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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

YSO implantation detector for beta-delayed neutron spectroscopy

A segmented-scintillator-based implantation detector was developed to study the energy distribution of β-delayed neutrons emitted from exotic isotopes. The detector comprises a 34 × 34 YSO scintillator coupled to an 8 × 8 anode Position-Sensitive Photo-Multiplier Tube (PSPMT) via a tapered light guide. The detector was used at RIBF, RIKEN for time-of-flight-based neutron spectroscopy measurement in the 78Ni region. The detector provides the position and timing resolution necessary for ion-beta correlations and time of flight measurements. The detector provides a high ~80% beta-detection efficiency and a sub-nanosecond timing resolution. This contribution discusses the details of the design, operation, implementation, and analysis developed to obtain neutron time-of-flight spectrum and the analysis methods in the context of neutron-rich nuclei in the 78Ni region.

Implantation detector↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
DOE OSTI2021

Capillary absorption spectroscopy for high temporal resolution measurements of stable carbon isotopes in soil and plant-based systems

Capillary Absorption Spectroscopy (CAS) is a relatively new analytical technique for performing stable isotope analysis. Here, we demonstrate the utility of CAS by recording and quantifying variation in 13C in controlled and biologically relevant applications. We calibrated CAS system response to increased 13CO2, with an observed ~4‰ increase in measured Δ13C for each 0.03 ppm shift in 13CO2 concentration. We leveraged this calibration to quantify rates of biogeochemical processes using a 13C tracer. For example, we monitored microbial respiration of 13C-glucose within an agricultural soil at 10 s quantification intervals and results demonstrated 8.6% ± 0.4 of added glucose was converted to 13CO2 within 1.5 h of incubation. We expanded the demonstration by adapting a rhizobox to permit continuous monitoring of 13CO2 in a soil (as distinct from plant) headspace to track the timing and quantify respiration rates of fresh plant photosynthate and observed a 3.5 h delay between plant exposure to a13CO2 tracer and the first signs of respiration by soil biota. These experiments highlight CAS is effective in producing high temporal resolution quantification of 13CO2 and demonstrate potential applications.

13CO2↗
DOE OSTI2020

Effect of initial γ -irradiation on infrared laser ablation of poly(vinyl alcohol) studied by infrared spectroscopy

We report the effect of initial γ-irradiation of poly(vinyl alcohol) (PVA) on the kinetics of polymer ablation under CO 2 laser irradiation was studied using infra-red spectroscopy. The rate of laser ablation rapidly decreases with increasing time of laser ablation up to 10 s. Initial γ-irradiation of a dose up to 100 kGy reduces the rate of the laser ablation, just like the effect of laser exposure. The number of OH and CH groups decrease in PVA both with laser irradiation and with γ-irradiation. The number of OH groups decreases more under laser irradiation. Unsaturated bonds are formed in γ–irradiated PVA on the surface of the crater formed by laser ablation of the polymer that has undergone radiolysis. Increasing the dose of initial γ-irradiation from 100 to 2300 kGy leads to an increase in the intensity of the IR bands of the unsaturated bonds. The number of IR bands in the spectra of the laser ablation crater of PVA does not change with the dose of initial γ-irradiation. A dose of radiolysis of 3500 kGy significantly changes the IR spectra of both the ablation crater and the coating formed from the ablation products. Many bands characteristic for PVA are no longer present and the intensities of bands corresponding to unsaturated bonds are increased. A computational model for loss of water and ketone formation was developed for PVA and PVA including a diol using G3(MP2). The highest activation energy barrier step in the decomposition reaction was formation of the enol species and water was shown to catalyze all decomposition steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A simulation pipeline for fast neutron imaging and spectroscopy using quantified detector attributes

Radiation imaging capabilities, essential in the nuclear nonproliferation regime, facilitate source localization and, in certain cases, spectroscopy. Scatter-based neutron cameras, which can measure the neutron signatures from special nuclear material, hold particular interest. Systems incorporating organic scintillators can extract neutron energy spectra, potentially distinguishing fission neutron sources from others, such as alpha-neutron sources. The development and testing of a scatter-based neutron imager, however, can be challenging without having an accurate simulation model or first constructing a prototype. This work describes a simulation pipeline that takes output from MCNPX-PoliMi simulations and creates the expected back-projection neutron images and neutron energy spectra. This pipeline was developed to improve the modeling of fast neutron imagers and bridge the current gap in literature, which predominantly focuses on gamma-ray Compton imager models. This work also reports on the significance of various real-world system considerations and their effects on the simulated detector responses. The pipeline was verified and validated with experimental data collected using a 252 Cf spontaneous fission source using a fast neutron scattering imager developed at the University of Michigan.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep spectral CNN for laser induced breakdown spectroscopy

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Laser Ablation-Capillary Absorption Spectroscopy: A novel approach for high throughput and increased spatial resolution measurements of δ 13 C in plant-soil systems

Spatial and temporal heterogeneity of nutrient exchange within the rhizosphere is a topic of increasing interest, although challenging to study due to limits in existing analytical capabilities. Here, we developed and demonstrated a new approach applying laser ablation sample introduction to capillary absorption spectroscopy (LA-CAS) to characterize carbon isotopic distribution within plant tissues, rhizosphere, and soil. We exposed switchgrass plants to 13 CO 2 to allow tracing of 13 C-labelled photosynthates within plant biomass and into the associated soil. The LA-CAS methods we describe leverage continuous measurements of a sample stream derived from laser ablation line scans (10-25 µm in width) over a sample surface which enables the user to produce an isotope map with a) higher data density or b) over larger spatial areas versus previously existing techniques. This versatility of LA-CAS is assessed through testing of a range of laser parameters (spot size, scan rates) on various materials (soil, plant biomass/tissues, and rhizosphere). Here, we demonstrate the ability of LA-CAS to provide near instantaneous $δ$ 13 C measurements over isotopically distinct surfaces to enable high spatially resolved mapping of 13 C-labelled material within the rhizosphere. Applying LA-CAS analysis to plant biomass, we observed higher $δ$ 13 C values concentrated within phloem structures, consistent with localized photosynthate transport. When mapping across the rhizosphere, 13 C-enriched soil was typically present within 5-10µm of root boundaries with a steep spatial increase in $δ$ 13 C when the scan approached the middle of the root. As with all LA approaches, care is required to ensure accurate results as phenomena linked to ablation, combustion, and isotopic measurement can impart artifacts if not carefully controlled. Still, taken as a whole, our demonstrations highlight the increased sample throughput, improved data density, and enhanced $δ$ 13 C capability of LA-CAS versus other LA techniques and emphasize the role this method can play in future plant and rhizosphere related studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparing plant litter molecular diversity assessed from proximate analysis and 13 C NMR spectroscopy

Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13 C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hydrated doped-BaZrO 3 proton conductors studied by positron annihilation lifetime spectroscopy

The study of defect chemistry for doped BaZrO 3 proton conductors is of particular interest because of defect interactions that can affect the proton conductivity of the material. Protons incorporated due to the material's hydration can be trapped by negatively charged immobile dopants, reducing proton mobility. The reduction of the proton conduction impedes using BaZrO 3 materials in energy conversion applications at intermediate temperatures (300°C – 600°C). The probing of proton trapping in doped BaZrO 3 is hindered by the limited availability of techniques sensitive to defect chemistries. In this work, we used positron annihilation lifetime spectroscopy (PALS) to study the defect chemistry of Y-doped and Sc-doped BaZrO 3 . Using a two-state positron trapping model we showed that PALS can be used to study the defect chemistry of hydrated dense proton conductors. Positron trapping rates and lifetimes were correlated with doping levels of the materials. Probability significance t-tests were carried out for PALS parameters to verify whether there are differences/similarities for various populations: non-doped/doped, level and type of doping, high temperature, and surface effects. Further, the results revealed that the initial doping generates a significant number of traps available for positrons. Doping in yttrium increased the positron trapping rate, while this effect was not observed with scandium. Low-temperature hydration affects specimens significantly inhibiting positron trapping at undoped BaZrO 3 material and highly doped specimens. Positronium formation in rough surface layers, and highly doped specimens was detected but does not exceed 1%.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Combining fast magic angle spinning dynamic nuclear polarization with indirect detection to further enhance the sensitivity of solid-state NMR spectroscopy

Dynamic nuclear polarization (DNP) and indirect detection are two commonly applied approaches for enhancing the sensitivity of solid-state NMR spectroscopy. However, their use in tandem has not yet been investigated. With the advent of low-temperature fast magic angle spinning (MAS) probes with 1.3-mm diameter rotors capable of MAS at 40 kHz it becomes feasible to combine these two techniques. In this study, we performed DNP-enhanced 2D indirectly detected heteronuclear correlation (idHETCOR) experiments on 13 C, 15 N, 113 Cd and 89 Y nuclei in functionalized mesoporous silica, CdS nanoparticles, and Y 2 O 3 nanoparticles. The sensitivity of the 2D idHETCOR experiments was compared with those of DNP-enhanced directly-detected 1D cross polarization (CP) and 2D HETCOR experiments performed with a standard 3.2-mm rotor. Due to low CP polarization transfer efficiencies and large proton linewidth, the sensitivity gains achieved by indirect detection alone were lower than in conventional (non-DNP) experiments. Nevertheless, despite the smaller sample volume the 2D idHETCOR experiments showed better absolute sensitivities than 2D HETCOR experiments for nuclei with the lowest gyromagnetic ratios. Finally, for 89 Y, 2D idHETCOR provided 8.2 times better sensitivity than the 1 D 89 Y-detected CP experiment performed with a 3.2-mm rotor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Scanning tunneling spectroscopy of surface-oxidized Gallium-stabilized δ -phase plutonium

Here, the first contiguous experimental scanning tunneling spectroscopy (STS) measurements of the electronic structure of a plutonium (Pu) surface are presented and compared to current theoretical results. The experiments took place under UHV conditions, for which Pu is known to retain a native surface oxide. The STS data shows that no band gap is observable at room temperature. Theoretical calculations predict that bulk Pu oxides are semiconductors, but the spectral results for the cleaned Pu surface native oxide under UHV conditions could be better characterized as that of a semimetal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

X-ray photoelectron spectroscopy of epitaxial films and heterostructures

X-ray photoelectron spectroscopy is a powerful experimental technique that yields invaluable information on a range of phenomena that occur in solids, liquids, and gasses. The binding energy and shape of a photoemission peak is sensitive not only to the atomic number, valence and orbital from which the electron is ejected, but also to complex many-body effects that accompany photoemission. Provided the influences of these different drivers of spectral line shapes can be disentangled, a great deal can be learned about the electronic structures of specific atoms in the material of interest. In addition to these largely local effects, the long-range electrostatic environment and resulting electric potential at the emitting atom have a direct effect on the measured binding energies. Furthermore, this fact opens the door to extracting information about the dependence of the valence and conduction band minima on depth below the surface, which in turn allows both vertical and lateral electrical transport data to be better understood. One purpose of this Report is to describe how the different physical forces described above impact spectral properties of complex oxide epitaxial films. This class of materials typically incorporates transition metal cations in different valences and of all the elements, these exhibit the most complex core-level spectra. A second purpose is to show how a comprehensive understanding of local physical effects in x-ray photoemission allows one to extract detailed information on internal electric fields and band edge discontinuities in heterostructures involving complex oxides from core-level line shapes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Scanning tunneling microscopy and spectroscopy of NiTe 2

Nickel ditelluride is an unusual member of the transition metal dichalcogenide family which has garnered interest due to potential valley spin-polarized surface states near the Fermi level and the presence of Dirac nodes in its electronic band structure. In this work, exfoliation of bulk nickel ditelluride is performed under ultra-high vacuum in order to generate a clean surface for scanning tunneling microscopy and spectroscopy at 4.8 K. Multiple features in the observed electronic density of states are observed in the vicinity of the Fermi level and compared to calculated band structures to elucidate their origins. Finally, our results are consistent with the presence of the spin-polarized surface states, yet indicate trivial states, which are close to the Fermi level, can interfere with their potential utility in spintronic applications.

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

Revealing aggregation of non-fullerene acceptors in intermixed phase by ultraviolet-visible absorption spectroscopy

Non-fullerene acceptor (NFA) aggregation is crucial in determining bulk-heterojunction (BHJ) organic photovoltaic (OPV) performance. However, it is still a big challenge to characterize the nanostructure of NFAs in the disordered donor-acceptor intermixed phase. Here, we demonstrate a method to characterize NFA aggregation and composition in the intermixed phase by measuring NFA concentration-dependent ultraviolet-visible (UV-vis) absorption spectroscopy of BHJ films. In various polymer:NFA films, an absorption shift as a function of increasing molecular concentration (ASIMC) phenomenon is observed, and different NFA aggregation behaviors can be distinguished. The ASIMC method was then applied to study the influence of processing conditions on the NFA concentration in the intermixed phase of devices to establish a correlation with device efficiency. The current work provides a feasible tool to study the nanostructure of NFAs in the complex polymer matrix and to understand the variations in the NFA concentration in the intermixed phase under non-equilibrium conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular-level insights into CO 2 activation on Ni(111) from in situ infrared spectroscopy

The activation of CO 2 on nickel surfaces is the first step of various CO 2 conversion processes. Consequently, understanding the surface intermediates formed during this step and their adsorption sites is crucial for elucidating reaction mechanisms. In this study, we employ in situ infrared spectroscopy to investigate the interaction of 1 mbar CO 2 with a model Ni(111) catalyst between 25°C and 300°C. Under these conditions, CO 2 is activated via a direct dissociation into CO and atomic oxygen, while the oxidation state of the Ni(111) surface is primarily determined by residual H 2 and O 2 . The high surface coverage of CO and oxygen at ambient CO 2 pressure induces a competitive adsorption mechanism on the energetically favored hollow sites. This mechanism leads to a temperature-dependent distribution of CO between top and hollow sites, potentially explaining the effect of reaction conditions on the adsorption site of CO intermediates in CO 2 conversion reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Stream lamination and rapid mixing in a microfluidic jet for X-ray spectroscopy studies

Microfluidic mixers offer new possibilities for the study of fast reaction kinetics down to the microsecond time scale, and methods such as soft X-ray absorption spectroscopy are powerful analysis techniques. These systems impose challenging constraints on mixing time scales, sample volume, detection region size and component materials. The current work presents a novel micromixer and jet device which aims to address these limitations. The system uses a so-called ‘theta’ mixer consisting of two sintered and fused glass capillaries. Sample and carrier fluids are injected separately into the inlets of the adjacent capillaries. At the downstream end, the two streams exit two micron-scale adjoining nozzles and form a single free-standing jet. The flow-rate difference between the two streams results in the rapid acceleration and lamination of the sample stream. This creates a small transverse dimension and induces diffusive mixing of the sample and carrier stream solutions within a time scale of 0.9 microseconds. The reaction occurs at or very near a free surface so that reactants and products are more directly accessible to interrogation using soft X-ray. We use a simple diffusion model and quantitative measurements of fluorescence quenching (of fluorescein with potassium iodide) to characterize the mixing dynamics across flow-rate ratios.

Huyke, Diego A. (ORCID:0000000283356613)↗