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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Surface/Bulk Structural Chemistry of Key Components of Solid Oxide Electrochemical Cells with In Situ / Operando Raman Spectroscopy

The remarkable attributes of solid oxide electrochemical cell technology (e.g., energy efficiency, low cost, scalability, low emissions, and operational flexibility, etc.) drive the wider adoption of electrochemical conversion routes for sustainability. It is critical for the codevelopment of solid oxide cell materials and processes to establish the mechanistic understanding of the underlying chemical phenomena at the molecular level. Herein, we summarize the advancements in Raman spectroscopy that provide structural/molecular information on electrode/electrolyte materials typically used in solid oxide cells for energy conversion. In particular, we discuss the multifactorial environment induced chemical processes that govern the performance and longevity of solid oxide electrochemical devices. The in situ/operando Raman spectroscopic investigations on the electrode/electrolyte materials reported in the literature are summarized with the emphasis on identification of key material properties that control the functional aspects of the solid oxide cells. The molecular level understanding of the electrochemical processes will allow advancement of the rational design of electrochemical materials for process level deployment of solid oxide cell technology.

Electrodes↗
DOE OSTI2024

Quantifying and Modeling the Impact of Phase State on the Ice Nucleation Abilities of 2-Methyltetrols as a Key Component of Secondary Organic Aerosol Derived from Isoprene Epoxydiols

Organic aerosols (OAs) may serve as ice-nucleating particles (INPs), impacting the formation and properties of cirrus clouds when their phase state and viscosity are in the semisolid to glassy range. However, there is a lack of direct parameterization between aerosol viscosity and their ice nucleation capabilities. In this study, we experimentally measured the ice nucleation rate of 2-methyltetrols (2-MT) aerosols, a key component of isoprene-epoxydiol-derived secondary organic aerosols (IEPOX-SOA), at different viscosities. These results demonstrate that the phase state has a significant impact on the ice nucleation abilities of OA under typical cirrus cloud conditions, with the ice nucleation rate increasing by 2 to 3 orders of magnitude when the phase state changes from liquid to semisolid. An innovative parametric model based on classical nucleation theory was developed to directly quantify the impact of viscosity on the heterogeneous nucleation rate. This model accurately represents our laboratory measurement and can be implemented into climate models due to its simple, equation-based form. Based on data collected from the ACRIDICON-CHUVA field campaign, our model predicts that the INP concentration from IEPOX-SOA can reach the magnitude of 1 to tens per liter in the cirrus cloud region impacted by the Amazon rainforest, consistent with recent field observations and estimations. This novel parameterization framework can also be applied in regional and global climate models to further improve representations of cirrus cloud formation and associated climate impacts.

2-methyltetrol↗
DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗
DOE OSTI2021

Hotspot Coevolution Is a Key Identifier of Near-Native Protein Complexes

Protein–protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each mode, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. Here in this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein–protein interfaces. We tested KFC-E on four benchmark sets of unbound examples and two benchmark sets of bound examples, with the results demonstrating a clear improvement over scores that examine conservation and coevolution across the entire interface.

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

Spectroscopic Determination of Key Energy Scales for the Base Hamiltonian of Chromium Trihalides

The van der Waals (vdW) chromium trihalides (CrX 3 ) exhibit field-tunable, two-dimensional magnetic orders that vary with the halogen species and the number of layers. Their magnetic ground states with proximity in energies are sensitive to the degree of ligand-metal (p-d) hybridization and relevant modulations in the Cr d-orbital interactions. We use soft X-ray absorption (XAS) and resonant inelastic X-ray scattering (RIXS) spectroscopy at Cr L -edge along with the atomic multiplet simulations to determine the key energy scales such as the crystal field 10 Dq and interorbital Coulomb interactions under different ligand metal charge transfer (LMCT) in CrX 3 (X= Cl, Br, and I). Through this systematic study, we show that our approach compared to the literature has yielded a set of more reliably determined parameters for establishing a base Hamiltonian for CrX 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Redox Homeostasis as a Key Regulator of Intramolecular Cyclization in Fungal Perylenequinones

Perylenequinones (PQs) such as hypocrellins and hypomycins are fungal-derived redox-active metabolites with known roles as photosensitizers in the oxidative stress response and applications in photodynamic therapy (PDT). Here, we report that Shiraia sp., a filamentous fungus, can survive and grow under strictly anaerobic (argon) conditions─an unexpected finding for a multicellular eukaryote. Modulating redox homeostasis through chemical reduction and oxygen limitation promotes the intramolecular cyclization of hypocrellins, enhancing hypomycin biosynthesis. Moisture content further influences these transformations, with high water levels favoring keto–enol tautomerization and dry, reducing environments promoting hydride substitution at the peripheral positions. These findings highlight redox modulation as a key driver of perylenequinone metabolism and suggest that PQs may contribute to maintaining redox balance under anaerobic stress, hinting at a broader role in oxygen-independent adaptation in filamentous fungi. This work offers new insights at the interface of redox biology, chemical signaling, and fungal metabolism, with potential implications for the stability and function of PQ-based PDT agents in hypoxic, reducing conditions such as tumor microenvironments.

cyclization↗
DOE OSTI2022

Tight Coupling of Surface and In-Plant Biochemistry and Convection Governs Key Fine Particulate Components over the Amazon Rainforest

We report that throughout the troposphere over the Amazon rainforest, isoprene epoxydiol-secondary organic aerosols (IEPOX-SOA) are key components of submicrometer particle mass. IEPOX-SOA is thought to form by multiphase chemical pathways in the atmosphere, but using detailed model simulations, we show that these pathways are strongly inhibited by the solid particle phase state prevailing in the upper troposphere. We find that 2-methyltetrol gases formed by in-plant biochemical oxidation pathways, their partitioning to fine particles, and dissolution in cloud droplets can explain over 90% of IEPOX-SOA mass concentrations in the upper troposphere. In agreement with previous PM10 chiral measurements, our simulations indicate that plant biochemical processes are the dominant source of particulate 2-methyltetrols in the lower troposphere as well (>70%). Our results imply that in-plant biochemistry plays a central but previously unidentified role in aerosol processes and atmosphere-biosphere-climate interactions over the rainforest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?

Despite decades of study, the identity of the dominant Li + -conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/Li 2 O/Li 2 CO 3 nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (LiPO 2 F 2 ), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature σ ≈ 0.18 mS cm –1 and E a ≈ 0.40 eV. Here, this enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li-P-O-F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces.

Zhong, Peichen [University of California, Berkeley↗
DOE OSTI2022

Expression Patterns of Energy-Related Genes in Single Cells Uncover Key Isoforms and Enzymes That Gain Priority Under Nanoparticle-Induced Stress

Cellular responses to nanoparticles (NPs) have been largely studied in cell populations, providing averaged values that often misrepresent the true molecular processes that occur in the individual cell. To understand how a cell redistributes limited molecular resources to achieve optimal response and survival requires single-cell analysis. Here we applied multiplex single molecule-based fluorescence in situ hybridization (fliFISH) to quantify the expression of 10 genes simultaneously in individual intact cells, including glycolysis and glucose transporter genes, which are critical for restoring and maintaining energy balance. We focused on individual gill epithelial cell responses to lithium cobalt oxide (LCO) NPs, which are actively pursued as cathode materials in lithium-ion batteries, raising concerns about their impact on the environment and human health. We found large variabilities in the expression levels of all genes between neighboring cells under the same exposure conditions, from only a few transcripts to over 100 copies in individual cells. Gene expression ratios among the 10 genes in each cell uncovered shifts in favor of genes that play key roles in restoring and maintaining energy balance. Among these genes are isoforms that can secure and increase glycolysis rates more efficiently, as well as genes with multiple cellular functions, in addition to glycolysis, including DNA repair, regulation of gene expression, cell cycle progression, and proliferation. Our study uncovered prioritization of gene expression in individual cells for restoring energy balance under LCO NP exposures. Broadly, our study gained insight into single-cell strategies for redistributing limited resources to achieve optimal response and survival under stress.

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

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Identifying Key Environmental Factors Explaining Temporal Patterns of DOC Export From Watersheds in the Conterminous United States

The export of dissolved organic carbon (DOC) from a watershed is a key component of the terrestrial biosphere carbon cycle. There is a need to improve our understanding of how and by how much various environmental factors are driving the temporal patterns of DOC export in order to accurately model and evaluate terrestrial carbon storage and fluxes. In this synthesis, we compiled observational data sets from 14 watersheds in the conterminous United States spanning the time period from 1981 to 2017. We used these data sets to examine the relative impacts of various climate, atmospheric deposition, and land cover factors on the temporal patterns of DOC export across watersheds of different sizes and landscape conditions, as well as the time-series autocorrelation of DOC export. Overall, our results suggest that the dominant factor on an annual scale was the amount of precipitation, which had a positive correlation with DOC export from a watershed. Overall increasing nitrogen deposition was coincident with increasing DOC export, and increasing sulfur deposition was coincident with declining DOC export. The seasonal pattern of DOC export was strongly regulated by air temperature, the long-term trend was negatively influenced by increasing sulfur deposition, and no obvious autocorrelation was detected in DOC export. In addition, higher rates of DOC export were positively correlated with greater area of wetlands within a watershed, but was not found to be strongly related to any of the other land cover types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Key Environmental and Ecological Variables of Wetland CH 4 and CO 2 Fluxes Change With Warming

Wetlands are important ecosystems for the global carbon cycle, impacting regional and global methane (CH 4 ) and carbon dioxide (CO 2 ) budgets. This study examines how environmental and ecological variables impact wetland CH 4 flux and net ecosystem exchange of CO 2 (NEE) across 17 sites globally. We also quantified the importance of variables for each wetland type and site at monthly scale under normal and warm temperatures using dominance analysis. We identified soil and air temperature (TS, TA, respectively) as key variables influencing wetland CH4, and latent heat (LE) and shortwave radiation (SW) for NEE under normal and warm conditions. However, the importance of some variables shifted with warming. For predicting the variability of wetland CH4 flux under warming, gross primary productivity (GPP) and LE, replacing wind direction (WD), were dominant variables for tropical swamps, while NEE was important for high-latitude fens and bogs under warm temperatures. For wetland NEE, the role of TA and TS decreased across all wetland types with warming, while vapor pressure deficit (VPD) became more important for mid and high-latitude wetlands. Our results reveal the complex responses of wetland carbon flux to environmental and ecological variables with warming and provide new insights into improving wetland models by incorporating additional variables and accounting for the changing roles of variables in carbon flux under warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Gas diffusion electrodes, reactor designs and key metrics of low-temperature CO 2 electrolysers

We report that CO 2 emissions can be recycled via low-temperature CO 2 electrolysis to generate products such as carbon monoxide, ethanol, ethylene, acetic acid, formic acid and propanol. In recent years, progress has been made towards an industrially relevant performance by leveraging the development of gas diffusion electrodes (GDEs), which enhance the mass transport of reactant gases (for example, CO 2 ) to the active electrocatalyst. Innovations in GDE design have thus set new benchmarks for CO 2 conversion activity. In this Review, we discuss GDE-based CO 2 electrolysers, in terms of reactor designs, GDE composition and failure modes, to identify the key advances and remaining shortfalls of the technology. This is combined with an overview of the partial current densities, efficiencies and stabilities currently achieved and an outlook on how phenomena such as carbonate formation could influence the future direction of the field. Our aim is to capture insights that can accelerate the development of industrially relevant CO 2 electrolysers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Equally high efficiencies of organic solar cells processed from different solvents reveal key factors for morphology control

The power conversion efficiency of organic solar cells (OSCs) is exceeding 20%, an advance in which morphology optimization has played a significant role. It is generally accepted that the processing solvent (or solvent mixture) can help optimize morphology, impacting the OSC efficiency. Here we develop OSCs that show strong tolerance to a range of processing solvents, with all devices delivering high power conversion efficiencies around 19%. By investigating the solution states, the film formation dynamics and the characteristics of the processed films both experimentally and computationally, we identify the key factors that control morphology, that is, the interactions between the side chains of the acceptor materials and the solvent as well as the interactions between the donor and acceptor materials. Our work provides new understanding on the long-standing question of morphology control and effective guides to design OSC materials towards practical applications, where green solvents are required for large-scale processing.

14 SOLAR ENERGY↗
DOE OSTI2026

Revealing key structures for reversible sulfur redox in amorphous polymeric sulfur

Amorphous polymeric sulfur cathodes, such as sulfurized polyacrylonitrile (SPAN), enable high-energy lithium-sulfur batteries without cobalt or nickel, leveraging abundant sulfur. However, the limited in situ understanding of their synthesis and electrochemistry has impeded targeted optimization. Here, in this study, we integrate operando high-energy total scattering with sulfur K-edge X-ray absorption spectroscopy to monitor SPAN's formation and cycling in real time. Our results show that S-C bond formation halts further fusion of cyclized polyacrylonitrile, fostering π-π stacking and a transition from long-chain to short-chain sulfur-critical for reversible sulfur redox. These features synergistically minimize polysulfide dissolution and charge-transfer resistance, enabling optimized SPAN to achieve high capacity retention over 1,000 cycles. Operando X-ray absorption spectroscopy reveals that residual protons drive thiol-thione tautomerism, with lithium replacement during the first discharge causing ~20% irreversible capacity loss. To enhance performance, minimizing -NH groups and expanding pyridine networks are key. These findings transform SPAN optimization from empirical tuning to mechanism‑guided engineering and point the way towards sulfur loadings and energy densities competitive with state‑of‑the‑art Li‑ion cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Time-resolved quantification of key species and mechanistic insights in low-temperature tetrahydrofuran oxidation

Here, we investigate the kinetics and report the time-resolved concentrations of key chemical species in the oxidation of tetrahydrofuran (THF) at 7500 torr and 450–675 K. Experiments are carried out using high-pressure multiplexed photoionization mass spectrometry (MPIMS) combined with tunable vacuum ultraviolet radiation from the Berkely Lab Advanced Light Source. Intermediates and products are quantified using reference photoionization (PI) cross sections, when available, and constrained by a global carbon balance tracking approach at all experimental temperatures simultaneously for the species without reference cross sections. From carbon balancing, we determine time-resolved concentrations for the ROO˙ and ˙OOQOOH radical intermediates, butanedial, and the combined concentration of ketohydroperoxide (KHP) and unsaturated hydroperoxide (UHP) products stemming from the ˙QOOH + O 2 reaction. Furthermore, we quantify a product that we tentatively assign as fumaraldehyde, which arises from UHP decomposition via H 2 O or ˙OH + H loss. The experimentally derived species concentrations are compared with model predictions using the most recent literature THF oxidation mechanism of Fenard et al., (Combust. Flame, 2018, 191, 252–269). Our results indicate that the literature mechanism significantly overestimates THF consumption and the UHP + KHP concentration at our conditions. The model predictions are sensitive to the rate coefficient for the ROO˙ isomerization to ˙QOOH, which is the gateway for radical chain propagating and branching pathways. Comparisons with our recent results for cyclopentane (Demireva et al., Combust. Flame, 2023, 257, 112506) provide insights into the effect of the ether group on reactivity and highlight the need to determine accurate rate coefficients of ROO˙ isomerization and subsequent reactions.

Demireva, Maria↗