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DOE OSTI2020

Leaf-scale carbon assimilation simulated by the multi-assumption architecture & testbed for process sensitivity analysis

Raw and processed output from the Multi-Assumption Architecture & Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_walker2020; Walker et al., 2018). The dataset contains leaf-scale carbon assimilation data from three MAAT simulations and their post-processing and plotting scripts. Simulation 1, labelled "FvC_4proc_proc," is a four-process, process sensitivity analysis (Dai et al., 2017) of carbon assimilation based on the Farquhar et al. (1980) photosynthesis model, and its variant proposed by Collatz et al. (1991). Simulation 2, labelled "FvC_4proc_salt," is a 14 parameter sensitivity analysis (Saltelli et al., 2010) of carbon assimilation based on the Farquhar and Collatz photosynthesis models. Simulation 3, labelled "FvC," is a factorial combination of the alternative process representations and parameter values presented in those original papers across a factorial combination of incident photosynthetically active radiation (50-2000 µmol m-2 s-1, in increments of 50) and atmospheric CO2 concentration (50-1500 µmol m-2 s-1, in increments of 50). Additional scripts/functions for post-processing and plotting are part of the MAAT repository. These data were generated to evaluate and compare the Farquhar and Collatz models of leaf-scale photosynthesis across a range of CO2 and incident light conditions. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures; .RDS files, a binary format specific to R and read with the 'readRDS' function, to store raw MAAT output; R and bash scripts (.bs) that help to run a MAAT simulation, .XML files that are output from MAAT describing a run, and .csv files which contain the calculated sensitivity indexes published in Walker et al. (2020).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

DESPERATE: A Python Library for Processing and Denoising NMR Spectra

NMR spectroscopy is an inherently insensitive technique with respect to the amount of observable signal. A common element in all NMR spectra is random thermal noise that is often characterized by a signal-to-noise ratio (SNR). SNR can be generically improved experimentally with repetitive signal averaging or during post-processing with apodization; the former of which often results in long experimental times and the latter results in the loss of spectral resolution. Denoising techniques can instead be used during post-processing to enhance SNR without compromising resolution. The most common approach relies on the singular-value decomposition (SVD) to discard noisy components of NMR data. SVD-based approaches work well, such as Cadzow and PCA, but are computationally expensive when used for large datasets that are often encountered in NMR (e.g., Carr-Purcell/Meiboom-Gill and nD datasets). Herein, we describe the implementation of a new wavelet transform (WT) routine for the fast and robust denoising of 1D and 2D NMR spectra. Several simulated and experimental datasets are denoised with both SVD-based Cadzow or PCA and WT’s, and the resulting SNR enhancements and spectral uniformity are compared. WT denoising offers similar and improved denoising compared with SVD and operates faster by several orders-of-magnitude in some cases. Further, all denoising and processing routines used in this work are included in a free and open-source Python library called DESPERATE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Programmable Quantum Networked Microgrids

Quantum key distribution (QKD) provides a potent solution to securely distribute keys for two parties. However, QKD itself is vulnerable to denial of service (DoS) attacks. A flexible and resilient QKD-enabled networked microgrids (NMs) architecture is needed but does not yet exist. In this article, we present a programmable quantum NMs (PQNMs) architecture. It is a novel framework that integrates both QKD and software-defined networking (SDN) techniques capable of enabling scalable, programmable, quantum-engineered, and ultra-resilient NMs. Equipped with a software-defined adaptive post-processing approach, a two-level key pool sharing strategy and an SDN-enabled event-triggered communication scheme, these PQNMs mitigate the impact of DoS attacks through programmable post-processing and secure key sharing among QKD links, a capability unattainable using existing technologies. Through comprehensive evaluations, we validate the benefits of PQNMs and demonstrate the efficacy of the presented strategies under various circumstances. Extensive results provide insightful resources for building QKD-enabled NMs in practice.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Fabrication of Conductive Paths on a Fused Deposition Modeling Substrate using Inkjet Deposition

Inkjet deposition is one of the most attractive fabrication techniques for producing cost efficient and lightweight electronic devices on various substrates with low environmental impact. Fused Deposition Modeling (FDM) is one of the most used and reliable additive manufacturing processes by extrusion of wire-shaped thermoplastic materials, which provides an opportunity for embedding printed electronics into mechanical structures during the building process and enables the design of compact smart structures that can sense and adapt to their own state and the environment. This paper represents one of the first explorations of integrating inkjet deposition of silver nanoparticle inks with the FDM process for making compact electro-mechanical structures. Three challenges have been identified and investigated, including the discontinuity of the printed lines resulting from the irregular surface of the FDM substrate, the non-conductivity of the printed lines due to the particle segregation during the droplet drying process, and the slow drying process caused by the skinning effect . Two different techniques are developed in this paper to address the issue of continuity of the printed lines, including surface ironing and a novel thermal plow technique that plows a channel in the FDM substrate to seal off the pores in the substrate and contain the deposited inks. Two solutions are also found for obtaining conductivity from the continuous printed lines, including porous surface coating and using a more viscous ink with larger nanoparticle size. Then the effects of the printing and post-processing parameters on the conductivity are examined. It is found that post-processing is a dominant factor in determining the conductivity of the printed lines.

42 ENGINEERING↗

DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗

DOE OSTI2023

Preserving Superconvergence of Spectral Elements for Curved Domains via $h$ and $p$-Geometric Refinement

Spectral element methods (SEM), extensions of finite element methods (FEM), have emerged as significant techniques for solving partial differential equations in physics and engineering. SEM can potentially deliver superior accuracy due to the potential superconvergence in nodal solutions for well-shaped tensor-product elements. However, the accuracy of SEM often degrades in complex geometries due to geometric inaccuracies near curved boundaries and the loss of superconvergence with simplicial or non-tensor-product elements. To overcome the first issue, we propose using h- and p-geometric refinement, which refines the mesh near high-curvature regions and increases the degree of geometric basis functions, respectively. We show that when using mixed-element meshes with tensor-product elements in the interior of the domain, curvature-based geometric refinement near boundaries can improve the accuracy of the interior elements by reducing pollution errors and preserving the superconvergence in nodal solutions. To address the second issue, we introduce a post-processing technique using the adaptive extended stencil finite element method (AES-FEM) to recover the accuracy near the curved boundaries. The combination of curvature-based geometric refinement and accurate post-processing offers an effective and easier-to-implement alternative to methods reliant on exact geometries. We demonstrate our techniques by solving the convection-diffusion equation in 2D and show up to two orders of magnitude of improvement in the solution accuracy, even when the elements are poorly shaped near boundaries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Preserving Superconvergence of Spectral Elements for Curved Domains

Spectral element methods (SEM), extensions of finite element methods (FEM), have emerged as significant techniques for solving partial differential equations in physics and engineering. SEM can potentially deliver superior accuracy due to the potential superconvergence in nodal solutions for well-shaped tensor-product elements. However, the accuracy of SEM often degrades in complex geometries due to geometric inaccuracies near curved boundaries and the loss of superconvergence with simplicial or non-tensor-product elements. To overcome the first issue, we propose using geometric refinement, which both refines the mesh near high-curvature regions and increases the degree of geometric basis functions. We show that when using mixed-element meshes with tensor-product elements in the interior of the domain, curvature-based geometric refinement near boundaries can improve the accuracy of the interior elements by reducing pollution errors and preserving the superconvergence in nodal solutions. To address the second issue, we introduce ApSEM, a post-processing technique using the adaptive extended stencil finite element method (AES-FEM) to recover the accuracy near the curved boundaries. The combination of curvature-based geometric refinement and accurate post-processing offers an effective and easier-to-implement alternative to methods reliant on exact geometries. We demonstrate our techniques by solving the convection-diffusion equation in 2D and 3D and show up to two orders of magnitude of improvement in the solution accuracy, even when the elements are poorly shaped near boundaries. We also show the efficiency of ApSEM as it can recover superconvergence in nodal solutions without drastically increasing the computational cost.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Pick and Place Robotic Actuator for Big Area Additive Manufacturing

Oak Ridge National Laboratory’s Manufacturing Demonstration Facility has created a system that works in tandem with an existing large-scale additive manufacturing (AM) system to ‘pick and place’ custom components into a part as it is printed. Large-scale AM leaves a layered surface finish and is typically post-processed through 5-axis CNC machining. Each surface must be accurately recorded into a laser tracking system. This process can be simplified with the use of fiducials, which are small location indicators placed on the surface of a part. Additionally, the ability to monitor an AM tool via wireless sensors is advantageous to gauge part health as it is fabricated and later used. The ‘pick and place’ system allows thermocouples, fiducials, and other sensors to be accurately placed throughout the tool as it is fabricated. This solution has the potential to reduce time, labor, and cost associated with fabricating, post-processing, and using AM parts.

Boulger, Alex↗

DOE OSTI2023

Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography

We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus $super$-$resolution$). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography

We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus ‘super-resolution’). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

High‐Loaded Electrode Filaments for Additive Manufacturing of Structural Batteries

Abstract The unique capability of 3D printing to create geometric complex structures presents a promising avenue for producing 3D electrodes aimed at enhancing power and energy densities within constrained spaces, which is challenging to achieve through traditional slurry casting methods. However, despite advancements over the years, 3D‐printed batteries have faced limitations in terms of mechanical robustness to endure significant volume changes during cycling and restricted electrochemical performance due to the lack of adequate electrode feedstock development or post‐processing treatments. Herein, a high‐loaded electrode filament with ≈65 wt.% fillers, enabling the fabrication of structural electrodes with improved electrochemical performance and superior mechanical properties, is developed. Through a combination of 3D printing and post‐processing techniques, interdigitated structures with high areal‐loading density, resulting in a full cell with an enhanced areal capacity of ≈12.28 mAh cm −2 at ≈0.92 mA cm −2 is fabricated. Moreover, the structural batteries, treated through the carbonizing process, are integrated by the carbon coating generated during carbonization, exhibiting remarkable compressive properties (with a modulus of 18.5 MPa and a strength of 1.09 MPa). Overall, the findings demonstrate the promising potential of 3D printed batteries for practical applications, while showcasing the scalability and design flexibility offered by 3D printing technology.

Chemistry↗

DOE OSTI2021

Fabrication of bulk alumina structures with humidity sensing capabilities using direct ink write technique

Purpose The purpose of this study is to evaluate different 3D structures for humidity sensing that will enable the fabrication of complex geometries with high moisture sensitivity. Design/methodology/approach Humidity sensors based on alumina ceramics were fabricated using direct ink write (DIW) technique. Different engineered surface area, polymer binder ratio and post-processing treatment were considered to increase moisture sensitivity. Findings It was found that the binder ratio plays an important role in controlling the rheology of the paste during printing and determining the pore size after post-processing treatment. The sensibility of the fabricated humidity sensor was investigated by measuring its capacitance response toward relative humidity (RH) varying from 40% to 90% RH at 25°C. It is shown that using 3D lattice design, printed alumina humidity sensor could improve sensitivity up to 31.6 pF/RH%, over an order of magnitude higher than solid alumina. Originality/value Most of the alumina humidity sensors available are films in nature because of manufacturing difficulties, which limited its potential of higher sensitivity, and thus broader applications. In this paper, a novel 3D alumina humidity sensor was fabricated using DIW 3D printing technology.

Engineering↗

DOE OSTI2016

Analysis-Software for Hyperspectral Algal Reflectance Probes v. 1.0

This software provides onsite analysis of the hyperspectral reflectance data acquired on an outdoor algal pond by a multichannel, fiber-coupled spectroradiometer. The analysis algorithm is based on numerical inversion of a reflectance model, in which the above-water reflectance is expressed as a function of the single backscattering albedo, which is dependent on the backscatter and absorption coefficients of the algal culture, which are in turn related to the algal biomass and pigment optical activity, respectively. Prior to the development of this software, while raw multichannel data were displayed in real time, analysis required a post-processing procedure to extract the relevant parameters. This software provides the capability to track the temporal variation of such culture parameters in real time, as raw data are being acquired, or can be run in a post processing mode. The software allows the user to select between different algal species, incorporate the appropriate calibration data, and observe the quality of the resulting model inversions.

Timlin, Jerilyn A.↗

DOE OSTI2016

SU-F-I-16: Short Breast MRI with High-Resolution T2-Weighted and Dynamic Contrast Enhanced T1-Weighted Images

Purpose: To develop and demonstrate a short breast (sb) MRI protocol that acquires both T2-weighted and dynamic contrast-enhanced T1-weighted images in approximately ten minutes. Methods: The sb-MRI protocol consists of two novel pulse sequences. The first is a flexible fast spin-echo triple-echo Dixon (FTED) sequence for high-resolution fat-suppressed T2-weighted imaging, and the second is a 3D fast dual-echo spoiled gradient sequence (FLEX) for volumetric fat-suppressed T1-weighted imaging before and post contrast agent injection. The flexible FTED sequence replaces each single readout during every echo-spacing period of FSE with three fast-switching bipolar readouts to produce three raw images in a single acquisition. These three raw images are then post-processed using a Dixon algorithm to generate separate water-only and fat-only images. The FLEX sequence acquires two echoes using dual-echo readout after each RF excitation and the corresponding images are post-processed using a similar Dixon algorithm to yield water-only and fat-only images. The sb-MRI protocol was implemented on a 3T MRI scanner and used for patients who had undergone concurrent clinical MRI for breast cancer screening. Results: With the same scan parameters (eg, spatial coverage, field of view, spatial and temporal resolution) as the clinical protocol, the total scan-time of the sb-MRI protocol (including the localizer, bilateral T2-weighted, and dynamic contrast-enhanced T1-weighted images) was 11 minutes. In comparison, the clinical breast MRI protocol took 43 minutes. Uniform fat suppression and high image quality were consistently achieved by sb-MRI. Conclusion: We demonstrated a sb-MRI protocol comprising both T2-weighted and dynamic contrast-enhanced T1-weighted images can be performed in approximately ten minutes. The spatial and temporal resolution of the images easily satisfies the current breast MRI accreditation guidelines by the American College of Radiology. The protocol has the potential of making breast MRI more widely accessible to and more tolerable by the patients. JMA is the inventor of United States patents that are owned by the University of Texas Board of Regents and currently licensed to GE Healthcare and Siemens Gmbh.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗

DOE OSTI2022

Additive manufacturing of LiNi 1/3 Mn 1/3 Co 1/3 O 2 battery electrode material via vat photopolymerization precursor approach

Additive manufacturing, also called 3D printing, has the potential to enable the development of flexible, wearable and customizable batteries of any shape, maximizing energy storage while also reducing dead-weight and volume. In this work, for the first time, three-dimensional complex electrode structures of high-energy density LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC 111) material are developed by means of a vat photopolymerization (VPP) process combined with an innovative precursor approach. This innovative approach involves the solubilization of metal precursor salts into a UV-photopolymerizable resin, so that detrimental light scattering and increased viscosity are minimized, followed by the in-situ synthesis of NMC 111 during thermal post-processing of the printed item. The absence of solid particles within the initial resin allows the production of smaller printed features that are crucial for 3D battery design. The formulation of the UV-photopolymerizable composite resin and 3D printing of complex structures, followed by an optimization of the thermal post-processing yielding NMC 111 is thoroughly described in this study. Based on these results, this work addresses one of the key aspects for 3D printed batteries via a precursor approach: the need for a compromise between electrochemical and mechanical performance in order to obtain fully functional 3D printed electrodes. In addition, it discusses the gaps that limit the multi-material 3D printing of batteries via the VPP process.

25 ENERGY STORAGE↗

DOE OSTI2018

Contamination control and assay results for the Majorana Demonstrator ultra clean components

The Majorana Demonstrator is a neutrinoless double beta decay experiment utilizing enriched Ge-76 detectors in 2 separate modules inside of a common solid shield at the Sanford Underground Research Facility. The Demonstrator has utilized world leading assay sensitivities to develop clean materials and processes for producing ultra-pure copper and plastic components. This experiment is now operating, and initial data provide new insights into the success of cleaning and processing. Post production copper assays after the completion of Module 1 showed an increase in U and Th contamination in finished parts compared to starting bulk material. A revised cleaning method and additional round of surface contamination studies prior to Module 2 construction have provided evidence that more rigorous process control can reduce surface contamination. In conclusion, this article describes the assay results and discuss further studies to take advantage of assay capabilities for the purpose of maintaining ultra clean fabrication and process design.

36 MATERIALS SCIENCE↗