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NASA NTRS2014

Micro-spec: an Integrated Direct-detection Spectrometer for Far-infrared Space Telescopes

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements.Micro-Spec (μ-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 (micrometers) wavelength range which will enable a wide range of flight missions that would otherwise be challenging due tothe large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 sq cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for micro-Spec is presented, with particular attention given to its two-dimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance.Two point designs with resolving power of 260 and 520 and an RMS phase error less than approximately 0.004 radians were developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

Far-infrared↗

DOE OSTI2025

Comparing Advanced Bipolar Membranes for High-Current Electrodialysis and Membrane Electrolysis

Advanced bipolar membranes (BPMs) with low water-dissociation overpotential (ηwd) may enable new electrochemical technologies for electrolysis, fuel cells, acid–base synthesis, brine remediation, lithium-battery recycling, and cement production. However, these advanced BPMs have only been demonstrated in BPM water electrolysis (BPMWE) configurations where the BPM is under static compression by the porous-transport layers. It is important to study these BPMs in applications like electrodialysis where large degrees of static compression are not possible. We present a BPM electrodialysis (BPMED) platform to measure water-dissociation overpotential (ηwd) and compare BPMWE and BPMED systems. We show advanced BPMs with half the ηwd compared to commercial BPMs for BPMED while maintaining ∼90% current efficiency from 0.05–0.5 A cm–2. The BPMED ηwd values are, however, about 0.2 V higher at 0.5 A cm–2 than those for BPMWE. Regardless, these results show that BPMs developed and optimized in BPMWE applications are well-suited for next-generation high-current-density BPMED technologies.

Vulpin, Olivia T↗

DOE OSTI2022

SPEL: Software tool for Porting E3SM Land Model with OpenACC in a Function Unit Test Framework

Most high-end computers adopt hybrid architecture, porting a large-scale scientific code onto accelerators is necessary. The paper presents a generic method for porting large-scale scientific code onto accelerators using compiler directives within a modularized function unit test platform. We have implemented the method and designed a software tool (SPEL) to port the E3SM Land Model (ELM) onto the GPUs in the Summit computer. SPEL automatically generates GPU-ready test modules for all ELM functions, such as CanopyFlux, SoilTemperature, and EcosystemDynamics. SPEL breaks the ELM into a collection of standalone unit test programs for easy code verification and further performance improvement. We further optimize several ELM test modules with advanced techniques, including memory reduction, reconstructed parallel loops, and asynchronous GPU kernel launch. We hope our study will inspire new toolkit developments that expedite large-scale scientific code porting with compiler directives.

Schwartz, Peter↗

DOE OSTI2016

Systematic study of target localization for bioluminescence tomography guided radiation therapy

Purpose: To overcome the limitation of CT/cone-beam CT (CBCT) in guiding radiation for soft tissue targets, the authors developed a spectrally resolved bioluminescence tomography (BLT) system for the small animal radiation research platform. The authors systematically assessed the performance of the BLT system in terms of target localization and the ability to resolve two neighboring sources in simulations, tissue-mimicking phantom, and in vivo environments. Methods: Multispectral measurements acquired in a single projection were used for the BLT reconstruction. The incomplete variables truncated conjugate gradient algorithm with an iterative permissible region shrinking strategy was employed as the optimization scheme to reconstruct source distributions. Simulation studies were conducted for single spherical sources with sizes from 0.5 to 3 mm radius at depth of 3–12 mm. The same configuration was also applied for the double source simulation with source separations varying from 3 to 9 mm. Experiments were performed in a standalone BLT/CBCT system. Two self-illuminated sources with 3 and 4.7 mm separations placed inside a tissue-mimicking phantom were chosen as the test cases. Live mice implanted with single-source at 6 and 9 mm depth, two sources at 3 and 5 mm separation at depth of 5 mm, or three sources in the abdomen were also used to illustrate the localization capability of the BLT system for multiple targets in vivo. Results: For simulation study, approximate 1 mm accuracy can be achieved at localizing center of mass (CoM) for single-source and grouped CoM for double source cases. For the case of 1.5 mm radius source, a common tumor size used in preclinical study, their simulation shows that for all the source separations considered, except for the 3 mm separation at 9 and 12 mm depth, the two neighboring sources can be resolved at depths from 3 to 12 mm. Phantom experiments illustrated that 2D bioluminescence imaging failed to distinguish two sources, but BLT can provide 3D source localization with approximately 1 mm accuracy. The in vivo results are encouraging that 1 and 1.7 mm accuracy can be attained for the single-source case at 6 and 9 mm depth, respectively. For the 2 sources in vivo study, both sources can be distinguished at 3 and 5 mm separations, and approximately 1 mm localization accuracy can also be achieved. Conclusions: This study demonstrated that their multispectral BLT/CBCT system could be potentially applied to localize and resolve multiple sources at wide range of source sizes, depths, and separations. The average accuracy of localizing CoM for single-source and grouped CoM for double sources is approximately 1 mm except deep-seated target. The information provided in this study can be instructive to devise treatment margins for BLT-guided irradiation. These results also suggest that the 3D BLT system could guide radiation for the situation with multiple targets, such as metastatic tumor models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Four-dimensional dose reconstruction through in vivo phase matching of cine images of electronic portal imaging device

Purpose: A method is proposed to reconstruct a four-dimensional (4D) dose distribution using phase matching of measured cine images to precalculated images of electronic portal imaging device (EPID). Methods: (1) A phantom, designed to simulate a tumor in lung (a polystyrene block with a 3 cm diameter embedded in cork), was placed on a sinusoidally moving platform with an amplitude of 1 cm and a period of 4 s. Ten-phase 4D computed tomography (CT) images of the phantom were acquired. A planning target volume (PTV) was created by adding a margin of 1 cm around the internal target volume of the tumor. (2) Three beams were designed, which included a static beam, a theoretical dynamic beam, and a planning-optimized dynamic beam (PODB). While the theoretical beam was made by manually programming a simplistic sliding leaf motion, the planning-optimized beam was obtained from treatment planning. From the three beams, three-dimensional (3D) doses on the phantom were calculated; 4D dose was calculated by means of the ten phase images (integrated over phases afterward); serving as “reference” images, phase-specific EPID dose images under the lung phantom were also calculated for each of the ten phases. (3) Cine EPID images were acquired while the beams were irradiated to the moving phantom. (4) Each cine image was phase-matched to a phase-specific CT image at which common irradiation occurred by intercomparing the cine image with the reference images. (5) Each cine image was used to reconstruct dose in the phase-matched CT image, and the reconstructed doses were summed over all phases. (6) The summation was compared with forwardly calculated 4D and 3D dose distributions. Accounting for realistic situations, intratreatment breathing irregularity was simulated by assuming an amplitude of 0.5 cm for the phantom during a portion of breathing trace in which the phase matching could not be performed. Intertreatment breathing irregularity between the time of treatment and the time of planning CT was considered by utilizing the same reduced amplitude when the phantom was irradiated. To examine the phase matching in a humanoid environment, the matching was also performed in a digital phantom (4D XCAT phantom). Results: For the static, the theoretical, and the planning-optimized dynamic beams, the 4D reconstructed doses showed agreement with the forwardly calculated 4D doses within the gamma pass rates of 92.7%, 100%, and 98.1%, respectively, at the isocenter plane given by 3%/3 mm criteria. Excellent agreement in dose volume histogram of PTV and lung-PTV was also found between the two 4D doses, while substantial differences were found between the 3D and the 4D doses. The significant breathing irregularities modeled in this study were found not to be noticeably affecting the reconstructed dose. The phase matching was performed equally well in a digital phantom. Conclusions: The method of retrospective phase determination of a moving object under irradiation provided successful 4D dose reconstruction. This method will provide accurate quality assurance and facilitate adaptive therapy when distinguishable objects such as well-defined tumors, diaphragm, and organs with markers (pancreas and liver) are covered by treatment beam apertures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

A perspective on Bayesian methods applied to materials discovery and design

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process-structure-property-performance (PSPP) chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian Optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. Here, the ultimate goal is to pave the way to autonomous materials discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗

NASA NTRS2013

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2 percent correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

satellite remote sensing↗

DOE OSTI2022

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Retrobiosynthesis of unnatural lactams via reprogrammed polyketide synthase

Engineered polyketide synthases (PKSs) have great potential as biocatalysts. These unnatural enzymes are capable of synthesizing molecules that are either not amenable to biosynthesis or are extremely challenging to access chemically. PKSs can thus be a powerful platform to expand the chemical landscape beyond the limits of conventional metabolic engineering. Here we employ a retrobiosynthesis approach to design and construct PKSs to produce δ-valerolactam (VL) and three enantiopure α-substituted VL analogues that have no known biosynthetic route. We introduce the engineered PKSs and pathways for various malonyl-CoA derivatives into Pseudomonas putida and use proteomics, metabolomics and culture condition optimization to improve the production of our target compounds. These α-substituted VLs are polymerized into polyamides (nylon-5) or converted into their N-acryloyl derivatives. RAFT polymerization produces bio-derived polymers with potential biomedical applications. Overall, this interdisciplinary effort highlights the versatility and effectiveness of a PKS-based retrobiosynthesis approach in exploring and developing innovative biomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Light detection with power and signal transmission over fiber

The Deep Underground Neutrino Experiment is a next generation long baseline (1300 km) neutrino oscillation experiment. The neutrino beam measurements will be performed by a near detector and far detector. The far detector will consist of four modules, installed 1.5 km deep underground, based on Liquid Argon Time Projection Chamber (LArTPC) technology to detect particles. The Vertical Drift (VD) LArTPC is a recent technology proposed by the DUNE collaboration for the second FD module. In VD, light collection will be optimized by embedding photon detectors within the LArTPC cathode, which is biased at −300 kV. As a result, power must arrive to the Photon Detection System and signal must be transmitted via non-conductive material. The proposed solution is to use Power-over-Fiber and Signal-over-Fiber. An intense validation of the system is being performed by the collaboration at the CERN Neutrino Platform. The design of the system and the results of the validation collected over the first half of 2022 are presented here.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Compact fiber-coupled narrowband two-mode squeezed light source

Quantum correlated states of light, such as squeezed states, are a fundamental resource for the development of quantum technologies, as they are needed for applications in quantum metrology, quantum computation, and quantum communications. It is thus critical to develop compact, efficient, and robust sources to generate such states. Here, we report on a compact, narrowband, fiber-coupled source of two-mode squeezed states of light at 795 nm based on four-wave mixing (FWM) in an 85 Rb atomic vapor. The source is designed in a small modular form factor, with two input fiber-coupled beams, the seed and pump beams required for the FWM, and two output fibers, one for each of the modes of the squeezed state. The system is optimized for low pump power (135 mW) to achieve a maximum intensity-difference squeezing of 4.4 dB after the output of fibers at an analysis frequency of 1 MHz. Furthermore, the narrowband nature of the source makes it ideal for atomic-based quantum sensing and quantum networking configurations that rely on atomic quantum memories. Such a source paves the way for a versatile and portable platform for applications in quantum information science.

Jain, Umang [University of Oklahoma, Norman, OK (U↗

DOE OSTI2017

Computing Properties of Hadrons, Nuclei and Nuclear Matter from Quantum Chromodynamics

This project was part of a coordinated software development effort which the nuclear physics lattice QCD community pursues in order to ensure that lattice calculations can make optimal use of present, and forthcoming leadership-class and dedicated hardware, including those of the national laboratories, and prepares for the exploitation of future computational resources in the exascale era. The UW team improved and extended software libraries used in lattice QCD calculations related to multi-nucleon systems, enhanced production running codes related to load balancing multi-nucleon production on large-scale computing platforms, and developed SQLite (addressable database) interfaces to efficiently archive and analyze multi-nucleon data and developed a Mathematica interface for the SQLite databases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

HPC-enabled computation of demand models at scale

The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

HPC4Mobilty w/ UCB

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1983

Theoretical performance of a vortex generator type of cloud droplet sampler

One of the goals of the NASA global tropospheric research program is understanding the chemical processes which influence the chemistry of the natural troposphere. An important element of this program is the study of the role of heterogenous chemistry in global tropospheric chemical cycles. Any study of the part which clouds play in the chemistry of tropospheric trace constituents requires a determination of cloud water chemical composition. There exists, therefore, a need for highly efficient collector devices which can obtain unbiased samples of cloud water in sufficient quantities aboard an aircraft platform. A promising concept which could meet the dual requirements of accurate sampling and efficient collection of cloud water droplets in the vortex generator. This concept involves an inertial separator which uses the centrifugal force of a rotating airstream. The separator is built into a commercial device which has been used for many years as a dust separator for air breathing engines. Attention is given to the optimization of the sampler design.

Miller, I. M.↗

NASA NTRS2018

The Dawn of Geostationary Air Quality Monitoring: Case Studies from Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9, 2016 and Los Angeles, California on June 27, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.

Geostationary↗

DOE OSTI2024

A Novel 'Smart Microchip Proppants' Technology for Precision Diagnostics of Hydraulic Fracture Networks (Edited Final Report)

This project introduces innovative technology to improve subsurface characterization, visualization, and diagnostics of unconventional reservoirs (fossil resources). Through a collaborative effort involving the University of Kansas, UCLA, MicroSilicon Inc., and EOG Resources, the project aims to deliver precision diagnostics for hydraulic fractures using novel high-resolution imaging technology based on smart microchip proppants. Additionally, it seeks to enhance the accuracy and predictability of integrated numerical, and machine-learning modeling techniques for hydraulic fracture characterization and simulation. This groundbreaking technology addresses significant gaps in understanding unconventional and tight reservoir behavior and optimizing well-completion strategies, enabling more cost-efficient recovery of unconventional resources.

02 PETROLEUM↗