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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗
DOE OSTI2024

An Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery

Lignocellulosic biomass is an abundant feedstock for producing sustainable fuels and chemicals. However, a key challenge in most biomass utilization strategies is the recovery of products from a dilute, typically aqueous, phase. In this respect, liquid–liquid extraction, which relies on a solvent to transfer a product of interest from one liquid phase to another (solvent-rich) phase, is a technology that can reduce the energy requirements for product recovery. To reduce solvent consumption, liquid–liquid extraction needs to be combined with another separation method (e.g., distillation) to recycle the solvent. Despite the research on solvent extraction, there are limited system-wide methods that allow us to determine when extraction is well suited to carry out a specific separation. Accordingly, we present a classification framework to predict whether extraction, coupled with distillation, is feasible and more economical than distillation. Our framework is based on features such as feed composition, liquid–liquid equilibrium constants, relative volatilities, and solvent price, and leads to trained classifiers that show good prediction accuracy. We further study how specific features influence the suitability of extraction. Furthermore, to showcase the applicability of the framework, we use it to analyze the separation of acetic acid from water.

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

Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems while allowing for underlying complex subsystem level interactions. Particularly, we present the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. Here, we detail a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64,000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Invariant soil water potential at zero microbial respiration explained by hydrological discontinuity in dry soils

We report that soil microbial respiration rates decrease with soil drying, ceasing below water potentials around -15 MPa. A proposed mechanism for this pattern is that under dry conditions, microbes are substrate limited because solute diffusivity is halted due to breaking of water film continuity. However, pore connectivity estimated from hydraulic conductivity and solute diffusivity (at Darcy's scale) is typically interrupted at much less negative water potentials than microbial respiration (-0.1 to -1 MPa). It is hypothesized here that the more negative respiration thresholds than at the Darcy's scale emerge because microbial activity is restricted to microscale soil patches that retain some hydrological connectivity even when it is lost at the macroscale. This hypothesis is explored using results from percolation theory and meta-analyses of respiration-water potential curves and hydrological percolation points. Lastly, when reducing the spatial scale from macroscale to microscale, hydrological and respiration thresholds become consistent, supporting the proposed hypothesis.

58 GEOSCIENCES↗
DOE OSTI2015

Atmospheric rivers do not explain UK summer extreme rainfall: ARS AND UK EXTREME RAINFALL

Extreme rainfall events continue to be one of thelargest natural hazards in the UK. In winter,heavy precipitation and floods have been linked with intense moisture transport events associated withatmospheric rivers (ARs), yet no large-scale atmospheric precursors have been linked to summer flooding inthe UK. This study investigates the link between ARs and extreme rainfall from two perspectives: (1) Givenan extreme rainfall event, is there an associated AR?(2) Given an AR, is there an associated extreme rainfallevent? We identify extreme rainfall events using theUK Met Office daily rain gauge data set and link theseto ARs using two different horizontal resolution atmospheric data sets (ERA-Interim and Twentieth CenturyReanalysis). The results show that less than 35% of winter ARs and less than 15% of summer ARs areassociated with an extreme rainfall event. Consistent with previous studies, at least 50% of extreme winterrainfall events are associated with an AR. However, less than 20% of the identified summer extreme rainfallevents are associated with an AR. The dependence of the water vapor transport intensity threshold used todefine an AR on the years included in the study, and on the length of the season, is also examined. Includinga longer period (1900–2012) compared to previous studies (1979–2005) reduces the water vapor transportintensity threshold used to define an AR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗
DOE OSTI2024

Suppressed Lone Pair Electrons Explain Unconventional Rise of Lattice Thermal Conductivity in Defective Crystalline Solids

Manipulating thermal properties of materials can be interpreted as the control of how vibrations of atoms (known as phonons) scatter in a crystal lattice. Compared to a perfect crystal, crystalline solids with defects are expected to have shorter phonon mean free paths caused by point defect scattering, leading to lower lattice thermal conductivities than those without defects. While this is true in many cases, alloying can increase the phonon mean free path in the Cd-doped AgSnSbSe 3 system to increase the lattice thermal conductivity from 0.65 to 1.05 W m –1 K –1 by replacing 18% of the Sb sites with Cd. It is found that the presence of lone pair electrons leads to the off-centering of cations from the centrosymmetric position of a cubic lattice. X-ray pair distribution function analysis reveals that this structural distortion is relieved when the electronic configuration of the dopant element cannot produce lone pair electrons. Furthermore, a decrease in the Grüneisen parameter with doping is experimentally confirmed, establishing a relationship between the stereochemical activity of lone pair electrons and the lattice anharmonicity. The observed “harmonic” behavior with doping suggests that lone pair electrons must be preserved to effectively suppress phonon transport in these systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Explaining the Size Dependence in Platinum-Nanoparticle-Catalyzed Hydrogenation Reactions

Hydrogenation reactions are industrially important reactions that typically require unfavorably high H 2 pressure and temperature for many functional groups. Herein we reveal surprisingly strong size-dependent activity of Pt nanoparticles (PtNPs) in catalyzing this reaction. Based on unambiguous spectral analyses, the size effect has been rationalized by the size-dependent d-band electron structure of the PtNPs. This understanding enables production of a catalyst with size of 1.2 nm, which shows a sixfold increase in turnover frequency and 28-fold increase in mass activity in the regioselective hydrogenation of quinoline, compared with PtNPs of 5.3 nm, allowing the reaction to proceed under ambient conditions with unprecedentedly high reaction rates. Finally, the size effect and the synthesis strategy developed herein may provide a general methodology in the design of metal-nanoparticle-based catalysts for a broad range of organic syntheses.

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

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

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

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Functional divergence in nitrogen uptake rates explains diversity–productivity relationship in microalgal communities

Abstract The relationship between biodiversity and productivity has emerged as a central theme in ecology. Mechanistic explanations for this relationship suggest that the role organisms play in the ecosystem (i.e., niches or functional traits) is a better predictor of ecosystem stability and productivity than taxonomic richness. Here, we tested the capacity of functional diversity in nitrogen uptake in experimental microalgal communities to predict the complementarity effect (CE) and selection effect (SE) of biodiversity on productivity. We grew five algal species as monocultures and as polycultures in pairwise combinations in homogeneous (ammonium, nitrate, or urea alone) and heterogeneous nitrogen (mixed nitrogen) environments to determine whether complementarity between species may be enhanced in heterogeneous environments. We show that the positive diversity effects on productivity in heterogeneous environments resulted from complementarity effects with no positive contribution by species‐specific SEs. Positive biodiversity effects in homogeneous environments, when present (nitrate and urea treatments but not ammonium), were driven both by CE and SE. Our results suggest that functional diversity increases species complementarity and productivity mainly in heterogeneous resource environments. These results provide evidence that the positive effect of functional diversity on community productivity depends on the diversity of resources present in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗
DOE OSTI2018

Not as easy as π: An insertional residue does not explain the π‐helix gain‐of‐function in two‐component FMN reductases

Abstract The π‐helix located at the tetramer interface of two‐component FMN‐dependent reductases contributes to the structural divergence from canonical FMN‐bound reductases within the NADPH:FMN reductase family. The π‐helix in the SsuE FMN‐dependent reductase of the alkanesulfonate monooxygenase system has been proposed to be generated by the insertion of a Tyr residue in the conserved α4‐helix. Variants of Tyr118 were generated, and their X‐ray crystal structures determined, to evaluate how these alterations affect the structural integrity of the π‐helix. The structure of the Y118A SsuE π‐helix was converted to an α‐helix, similar to the FMN‐bound members of the NADPH:FMN reductase family. Although the π‐helix was altered, the FMN binding region remained unchanged. Conversely, deletion of Tyr118 disrupted the secondary structural properties of the π‐helix, generating a random coil region in the middle of helix 4. Both the Y118A and Δ118 SsuE SsuE variants crystallize as a dimer. The MsuE FMN reductase involved in the desulfonation of methanesulfonates is structurally similar to SsuE, but the π‐helix contains a His insertional residue. Exchanging the π‐helix insertional residue of each enzyme did not result in equivalent kinetic properties. Structure‐based sequence analysis further demonstrated the presence of a similar Tyr residue in an FMN‐bound reductase in the NADPH:FMN reductase family that is not sufficient to generate a π‐helix. Results from the structural and functional studies of the FMN‐dependent reductases suggest that the insertional residue alone is not solely responsible for generating the π‐helix, and additional structural adaptions occur to provide the altered gain of function.

McFarlane, Jeffrey S.↗
DOE OSTI2015

Sneutrino Higgs models explain lepton non-universality in eejj, eνjj excesses

Recent searches for first-generation leptoquarks and heavy right-handed W R bosons have seen excesses in final states with electrons and jets. A bizarre property of these excesses is that they appear to violate lepton universality. With these results in mind, we study the phenomenology of supersymmetric models in which the Higgs arises as the sneutrino in an electron supermultiplet. Since the electron is singled out in this approach, one can naturally account for the lepton flavor structure of the excesses. In this work, we show that in such a framework, one can significantly alleviate the tension between the Standard Model and the data and yet evade current constraints from other searches. Finally we point out that correlated excesses are expected to be seen in future multilepton searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Explaining dark matter and B decay anomalies with an L μ - L τ model

We present a dark sector model based on gauging the L μ - L τ symmetry that addresses anomalies in b→ sμ + μ - decays and that features a particle dark matter candidate. The dark matter particle candidate is a vector-like Dirac fermion coupled to the Z' gauge boson of the L μ - L τ symmetry. We compute the dark matter thermal relic density, its pair-annihilation cross section, and the loop-suppressed dark matter-nucleon scattering cross section, and compare our predictions with current and future experimental results. We demonstrate that after taking into account bounds from Bs meson oscillations, dark matter direct detection, and the CMB, the model is highly predictive: B physics anomalies and a viable particle dark matter candidate, with a mass of ~ (5 - 23) GeV, can be accommodated only in a tightly-constrained region of parameter space, with sharp predictions for future experimental tests. The viable region of parameter space expands if the dark matter is allowed to have L μ - L τ charges that are smaller than those of the SM leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗