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DOE OSTI2024

Nonthermal Plasma-Stimulated C–N Coupling from CH 4 and N 2 Depends on the Presence of Surface CH x and Plasma-Phase CN Species

Formation of C–N containing compounds from plasma-catalytic coupling of CH 4 and N 2 over various transition metals (Ni, Pd, Cu, Ag, and Au) is investigated using a multimodal spectroscopic approach, combining polarization-modulation infrared reflection–absorption spectroscopy (PM-IRAS) and optical emission spectroscopy (OES). Through sequential experiments utilizing CH 4 and N 2 nonthermal plasmas, we minimize plasma-phase reactions and identify key intermediates for C–N coupling on metal surfaces. Results show that simultaneous CH 4 and N 2 exposure with plasma stimulation produces surface C–N species. However, N 2 –CH 4 sequential exposure does not lead to C–N species formation, while CH 4 –N 2 sequential exposure reveals the presence of CH x surface species and CN radical species as key precursors to C–N species formation. From further analysis using X-ray photoelectron spectroscopy and liquid chromatography–mass spectrometry, the influence of exposure conditions on the degree of nitrogen incorporation and the nature of C–N species formed were revealed. The work highlights the importance of surface chemistry and exposure conditions in surface C–N coupling with plasma stimulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Forecast of Wildfire Potential Across California USA Using a Transformer

Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

Limber, Russell [ORNL]↗

NASA NTRS1990

A three-dimensional application with the numerical grid generation code: EAGLE (utilizing an externally generated surface)

Program EAGLE (Eglin Arbitrary Geometry Implicit Euler) is a multiblock grid generation and steady-state flow solver system. This system combines a boundary conforming surface generation, a composite block structure grid generation scheme, and a multiblock implicit Euler flow solver algorithm. The three codes are intended to be used sequentially from the definition of the configuration under study to the flow solution about the configuration. EAGLE was specifically designed to aid in the analysis of both freestream and interference flow field configurations. These configurations can be comprised of single or multiple bodies ranging from simple axisymmetric airframes to complex aircraft shapes with external weapons. Each body can be arbitrarily shaped with or without multiple lifting surfaces. Program EAGLE is written to compile and execute efficiently on any CRAY machine with or without Solid State Disk (SSD) devices. Also, the code uses namelist inputs which are supported by all CRAY machines using the FORTRAN Compiler CF177. The use of namelist inputs makes it easier for the user to understand the inputs and to operate Program EAGLE. Recently, the Code was modified to operate on other computers, especially the Sun Spare4 Workstation. Several two-dimensional grid configurations were completely and successfully developed using EAGLE. Currently, EAGLE is being used for three-dimension grid applications.

Houston, Johnny L.↗

NASA NTRS2023

Prototype Global Analysis of LISA Data with Multiple Source Types

The novel data analysis challenges posed by the Laser Interferometer Space Antenna (LISA) arise from the overwhelmingly large number of astrophysical sources in the measurement band and the density with which they are found in the data. Robust detection and characterization of the numerous gravitational wave sources in LISA data can not be done sequentially, but rather through a simultaneous global fit of a data model containing the full suite of astrophysical and instrumental features present in the data. While previous analyses have focused on individual source types in isolation, here we present the first demonstration of a LISA global fit analysis containing combined astrophysical populations. The prototype pipeline uses a blocked Metropolis Hastings algorithm to alternatingly fit to a population of ultra compact galactic binaries, known “verification binaries” already identified by electromagnetic observations, a population of massive black hole mergers, and an instrument noise model. The Global LISA Analysis Software Suite (GLASS) is assembled from independently developed samplers for the different model components. The modular design enables flexibility to future development by defining standard interfaces for adding new, or updating additional, components to the global fit without being overly prescriptive for how those modules must be internally designed. The GLASS pipeline is demonstrated on data simulated for the LISA Data Challenge 2b. Results of the analysis and a road-map for continued development are described in detail.

Gravitational Waves↗

DOE OSTI2024

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction—a retrospective analysis of the MIMIC database

Background This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO 2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO 2 /FiO 2 ratio, and PaO 2 value as protective factors. Conclusion NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients’ SOFA, APSIII, GCS, PaCO 2 , PaO 2 , PH, PaO 2 /FiO 2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

Fu, Wei↗

DOE OSTI2025

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon ( Scaphirhynchus albus ) in the Missouri River, Montana and North Dakota

We released nearly 1.0 million 1-day post-hatch (dph) and 5-dph pallid sturgeon (Scaphirhynchus albus) free embryos in the Missouri River on 1 July 2019 and sequentially captured survivors at multiple sites through a 240-km river reach to quantify daily growth and survival rates during the early life stages. Genetic analysis was used to assign captured fish to released family lots and known ages. Growth rate was similar (0.74–0.75 mm day –1 ) between the 1- and 5-dph age groups during the 3–4-day dispersal period when water temperature averaged 16.8 °C. Daily survival rate was 0.64 during 1–4 dph for the original 1-dph age group and 0.80 during 5–7 dph for the original 5-dph age group. Total survival during free embryo dispersal (hatch to 9 dph) was estimated as 0.0437. The transition from dispersing as free embryos to settling as benthic larvae was verified for fish originally released as 5 dph. Growth of settled larvae was quantified with a Gompertz model through 75 dph (9 September; 112 mm) when water temperature was 18.8–21.0 °C in the rearing areas. Settled larvae had an estimated daily survival rate of 0.96, and estimated total survival during 9–75 dph was 0.0714. This study provides the first empirical survival estimates for pallid sturgeon early life stages in natural settings and is one of few studies reporting similar information for other sturgeon species. Applications of this work extend to pallid sturgeon restoration programs where population models are being developed to predict recruitment potential and population responses to river management alternatives.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Bioleaching ion-unexchangeable rare earth in ion-adsorption type rare earth waste tailing

Ion-adsorption type rare earth ores (IREO) in China serve as the primary source of medium and heavy rare earth globally. With the rapidly growing demand for rare earth elements (REEs) and the dwindling supply of premium IREO, enhancing the recovery of REEs, especially the ion-unexchangeable REEs with ultra-low content and ambiguous speciation from IREO or its tailing, has become a critical trend and challenge. Here, this study identified the occurrences of ion-unexchangeable REEs, primarily detected in Fe-enriched minerals, xenotime, and monazite of IREO using TESCAN integrated mineral analyzer (TIMA) and laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) analysis. To extract these elusive REEs, a bioleaching technique utilizing Aspergillus niger (A. niger) metabolites was proposed, achieving a leaching yield of 31.4 wt%. Complementary sequential chemical extraction methods (SCEM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations further elucidate the underlying mechanism, involving the carboxylic acid produced by the metabolic process of microbes that dissolves goethite by disrupting Fe—O bonds and liberating REEs, which complexed with carboxylate (R—COO—), to promote further dissolution. This work offers insight into enhancing the recovery of ion-unexchangeable REEs from IREO or its tailing, paving the way for sustainable and efficient rare earth mining practices.

42 ENGINEERING↗

DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗

DOE OSTI2026

Exploring the Origins of Anti-Ambipolarity in BBL Polymer: Links to Redox Chemistry, Electronic Structure, and Structural Dynamics

We examine the intrinsic physical-chemical properties of the conjugated ladder-type polymer poly(benzimidazobenzophenanthroline) (BBL) in response to electron transfer. We aim at explaining the origin of the anti-ambipolar behavior behind the observed BBL nonlinear response associated with specific device architectures. To elucidate this point, we use theory and computation based on first principles, including density functional theory optimizations, ab initio molecular dynamics, time-dependent DFT, and Marcus-theory analysis. Our results reveal that this redox response is not simply monotonic but follows an alternating odd/even pattern in which gap narrowing and reopening occur sequentially before near-gapless behavior emerges at high charging. Converging theoretical evidence in this work demonstrates that bell shaped conductivity in BBL originates in its fundamental electronic structure and supramolecular organization.

FOS: Physical sciences↗

NASA NTRS1991

Generalized covariance analysis for partially autonomous deep space missions

A new covariance analysis method is presented that is suitable for the evaluation of multiple impulsive controllers acting on some stochastic process x. The method accommodates batch and sequential estimators with equal ease and accounts for time-delay effects in a natural manner. The formalism is developed in terms of a generalized state vector that is formed from the system state vector x, augmented by various fixed epoch estimates, and a data vector formed from discrete time observations of the system. Recursions are developed for time transition, measurement incorporation, and impulsive control updating of the generalized covariance matrix. Means of limiting the dimensional growth of the generalized state vector via the processes of estimator epoch adjustment and measurement vector deflation are described and the application of numerically stable matrix factorization methods to the generalized covariance recursions is outlined. The method is applied to the Magellan spacecraft to demonstrate the capability of ground-based optimal estimation and control of gyro/star scanner misalignment.

Boone, Jack N.↗

NASA NTRS1996

Attitude Accuracy Study for the Earth Observing System (EOS) AM-1 Spacecraft

Earth Observing System (EOS) spacecraft will take measurements of the Earth's clouds, oceans, atmosphere, land, and radiation balance. These EOS spacecraft are part of the National Aeronautics and Space Administration's Mission to Planet Earth, and consist of several series of satellites, with each series specializing in a particular class of observations. This paper focuses on the EOS AM-1 spacecraft, which is the first of three satellites constituting the EOS AM series (morning equatorial crossing) and the initial spacecraft of the EOS program. EOS AM-1 has a stringent onboard attitude knowledge requirement, of 36/41/44 arc seconds (3 sigma) in yaw/roll/pitch, respectively. During normal mission operations, attitude is determined onboard using an extended Kalman sequential filter via measurements from two charge coupled device (CCD) star trackers, one Fine Sun Sensor, and an Inertial Rate Unit. The attitude determination error analysis system (ADEAS) was used to model the spacecraft and mission profile, and in a worst case scenario with only one star tracker in operation, the attitude uncertainty was 9.7/ll.5/12.2 arc seconds (3 sigma) in yaw/roll/pitch. The quoted result assumed the spacecraft was in nominal attitude, using only the 1-rotation per orbit motion of the spacecraft about the pitch axis for calibration of the gyro biases. Deviations from the nominal attitude would show greater attitude uncertainties, unless calibration maneuvers which roll and/or yaw the spacecraft have been performed. This permits computation of the gyro misalignments, and the attitude knowledge requirement would remain satisfied.

Lesikar, James D., II↗

NASA NTRS2023

Soluble Organic Matter Molecular Atlas of Ryugu Reveals Cold Hydrothermalism on C-Type Asteroid Parent Body

The sample from the near-Earth carbonaceous asteroid (162173) Ryugu is analyzed in the context of carbonaceous meteorites soluble organic matter. The analysis of soluble molecules of samples collected by the Hayabusa2 spacecraft shines light on an extremely high molecular diversity on the C-type asteroid. Sequential solvent extracts of increasing polarity of Ryugu samples are analyzed using mass spectrometry with complementary ionization methods and structural information confirmed by nuclear magnetic resonance spectroscopy. Here we show a continuum in the molecular size and polarity, and no organomagnesium molecules are detected, reflecting a low temperature and water-rich environment on the parent body approving earlier mineralogical and chemical data. High abundance of sulfidic and nitrogen rich compounds as well as high abundance of ammonium ions confirm the water processing. Polycyclic aromatic hydrocarbons are also detected in a structural continuum of carbon saturations and oxidations, implying multiple origins of the observed organic complexity, thus involving generic processes such as earlier carbonization and serpentinization with successive low temperature aqueous alteration.

Asteroids, comets and Kuiper belt↗

NASA NTRS1984

Failure analysis of plasma-sprayed thermal barrier coatings

Thermally induced failure processes of plasma-sprayed thermal barrier coatings are examined. Cracking processes give rise to noise which was monitored by acoustic emission (AE) techniques. The sequential failure of coatings was examined from samples which were thermally cycled. Coatings of yttria-stabilized zirconia with and without a NiCrAlZr bond coat were plasma-sprayed onto U700 alloy rod. In some cases the substrate was intentionally overheated during deposition of the thermal protection system to check how this process variable influenced the AE response of the specimen. In this way a qualitative appraisal of how process variables affect coating integrity could be discerned in terms of cracking behavior. Results from up to seven consecutive thermal cycles are reported here. Coating failure was observed in all cases. Failure of the thermal protection system is progressive, since cracking and crack growth were observed prior to ultimate failure. Thus castastrophic failure occurs at some stage when there is a transformation from the microcrack to a macrocrack network.

Berndt, C. C.↗

NASA NTRS1984

Failure analysis of plasma-sprayed thermal barrier coatings

Berndt, C. C.↗

NASA NTRS2022

Sequential Star Formation in the Young SMC Region NGC 602: Insights from ALMA

NGC 602 is a young, low-metallicity star cluster in the "Wing" of the Small Magellanic Cloud. We reveal the recent evolutionary past of the cluster through analysis of high-resolution (∼0.4 pc) Atacama Large Millimeter/submillimeter Array observations of molecular gas in the associated H ii region N90. We identify 110 molecular clumps (R < 0.8 pc) traced by CO emission, and study the relationship between the clumps and associated young stellar objects (YSOs) and pre-main-sequence (PMS) stars. The clumps have high virial parameters (typical αvir = 4–11) and may retain signatures of a collision in the last ≲8 Myr between H i components of the adjacent supergiant shell SMC-SGS 1. We obtain a CO-bright-to-H2 gas conversion factor of XCO,B = (3.4 ± 0.2) × 10 20 cm−2 (K km s −1 ) −1 , and correct observed clump properties for CO-dark H 2 gas to derive a total molecular gas mass in N90 of 16,600 ± 2400 M ⊙ . We derive a recent (≲1 Myr) star formation rate of 130 ± 30 M ⊙ Myr −1 with an efficiency of 8% ± 3% assessed through comparing total YSO mass to total molecular gas mass. Very few significant radial trends exist between clump properties or PMS star ages and distance from NGC 602. We do not find evidence for a triggered star formation scenario among the youngest (≲2 Myr) stellar generations, and instead conclude that a sequential star formation process in which NGC 602 did not directly cause recent star formation in the region is likely.

Theo J O’Neill↗

DOE OSTI2023

Quantifying optical loss of high-voltage degradation modes in photovoltaic modules using spectral analysis

The direct current bias for photovoltaic (PV) modules interconnected in series-strings may include both high voltage negative (“HV-”) and positive (“HV+”) polarity with respect to the electrical ground. Multiple degradation modes, resulting in quantifiable optical loss, were found to occur during HV-/HV+ sequential stress, including corrosion of the external glass surface, encapsulant delamination (at its interfaces with the glass and the PV cell), internal haze formation (resulting from a chemical interaction between the glass and the encapsulant), corrosion and migration of the gridlines, and corrosion of the silicon nitride (Si x N y ) antireflective coating on the cell. The effects of these separate modes were examined using monolithic (e.g., glass or PV cell) and laminated-coupon (glass/encapsulant/glass or glass/encapsulant/cell/encapsulant/backsheet) specimens. Characterizations during and after unbiased accelerated testing at 85°C/85% relative humidity included spectrophotometry, optical microscopy, electron microscopy, and ellipsometry. For some module components (i.e., the glass and the Si x N y coating), the optical performance was determined through iterative analysis of empirical measurements. Concentrating on just their spectral effect, a novel model was then developed to estimate the transfer of light to the PV cell and the return of light from the PV module with simultaneous degradation mechanisms, which was compared with a mini-module previously subjected to HV-/HV+ stress. Here the model suggests that one third of the current loss observed for the mini-module can be attributed to the optical degradation of the packaging materials. The dominant degradation modes include encapsulant delamination and corrosion of the Si x N y coating. Recommendations are given so that the optical model may be improved relative to accelerated testing and validated relative to field aging.

14 SOLAR ENERGY↗