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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sustainable production of 5-hydroxymethyl furfural from glucose for process integration with high fructose corn syrup infrastructure

5-Hydroxymethyl furfural (HMF) is a platform chemical, which can be derived from lignocellulosic biomass, and used for production of liquid fuels and polymers. We demonstrate a process for production of HMF using sequential enzymatic and catalytic reactions of glucose to synthesize HMF, and simulated-moving-bed (SMB) separation to purify HMF. The adsorption thermodynamic parameters of glucose, fructose, and HMF on a commercial chromatography resin are experimentally determined for modeling the SMB-based HMF production process. The experimental data are used to develop a rigorous process model and then estimate the cost of production. Chromatographic separation of HMF has 16% lower operating costs compared to an extraction-based process and has a minimum selling price of approximately $1478 per ton. We demonstrate that the HMF process can be integrated with the high fructose corn syrup (HFCS) process, and we performed analyses considering two systems including construction of a new integrated facility and retrofitting an existing HFCS facility to produce HMF. Our analyses suggest that the latter approach is a promising short-term low-risk strategy to advance the HMF production technology to commercial scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Signal formation and sharing in AC-LGADs using the ALTIROC 0 front-end chip

The development of detectors that provide high resolution in four dimensions has attracted wide-spread interest in the scientific community for applications in high-energy physics, nuclear physics, medical imaging, mass spectroscopy as well as quantum information. However, finding a technology capable of fulfilling such aspiration proved to be an arduous task. Among other silicon-based candidates, the Low-Gain Avalanche Diode (LGAD) has already shown excellent timing performances but proved to be unsuitable for fine pixelization. Therefore, the AC-coupled LGAD (AC-LGAD) approach was introduced to provide high resolution in both time and space, making it a promising candidate for future 4D detectors. However, appropriate readout electronics must be developed to match the sensor's fast-time and fine-pitch capabilities. This is currently a major technological challenge. Here, we test AC-LGAD prototypes read out by the fast-time ASIC ALTIROC 0, originally developed for the readout of DC-coupled LGADs for the ATLAS experiment at the High Luminosity-LHC. Signal generated by either betas from a 90 Sr source or a focused infra-red laser were analyzed. This paper details the first successful readout of an AC-LGAD sensor using a readout chip. This result will pave the way for the design and construction of a new generation of AC-LGAD-based 4D detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Setups for eliminating static charge of the ATLAS18 strip sensors

Abstract Construction of the new all-silicon Inner Tracker (ITk), developed by the ATLAS collaboration to beable to track charged particles produced at the High-Luminosity LHC, started in 2020 and is expected tocontinue till 2028. The ITk detector will include 18,000 highly segmented and radiation hard n+-in-psilicon strip sensors (ATLAS18), which are being manufactured by Hamamatsu Photonics. Mechanical and electrical characteristics of produced sensors are measured upon their delivery at several institutes participating in a complex Quality Control (QC) program. The QC tests performed on each individual sensor check the overall integrity and quality of the sensor.During the QC testing of ATLAS18 strip sensors, an increased number of sensors thatfailed the electrical tests was observed. In particular, IV measurements indicated an early breakdown,while large areas containing several tens or hundreds of neighbouring strips with low interstrip isolationwere identified by the Full strip tests, and leakage current instabilities were measured in a long-term leakage current stability setup. Moreover, a high surface electrostatic charge reaching a level of several hundreds of volts per inch was measured on a large number of sensors and on the plastic sheets, whichmechanically protect these sensors in their paper envelopes. Accumulated data indicates a clearcorrelation between observed electrical failures and the sensor charge-up.To mitigate the above-described issues, the QC testing sites significantly modified the sensor handlingprocedures and introduced sensor recovery techniques based on irradiation of the sensor surface with UVlight or application of intensive flows of ionized gas. In this presentation, we will describe the setupsimplemented by the QC testing sites to treat silicon strip sensors affected by static charge andevaluate the effectiveness of these setups in terms of improvement of the sensor performance.

Instruments & Instrumentation↗
DOE OSTI2020

A novel CMB component separation method: hierarchical generalized morphological component analysis

ABSTRACT We present a novel technique for cosmic microwave background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to generalized morphological component analysis (GMCA), we introduce hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on Nside = 256 simulated sky maps that include dust, synchrotron, free–free, and anomalous microwave emission, and show that HGMCA reduces foreground contamination by $25{{\ \rm per\ cent}}$ over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the 0.02–0.03 per cent level at ℓ > 200 (and $\lt 0.26{{\ \rm per\ cent}}$ for all ℓ), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art internal linear combination type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Nuclear spin metrology with nitrogen vacancy center in diamond for axion dark matter detection

We present a method to directly detect the axion dark matter using nitrogen vacancy centers in diamonds. In particular, we use metrology leveraging the nuclear spin of nitrogen to detect axion-nucleus couplings. This is achieved through protocols designed for dark matter searches, which introduce a novel approach of quantum sensing techniques based on the nitrogen vacancy center. Although the coupling strength of the magnetic fields with nuclear spins is three orders of magnitude smaller than that with electron spins for conventional magnetometry, the axion interaction strength with nuclear spins is the same order of magnitude as that with electron spins. Furthermore, we can take advantage of the long coherence time by using the nuclear spins for the axion dark matter detection. Our method has the potential to be sensitive to a broad frequency range ≲ 100 Hz corresponding to the axion mass m a ≲ 4 × 10 − 13 eV . We present the detection limit of our method for both the axion-neutron and the axion-proton couplings and discuss its significance in comparison with other proposed ideas. We also show that the sensitivities of the NV center sensor to various spin species will open up new directions for constructing protocols that can mitigate magnetic noise effects. Published by the American Physical Society 2025

Chigusa, So (ORCID:0000000160054447)↗
DOE OSTI2024

Engineering and revealing Dirac strings in spinor condensates

Artificial monopoles have been engineered in various systems, yet there has been no systematic study of the singular vector potentials associated with the monopole field. We show that the Dirac string, the line singularity of the vector potential, can be engineered, manipulated, and made manifest in a spinor atomic condensate. We elucidate the connection among spin, orbital degrees of freedom, and the artificial gauge, and show that there exists a mapping between the vortex filament and the Dirac string. We also devise a proposal where preparing initial spin states with relevant symmetries can result in different vortex patterns, revealing an underlying correspondence between the internal spin states and the spherical vortex structures. Such a mapping also leads to a new way of constructing spherical Landau levels, and monopole harmonics. Our observation provides insights into the behavior of quantum matter possessing internal symmetries in curved spaces. Published by the American Physical Society 2024

Xu, Gui-Sheng↗
DOE OSTI2021

Design and Beam Test Results for the 2-D Projective sPHENIX Electromagnetic Calorimeter Prototype

sPHENIX is a new experiment under construction for the Relativistic Heavy Ion Collider at Brookhaven National Laboratory which will study the quark-gluon plasma to further the understanding of quantum chromodynamics (QCP) matter and interactions. A prototype of the sPHENIX electromagnetic calorimeter (EMCal) was tested at the Fermilab Test Beam Facility in Spring 2018 as experiment T-1044.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Uncertainty-Informed Volume Visualization using Implicit Neural Representation

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MC-Dropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.

Saklani, Shanu↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Additive Polynomial Time Integrators, Part I: Framework and Fully Implicit-Explicit Collocation Methods

In this paper we generalize the polynomial time integration framework to additively partitioned initial value problems. The framework we present is general and enables the construction of many new families of additive integrators with arbitrary order-of-accuracy and varying degree of implicitness. In this first work, we focus on a new class of implicit-explicit polynomial block methods that are based on fully implicit Runge–Kutta methods with Radau nodes and that possess high stage order. Here, we show that the new fully implicit-explicit (FIMEX) integrators have improved stability compared to existing IMEX Runge–Kutta methods, while also being more computationally efficient due to recent developments in preconditioning techniques for solving the associated systems of nonlinear equations. For PDEs on periodic domains where the implicit component is trivial to invert, we will show how parallelization of the right-hand side evaluations can be exploited to obtain significant speedup compared to existing serial IMEX Runge–Kutta methods. For parallel (in space) finite element discretizations, the new methods can achieve orders of magnitude better accuracy than existing IMEX Runge–Kutta methods and/or achieve a given accuracy several times times faster in terms of computational runtime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗
DOE OSTI2021

Are suspensory ligaments important for middle ear reconstruction?

As the resolution of 3D printing techniques improves, the possibility of individualized, 3-ossicle constructions adds a new dimension to middle ear prostheses. In order to optimize these designs, it is essential to understand how the ossicles and ligaments work together to transmit sound, and thus how ligaments should be replicated in a middle ear reconstruction. The middle ear ligaments are thought to play a significant role in maintaining the position of the ossicles and constraining axis of rotation. Paradoxically, investigations of the role of ligaments to date have shown very little impact on middle ear sound transmission. We explored the role of the two attachments in the gerbil middle ear analogous to human ligaments, the posterior incudal ligament and the anterior mallear process, severing both attachments and measuring change in hearing sensitivity. The impact of severing the attachments on the position of the ossicular chain was visualized using synchrotron microtomography imaging of the middle ear. In contrast to previous studies, a threshold change on the order of 20 dB across a wide range of frequencies was found when both ligaments were severed. Concomitantly, a shift in position of the ossicles was observed from the x-ray imaging and 3D renderings of the ossicular chain. These findings contrast with previous studies, demonstrating that these ligaments play a significant role in the transmission of sound through the middle ear. It appears that both mallear and incudal ligaments must be severed in order to impair sound transmission. The results of this study have significance for middle ear reconstructive surgery and the design of 3D-printed three-ossicle biocompatible prostheses.

3D printing↗
DOE OSTI2023

Progress on an Electron Beam Profile Monitor at the Fermilab Main Injector

The current program at Fermilab involves the construction of a new superconducting linear accelerator (LINAC) to replace the existing warm version. The new LINAC, together with other planned improvements, is in support of proton beam intensities in the Main Injector (MI) that will exceed 2 MW. Measuring the transverse profiles of these high intensity beams in a ring requires non-invasive techniques. The MI uses ionization profile monitors as its only profile system. An alternative technique involves measuring the deflection of a probe beam of electrons with a trajectory perpendicular to the proton beam. This type of device was installed in MI and initial studies of it have been previously presented. This paper will present the status and recent studies of the device utilizing different techniques.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Acceptance Calculation and Study of Fault Scenarios in the PIP-II Linac

The Proton Improvement Plan (PIP)-II is a proposed high intensity proton facility being developed at the Fermilab. The PIP-II is primarily based on construction of a new superconducting radio frequency (SRF) linear accelerator (linac) that would deliver an average beam current of 2mA with output energy up to 800 MeV. The acceptance of an accelerator outlines its sensitivity against deviations from its nominal design parameters. Consequently, a higher acceptance minimizes possibility of the beam loss. This paper presents longitudinal and transverse acceptance of the PIP-II SRF linac. Furthermore, paper also discusses implications of fault scenarios, such as the cavity failure, on the beam optics and acceptances.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Design Considerations for Fermilab Multi-MW Proton Facility in the DUNE/LBNF era

Fermilab has submitted two Snowmass whitepapers on a future 2.4~MW upgrade for DUNE/LBNF featuring a 2 GeV extension of the PIP-II linac and the construction of a new rapid-cycling-synchrotron. This paper summarizes the relationship between these two scenarios, emphasizing the commonalities and tracing the differences to their original design questions. In addition to a high-level summary of the two 2.4~MW upgrade scenarios, there is a brief discussion of staging, beamline capabilities, subsequent upgrades, and relevant R&D. We are proposing a vigorous program to address various challenges associated with each scenario and to down-select the concept, most suitable to provide proton beams for years to come.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Progress on an Electron Beam Profile Monitor at the Fermilab Main Injector

The current program at Fermilab involves the construction of a new superconducting linear accelerator (LINAC) to replace the existing warm version. The new LINAC, together with other planned improvements, is in support of proton beam intensities in the Main Injector (MI) that will exceed 2 MW. Measuring the transverse profiles of these high intensity beams in a ring requires non-invasive techniques. The MI uses ionization profile monitors as its only profile system. An alternative technique involves measuring the deflection of a probe beam of electrons with a trajectory perpendicular to the proton beam. This type of device was installed in MI and initial studies of it have been previously presented. This paper will present the status and recent studies of the device utilizing different techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Optimization Studies of Radiation Shielding for PIP-II Project at Fermilab

The Proton Improvement Plan-II (PIP-II) at Fermilab represents a significant advancement in the quest to answer some of the most profound questions about our universe using the world's most intense high-energy neutrino beam. The project requires the construction of a new addition to the Fermilab accelerator complex – an 800-MeV high-intensity superconducting linear accelerator. Ensuring the safety and regulatory compliance of this ambitious project is paramount, necessitating thorough dose rate assessments under both normal operational and accidental scenarios to align with the Fermilab Radiological Control Manual (FRCM) standards. Our approach included a shielding optimization used for the simulations with the Monte Carlo code MARS [1,2,3] to incorporate new magnet and collimator designs, essential for reflecting the current state of PIP-II infrastructure. The implementation of high-resolution detector planes, despite their computational demands, enabled us to gather detailed radiation field data crucial for optimizing shielding configurations. To overcome the significant computational demands, we developed a branching code that drastically reduced simulation runtimes while maintaining statistical integrity. This was achieved through geometry splitting and the application of Russian Roulette techniques, tailored to prioritize regions of interest based on predefined importances and weight limits.

43 PARTICLE ACCELERATORS↗