that the bandgaps of Se x Te 1- x films can be tuned continuously from 0.31 eV (Te) to 1.87 eV (Se). Owing to their tunable bandgaps, the peak responsivity position and photoresponse edge of Se x Te 1- x film-based photoconductors can be tuned in the SWIR regime. By using an optical cavity substrate consisting of Au/Al 2 O 3 to enhance its absorption near the bandgap edge, the Se 0.32 Te 0.68 film (an optical bandgap of ≈0.8 eV)-based photoconductor exhibits a cut-off wavelength at ≈1.7 μm and gives a responsivity of 1.5 AW -1 and implied detectivity of 6.5 × 10 10 cm Hz 1/2 W -1 at 1.55 μm at room temperature. Importantly, the nature of the thermal evaporation process enables the fabrication of Se 0.32 Te 0.68 -based 42 × 42 focal plane arrays with good pixel uniformity, demonstrating the potential of this unique material system used for infrared imaging sensor systems.
The drive to reduce fuel consumption in transportation has encouraged the emergence of low viscosity lubricants to reduce viscous losses in the engine, drivetrain, and other components. However, viscosity reduction increases the risk of surface damage, thus motivating the development of new anti-wear (AW) additives. Capped ZrO 2 nanocrystals (NCs) in base oils have been shown to form AW tribofilms within microscale sliding contacts. However, the potential of ZrO 2 NCs to protect surfaces subjected to rolling-sliding contact from macroscale damage, such as micro-pitting, remains unexplored. Here, we explore the ability of ZrO 2 NCs to form protective tribofilms under harsh conditions using a micro-pitting rig (MPR), consisting of a three ring-on-roller configuration. The experiments were conducted in polyalphaolephin (PAO) base oil, with and without 5 nm diameter ZrO 2 NCs, at two levels of slide-to-roll ratio (SRR) (30% and 0%) and at variable test durations up to long durations (119 h). MPR results showed the use of ZrO 2 NCs gives rise to the formation of a tribofilm covering the roller surfaces and decreases the initiation and propagation of micro-pits compared to tests in pure PAO base stock. Transmission electron microscopy (TEM) performed on focused ion beam (FIB) milled cross-sectional samples of the roller surfaces revealed the growth of dense and 50–100 nm thick ZrO 2 -based tribofilms independent of (SRR), indicating the potential for robust micro-pitting fatigue protection. Nevertheless, small cracks localized within the near surface region of the roller tested at the most severe conditions (30% SRR and 119 h) were observed. The initiation of these cracks was directly related to the presence of manganese sulphide (MnS) inclusions in the steel, revealed using TEM combined with energy dispersive spectroscopy (EDS). Furthermore, the results highlight the benefits of the protective tribofilms formed by ZrO 2 NCs and suggest approaches for further optimizing their use.
Quantum error-correction codes protect information from realistic noisy channels and lie at the heart of quantum computation and communication tasks. Understanding the optimal performance and other information-theoretic properties, such as the achievable rates, of a given code is crucial, as these factors determine the fundamental limits imposed by the encoding in conjunction with the noise channel. Here, we use the transpose channel to analytically obtain the near-optimal performance of any Gottesman-Kitaev-Preskill (GKP) code under pure loss and pure amplification. We present rigorous connections between GKP code’s near-optimal performance and its dual lattice geometry and average input energy. With no energy constraint, we show that when |𝜏/(1−𝜏)| is an integer, specific families of GKP codes simultaneously achieve the loss and amplification capacity. 𝜏 is the transmissivity (gain) for loss (amplification). Our results establish GKP code as the first structured bosonic code family that achieves the capacity of loss and amplification.
Lithium niobate (LN) is a promising material for building acoustic resonators used in quantum applications, but its performance is limited by poorly understood material defects called two-level systems (TLSs). Here, in this work, we fabricate high-performance acoustic resonators from LN with quality factors up to 6 ×10 7 and use them to separate bulk and surface contributions to TLS loss. By comparing these bulk acoustic wave (BAW) resonators with previous surface-acoustic-wave and phononic-crystal studies, we show that devices with high surface-participation ratios are limited by surface TLSs, while our BAW devices reveal an intrinsic bulk TLS limit. Through systematic surface treatments and microscopy, we demonstrate that BAW resonator performance remains unchanged despite surface modifications, confirming operation in a bulk-limited regime. Here, our work establishes quantitative bounds on both surface and bulk TLS losses in LN within the context of the material growth and fabrication approaches we have pursued, and it provides guidance for future device engineering and materials development.
Quantum error correction works effectively only if the error rate of gate operations is sufficiently low. However, some rare physical mechanisms can cause a temporary increase in the error rate that affects many qubits; examples include ionizing radiation in superconducting hardware and large deviations in the global control of atomic systems. We refer to such rare transient spikes in the gate error rate as error bursts. In this work, we investigate the resilience of the rotated surface code to generic error bursts. We assume that, after appropriate mitigation strategies, the spike in the error rate lasts for only a single syndrome-extraction cycle; we also assume that the enhanced error rate is uniform across the code block. Under these assumptions, and for a circuit-level depolarizing noise model, we perform Monte Carlo simulations to determine the regime in burst error rate and background error rate for which the memory time becomes arbitrarily long as the code block size grows. Our results indicate that suitable hardware mitigation methods combined with standard decoding methods may suffice to protect against transient error bursts in the rotated surface code.
Specialized computational chemistry packages have permanently reshaped the landscape of chemical sciences by providing tools to support and guide the experimental effort and for prediction of chemical and materials properties. In this regard, a special role has been played by electronic structure packages where complex chemical and materials processes can be modeled using first-principle-driven methodologies. Over the last few decades, the rapid development of computing technologies and a tremendous increase in computational power has offered a unique chance to study complex chemical transformations using sophisticated and predictive many-body techniques to describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, a critical role has been played by novel parallel algorithms capable of taking advantage of computational resources to address polynomial scaling of electronic structure methods. NWChem was among the first electronic structure codes that focused on delivering scalable performance for electronic structure simulations. Herein, we briefly review the NWChem suite of computational codes including its history, design principles, parallel tools, current capabilities, outreach and outlook.
To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.
Fast, reliable logical operations are essential for realizing useful quantum computers. By redundantly encoding logical qubits into many physical qubits and using syndrome measurements to detect and correct errors, we can achieve low logical error rates. However, for many practical quantum error correction codes such as the surface code, owing to syndrome measurement errors, standard constructions require multiple extraction rounds—of the order of the code distance d—for fault-tolerant computation, particularly considering fault-tolerant state preparation. Here we show that logical operations can be performed fault-tolerantly with only a constant number of extraction rounds for a broad class of quantum error correction codes, including the surface code with magic state inputs and feedforward, to achieve ‘transversal algorithmic fault tolerance’. Through the combination of transversal operations7 and new strategies for correlated decoding, despite only having access to partial syndrome information, we prove that the deviation from the ideal logical measurement distribution can be made exponentially small in the distance, even if the instantaneous quantum state cannot be made close to a logical codeword because of measurement errors. We supplement this proof with circuit-level simulations in a range of relevant settings, demonstrating the fault tolerance and competitive performance of our approach. Furthermore, our work sheds new light on the theory of quantum fault tolerance and has the potential to reduce the space–time cost of practical fault-tolerant quantum computation by over an order of magnitude.
CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.
The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
Quantum Error Correction (QEC) is essential for fault-tolerant, large-scale quantum computation. However, error drift in qubits undermines QEC performance during long computations, necessitating frequent calibration. Conventional calibration methods disrupt quantum states, requiring system downtime and rendering in situ calibration impractical. To address this challenge, we propose QECali, a novel framework that enables in situ calibration for surface codes. Our evaluation demonstrates that QECali introduces modest qubit overhead and negligible increases in execution time, offering the first practical solution for in situ calibration in surface code based quantum computation.