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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Localized corrosion: Passive film breakdown vs. Pit growth stability, Part VI: Pit dissolution kinetics of different alloys and a model for pitting and repassivation potentials

Here, the pitting resistance of SS316L, SS304, SS430, and pure Fe was studied in chloride-containing solution. Among these alloys, SS316L exhibits the highest pit initiation rate, but also the highest pitting potential. To explain this phenomenon, the maximum pit dissolution current density (i diss,max ) of the alloys in saturated pit solution was measured using one-dimensional artificial pit electrodes. A lower i diss,max was found to correlate with higher pitting and repassivation potentials. Based on a recently proposed pitting framework, a conceptual model for pitting and repassivation potentials was established, based on which the effect of i diss,max on pitting resistance was discussed.

one-dimensional artificial pit electrode↗
DOE OSTI2022

Fourier-based methods for removing mesh anomalies from angle resolved photoemission spectra

Recent improvements to spatial resolution in angle-resolved photo-emission spectroscopy (ARPES) have made it common to perform measurements with a very brief dwell time, for the purpose of mapping the spectral function over large surface regions. However, rapid measurement modalities can suffer a grid-like intensity modulation due to a wire mesh that is typically placed in front of the ARPES detector to block stray electrons. Here, we explore Fourier-based methods that can effectively remove this artifact, and improve the quality of ARPES images obtained in rapid scanning modes. In conclusion, an open source software package is provided containing implementations of demonstrated algorithms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

The global policy landscape of ISO 50001 energy management systems

While many options exist to improve industrial demand-side energy efficiency, energy management systems (EnMSs)—particularly those aligned with ISO 50001—are proven to drive continuous and meaningful energy performance improvements. Governments leverage these EnMSs in their policies to advance national objectives including enhancing industrial competitiveness and achieving environmental goals. Existing research has focused on the impact of EnMSs at the company level, while comprehensive work on EnMSs in a global policy context is lacking. We seek to close this gap by investigating the extent to which current national policies incorporate the utilization of EnMSs, particularly the ISO 50001 standard. Our paper employs a hybrid approach, combining a literature review and expert interviews across 28 governments representing > 86% of global primary energy consumption. We dissect policy mechanisms, governance levels, underlying motivations, and trends in present EnMS policies. We find that > 96% of the investigated countries include EnMSs within their policy scope; 90% of policies including EnMSs utilize the ISO 50001 standard in some capacity. Primary policy motivations include decarbonization, energy savings for industrial competitiveness, and energy system resilience. We highlight that in the EnMS context, policy mixes—combining economic incentives, regulatory instruments, and information-based approaches—are more effective than standalone measures. Our work provides a novel global overview of governmental EnMS policies, moving beyond whether EnMS should be adopted to focus on how they can be implemented most effectively.

Moreno, Francisco Luis↗
DOE OSTI2021

Deep learning inversion of gravity data for detection of CO 2 plumes in overlying aquifers

In this work, we developed an effective U-Net based deep learning (DL) model for inversion of surface gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution along a vertical cross-section due to CO 2 leakage through a wellbore within a deep CO 2 storage reservoir. We used synthetic data to model two types of CO 2 leakage scenarios: one CO 2 plume in a shallow aquifer (single plume case), and two plumes present at different depths (double plume case). The 3-D synthetic plume samples were created by sampling among predetermined CO 2 plume depths, saturations, and volumes. The corresponding surface gravity data on a rectangular grid were generated by a 3-D forward model. The U-Net model detected 72% of single-plume samples, and one or both plumes in 75% of double-plume samples. Most of the undetected single plumes have small gravity field strengths below the typical noise level of 5 μGal. This model generated reproducible, reliable predictions with acceptable errors and demonstrated improved spatial resolution over the conventional least-squares inversion. In contrast to the conventional least-squares inversion, which often overestimates the size of its target and underestimates its density, this U-Net model accurately delineated the boundary of a target. Furthermore, this DL inversion detected deep, small, or low saturation CO 2 plumes that are often more difficult to resolve with conventional gravity inversion methods. We note the limitations of this feasibility study, including the use of synthetic data with regular CO 2 plume shapes, and the prediction of a 2-D plume cross-section rather than the full 3-D plume, as well, we recognize the lower detection fraction for double-plume scenarios. Nevertheless, this study demonstrates that DL gravity inversion is a promising and potentially superior method to conventional least-squares inversion. Our U-Net based deep learning inversion approach may be adapted for inversion of other types of geophysical data. DL inversion can facilitate near real-time monitoring of geologic carbon sequestration to provide site operators with prompt information about subsurface CO 2 distribution for risk management and mitigation.

58 GEOSCIENCES↗
DOE OSTI2022

Exploring the NΛ–NΣ coupled system with high precision correlation techniques at the LHC

The interaction of Λ and Σ hyperons (Y) with nucleons (N) is strongly influenced by the coupled-channel dynamics. Due to the small mass difference of the NΛ and NΣ systems, the sizable coupling strength of the NΣ↔NΛ processes constitutes a crucial element in the determination of the NΛ interaction. In this letter we present the most precise measurements on the interaction of pΛ pairs, from zero relative momentum up to the opening of the NΣ channel. The correlation function in the relative momentum space for pΛ ⊕$\bar{p}$$\bar{Λ}$ pairs measured in high-multiplicity triggered pp collisions at $\sqrt{s}$=13 TeV at the LHC is reported. The opening of the inelastic NΣ channels is visible in the extracted correlation function as a cusp-like structure occurring at relative momentum k*=289 MeV/c. This represents the first direct experimental observation of the NΣ↔NΛ coupled channel in the pΛ system. The correlation function is compared with recent chiral effective field theory calculations, based on different strengths of the NΣ↔NΛ transition potential. A weaker coupling, as possibly supported by the present measurement, would require a more repulsive three-body NNΛ interaction for a proper description of the Λ in-medium properties, which has implications on the nuclear equation of state and for the presence of hyperons inside neutron stars.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Characterization of hot-pressed von Alpen type NASICON ceramic electrolytes

The Na-ion conducting solid electrolyte NASICON is a promising solid state electrolyte (SSE) for use in Na based electrochemical systems. The effects of particle morphology and synthesis of von Alpen type NASICON via hot-pressing were investigated. Spray dried particles were shown to improve mechanical and electrochemical properties. X-ray diffraction, scanning electron microscopy, impulse excitation measurements, Vickers hardness, and impedance spectroscopy were used to correlate relationships between phase purity, microstructure, mechanical properties, and electrochemical properties. Scanning electron microscopy showed the presence of a glass phase as between 19.08% and 28.23% area percentage and a ZrO 2 phase between 0.46% and 3.51% area percentage in the hot-pressed samples. Impulse excitation showed elastic modulus varying from 83.65 to 97.65 GPa. The first values of Poisson's ratio and shear modulus of NASICON are reported as between 0.23 and 0.26 and 33.87 to 38.79 GPa, respectively. Vickers hardness and fracture toughness measurements showed Vickers hardness ranging from 4.30 to 4.68 GPa and fracture toughness ranging from 1.05 to 1.09 MPa m ½ . Impedance spectroscopy showed grain conductivities ranging from 1.023 to 1.287 mS/cm, with grain boundary resistance percentages between 73.22 and 47.5%. Total conductivities ranged from 0.292 to 0.596 mS/cm, with 0.596 mS/cm being achieved at room temperature for the spray dried powders with lower grain boundary resistance percentages.

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

A Trapping-Micro-LC-FAIMS/dCV-MS Strategy for Ultrasensitive and Robust Targeted Quantification of Protein Drugs and Biomarkers

The sensitivity of LC-MS in quantifying target proteins in plasma/tissues is significantly hindered by co-eluted matrix interferences. While antibody-based immuno-enrichment effectively reduces interferences, developing and optimizing antibodies are often time-consuming and costly. Here, in this study, by leveraging the orthogonal separation capability of Field Asymmetric Ion Mobility Spectrometry (FAIMS), we developed a FAIMS/differential-compensation-voltage (FAIMS/dCV) method for antibody-free, robust, and ultra-sensitive quantification of target proteins directly from plasma/tissue digests. By comparing the intensity-CV profiles of the target vs. co-eluted endogenous interferences, the FAIMS/dCV approach identifies the optimal CV for quantification of each target protein, thus maximizing the signal-to-noise ratio (S/N). Compared to quantification without FAIMS, this technique dramatically reduces endogenous interferences, showing a median improvement of the S/N by 14.8-fold for the quantification of 17 representative protein drugs and biomarkers in plasma or tissues and a 5.2-fold median increase in S/N over conventional FAIMS approach, which uses the peak CV of each target. We also discovered that the established CV parameters remain consistent over months and are matrix-independent, affirming the robustness of the developed FAIMS/dCV method and the transferability of the method across matrices. The developed method was successfully demonstrated in three applications: the quantification of monoclonal antibodies with sub-ng/mL LOQ in plasma, an investigation of the time courses of evolocumab and its target PCSK9 in a preclinical setting, and a clinical investigation of low abundance obesity-related biomarkers. This innovative and easy-to-use method has extensive potential in clinical and pharmaceutical research, particularly where sensitive and high-throughput quantification of protein drugs and biomarkers is required.

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

New Insights into the Bulk and Surface Defect Structures of Ceria Nanocrystals from Neutron Scattering Study

Neutron diffraction and pair distribution function studies coupled with Raman spectroscopy have successfully unraveled the detailed oxygen defect structures of ceria nanocubes and nanorods. Two types of defect sites are revealed for the ceria nanocrystals: surface and bulk defects. It is proposed that the surface oxygen defects in both types of CeO 2 nanocrystals are predominantly the partially reduced Ce 3 O 5+x , with the bulk defect structures dominated by interstitial Frenkel-type oxygen vacancies. Ceria nanorods possess much higher concentration of surface oxygen defects relative to the nanocubes, albeit with only slightly higher concentration of bulk Frenkel-type oxygen vacancies. Upon annealing the nanorod sample at 600 °C under vacuum (~10 –4 to 10 –5 mbar), a partially reduced ceria phase with long-range oxygen vacancy ordering (Ce 3 O 5+x ) has been observed experimentally for the first time. This intriguing observation that surface defect phases can take on ordered defect sublattices under certain conditions is of great value in understanding the temperature-dependent catalytic performance of ceria nanocrystals. Furthermore, a drastic decrease of the surface vacancies in the ceria nanocrystals is observed upon exposure to SO 2 , especially for the nanorods, a likely origin for the sulfur poisoning effect on ceria-based materials. This study suggests that tailoring surface morphology is a promising strategy to control defect properties of ceria nanomaterials. It also provides fundamental insights to stabilize surface oxygen defects in CeO 2 nanocrystals to achieve high redox performance under corrosive environments such as under SO 2 /SO x exposure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI 3 phase ([C 4 H 10 NO][PbI 3 ]) and a two-dimensional (2D) (morph) 2 PbI 4 phase ([C 4 H 10 NO] 2 [PbI 4 ]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we discuss possible reaction schemes that may selectively promote the formation of morph-Pb-I phases with different dimensionalities. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Magnetosynthesis Effect on the Structure and Ground State of Cu 2+ -Based Antiferromagnets

Synthetic variables can have an outsized influence on the crystal structure and magnetic properties of a material, particularly those of quantum materials. In this work, we investigate the impact of synthesis under a magnetic field (magnetosynthesis) on the crystal structure and magnetic properties of several Cu 2+ (S = 1/2)-based materials with antiferromagnetic interactions and varying levels of magnetic frustration, from simple antiferromagnets to a quantum spin liquid. Here, we develop methods to apply small (0.09–0.37 T) magnetic fields during low-temperature hydrothermal, evaporative, and rehydration syntheses of the simple antiferromagnet CuCl 2 ·2H 2 O, the canted antiferromagnet (Cu,Zn) 3 Cl 4 (OH) 2 ·2H 2 O, the frustrated and canted antiferromagnet atacamite Cu 2 (OH) 3 Cl, and the highly frustrated quantum spin liquid herbertsmithite Cu 3 Zn(OH) 6 Cl 2 . We report the first single-crystal X-ray structural determination of the Cu 3 Cl 4 (OH) 2 ·2H 2 O structure type and probe the stability of this phase both experimentally and computationally. Atacamite Cu 2 (OH) 3 Cl synthesized under a 0.19 T field experiences a 0.15 K (∼3%) decrease in its Néel transition temperature. This result suggests that magnetosynthesis with small applied fields may have a very subtle influence upon the magnetic properties of moderately magnetically frustrated 3 d materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of Porous Crystalline Materials for Selective Binding of O 2 from Air

Porous materials that take advantage of different chemisorptive behaviors of O 2 and N 2 could be an effective alternative to approaches based on physisorption for this important separation. Recently, the tetra-nuclear cobalt complex [(Co(III) 2 (bpbp)O 2 ) 2 bdc](PF 6 ) 4 (CSD code: GAMVIB; bpbp – = 2,6-bis(N,N-bis(2-pyridylmethyl)aminomethyl)-4-tert-butylphenolato; bdc 2– = 1,4-benzenedicarboxylato) was shown to have potential for O 2 /N 2 separations based on this concept. This observation raises the question of what other known materials have similar properties. Here, we combine structure screening with a high-throughput periodic density functional theory (DFT) workflow to investigate O 2 and N 2 adsorption in materials from validated crystal structure databases (e.g., CoRE-MOF database and CSD database). These calculations identify multiple materials that have similar di-Co clusters to GAMVIB that are predicted to selectively bind O 2 over N 2 , and suggest design rules that can be used to tune the O 2 and N 2 affinities in materials of this kind.

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

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Effect of Surface Texture on Pinhole Formation in SiO x -Based Passivated Contacts for High-Performance Silicon Solar Cells

High-efficiency silicon solar cells rely on some form of passivating contact structure to reduce recombination losses at the crystalline silicon surface and losses at the metal/Si contact interface. One such structure is polycrystalline silicon (poly-Si) on oxide, where heavily doped poly-Si is deposited on a SiO x layer grown directly on the crystalline silicon (c-Si) wafer. Depending on the thickness of the SiO x layer, the charge carriers can cross this layer by tunneling (<2 nm SiO x thickness) or by direct conduction through disruptions in the SiOx, often referred to as pinholes, in thicker SiO x layers (>2 nm). In this work, we study structures with tunneling- or pinhole-like SiO x contacts grown on pyramidally textured c-Si wafers and expose variations in the SiOx layer properties related to surface morphology using electron-beam-induced current (EBIC) imaging. Using EBIC, we identify and mark regions with potential pinholes in the SiO x layer. We further perform high-resolution transmission electron microscopy on the same areas, thus allowing us to directly correlate locally enhanced carrier collection with variations in the structure of the SiO x layer. Our results show that the pinholes in the SiO x layer preferentially form in different locations based on the annealing conditions used to form the device. With greater understanding of these processes and by controlling the surface texture geometry, there is potential to control the size and spatial distribution of oxide disruptions in silicon solar cells with poly-Si on oxide-type contacts; usually, this is a random phenomenon on polished or planar surfaces. Such control will enable us to consistently produce high-efficiency devices with low recombination currents and low junction resistances using this contact structure.

14 SOLAR ENERGY↗
DOE OSTI2025

Elucidating the Competitive Hydrodeoxygenation of Lignin-Derived Oxygenates over Bulk MoO 3 Catalyst through Kinetic Analysis

Ambient pressure hydrodeoxygenation (HDO) of lignin-derived oxygenates over molybdenum oxide-based catalysts is an effective strategy to produce chemicals that can be directly integrated into our existing petrochemical infrastructure. Complexities pertaining to the simultaneous kinetic and mechanistic analysis of HDO have limited research endeavors to single-compound systems. Although valuable insight into the catalytic reaction has been gained through this approach, it provides limited understanding of the competitive adsorption behavior manifest in a realistic multioxygenate reaction environment. To address this shortcoming, simultaneous gas-phase acetone and anisole HDO was performed at 330 °C and ≤1 bar H 2 partial pressure over bulk MoO 3 . Propene, propane, and benzene were the HDO products formed, showing a similar product distribution to the single-compound system. Selectivity to propene and propane was ∼14 times higher than benzene, even at three times higher anisole partial pressure compared to acetone. A negative anisole HDO (−0.97 ± 0.22) rate order with varying acetone partial pressure suggested a strong inhibition effect on anisole HDO by acetone. Conversely, with increasing anisole partial pressure, a rate order of −0.07 ± 0.12 was observed for acetone HDO, implying a weak impact of anisole cofeed on acetone HDO. A kinetic-driven approach was taken to estimate the relative adsorption constants of the oxygenates. Acetone exhibited a 6.4 times higher adsorption propensity on the HDO active site than anisole. Relative adsorption constants for phenolics increased with increasing basicity of the oxygenate but decreased for aliphatic molecules, suggesting a volcano-shaped relationship. The results suggest the possibility of an optimal electron density around the molecule’s oxygen atom to maximize the molecule’s adsorption strength.

acid sites↗