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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Galvanostatic Intermittent Titration Technique Reinvented: Part I. A Critical Review

The galvanostatic intermittent titration technique (GITT), introduced in 1977 by Weppner and Huggins, provided a readily accessible means to measuring the chemical diffusion coefficient of electrochemical electrode materials. The method continues to be widely used today, but the reported diffusivity values are highly inconsistent, ranging as much as four orders of magnitude for some Li layered oxide compositions. Even qualitative trends of diffusivity are inconsistent, suggesting significant flaws in the implementation of the method. Other variants of the GITT method also suffer from similar inconsistency problems. Here we identify numerous sources of significant error including composition-dependent reaction overpotentials, mathematical flaws in the relaxation analysis methods, finite-size and non-planar geometry effects, inter-particle inhomogeneity issues, early transient effects, and surface area uncertainties. We propose a simple relaxation analysis scheme using the time variable t relax + τ - t relax , where t relax is relaxation time and τ is the galvanostatic pulse duration. We also propose to use dense diffusion-limited samples to isolate the bulk-diffusion process in the time domain. Chemical diffusivity can be extracted much more reliably with this improved implementation of the GITT method.

25 ENERGY STORAGE↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2021

Optimal interpolation of global dissolved oxygen: 1965–2015

Oxygen inventory of the global ocean has declined in recent decades potentially due to the warming-induced reduction in solubility as well as the circulation and biogeochemical changes associated with ocean warming and increasing stratification. Earth System Models predict continued oxygen decline for this century with profound impacts on marine ecosystem and fisheries. Observational constraint on the rate of oxygen loss is crucial for assessing the ability of models to accurately simulate these changes. There are only a few observational assessments of the global oceanic oxygen inventory reporting a range of oxygen loss. This study develops a gridded data set of dissolved oxygen for the global oceans using optimal interpolation method. The resulting gridded product includes full-depth map of dissolved oxygen as 5-year moving average from 1965 to 2015 with uncertainty estimates. The uncertainty can come from unresolved small-scale and high-frequency variability and mapping errors. The multi-decadal trend of global dissolved oxygen is in the range of -281 to -373 Tmol/decade. This estimate is more conservative than previous works. In this study, the grid points far from the observations are essentially set equal to zero anomaly from the climatology. Calculating global inventory with this approach produces a relatively conservative estimate; thus, the results from this study likely provide a useful lower bound estimate of the global oxygen loss.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Depth-dependent links between microbial taxa and nitrous oxide emissions in a long-term cotton cropping system employing soil health practices

Long-term management practices can shape soil microbial communities in ways that influence nitrogen (N) dynamics and nitrous oxide (N 2 O) emissions. We leverage a 41-year continuous cotton cropping experiment with contrasting tillage, cover cropping, and N fertilization regimes to investigate how these long-term strategies influence soil microbial communities and their associations with N 2 O fluxes during the cotton growing season. Using 16S rRNA gene metabarcoding, we assessed microbial composition in surface and subsurface soils and evaluated its relationship with temporal N 2 O emissions. Among the management practices, N fertilization – a known driver of N 2 O emissions – had the strongest effect on microbial community composition and was linked to a greater number of taxa correlated to N 2 O emissions, particularly in surface soils. Soil pH emerged as a key variable influencing microbial structure across depth and was negatively associated with both N 2 O emissions and microbial composition in the surface layers of fertilized soils. In total, 57 archaeal/bacterial taxa were correlated with N 2 O fluxes, but only seven were shared across depths, suggesting distinct microbial contributors in surface and subsurface soils. Several of these taxa have been previously reported to be associated with N and C cycling processes such as nitrate respiration or carbon turnover, indicating functional context to their correlation with N 2 O fluxes. Temporal shifts in the abundance of key taxa aligned with seasonal peaks in N 2 O emissions, notably in early and late August, and were most pronounced under conventional tillage, hairy vetch cover cropping, and N fertilization. While 16S-based associations cannot confirm functional gene presence or activity, these findings demonstrate that long-term fertilization and associated soil acidification are dominant drivers of microbial shifts linked to N 2 O emissions and highlight the importance of accounting for depth-specific and seasonal microbial dynamics when evaluating management impacts on greenhouse gas emissions.

16S rRNA gene sequencing↗
DOE OSTI2023

Growth and passivation of individual carbon nanoparticles by C2H2 addition at high temperatures: Dependence of growth rate and evolution on material and size

Absolute kinetics for reactions of C2H2 with a series of ∼60 individual carbon nanoparticles (NPs) from graphite, graphene, graphene oxide, carbon black, diamond, and nano-onion feedstocks were measured for temperatures (TNP) ranging from 1200 to 1700 K. All the NPs were observed to gain mass by carbon addition under conditions that varied with feedstock but with large variations in initial growth rate. Long reaction periods were studied to allow the evolution of growth rates over time to be observed. Diamond NPs were found to passivate against C2H2 addition if heated above ∼1400 K, and the highly variable initial reactivity for carbon nano-onions was found to depend on the presence of non-onion-structure surface carbon. For graphitic and carbon black NPs, three distinct growth modes were observed, correlated with the initial NP mass (Minitial). Smallest graphitic and carbon black NPs, with masses <∼25 MDa, initially grew rapidly but also passivated quickly after adding <4% of Minitial. NPs in the 20–50 MDa range also passivated but only after multiple waves of fast growth separated by periods of low reactivity, with up to ∼11% total mass gain before passivation. The largest carbon black and graphitic NPs, with Minitial >50 MDa, grew rapidly and continuously, adding up to ∼300% of Minitial with no sign of rate slowing as long as C2H2 was present. The efficiencies for C2H2 addition and etching by O2 are strongly correlated, but the correlation changes as the NPs passivate. Growth and passivation mechanisms are discussed.

Chemistry↗
DOE OSTI2022

Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model

Abstract. Lakes in the Arctic are important reservoirs of heat with much lower albedo in summer and greater absorption of solar radiation than surrounding tundra vegetation. In the winter, lakes that do not freeze to their bed have a mean annual bed temperature >0 ∘C in an otherwise frozen landscape. Under climate warming scenarios, we expect Arctic lakes to accelerate thawing of underlying permafrost due to warming water temperatures in the summer and winter. Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance. We applied the LAKE 2.0 model to simulate water temperatures in three Arctic lakes in northern Alaska over several years and tested the sensitivity of the model to several perturbations of input meteorological variables (precipitation, shortwave radiation, and air temperature) and several model parameters (water vertical resolution, sediment vertical resolution, depth of soil column, and temporal resolution). The LAKE 2.0 model is a one-dimensional model that explicitly solves vertical profiles of water state variables on a grid. We used a combination of meteorological data from local and remote weather stations, as well as data derived from remote sensing, to drive the model. We validated modeled water temperatures with data of observed lake water temperatures at several depths over several years for each lake. Our validation of the LAKE 2.0 model is a necessary step toward modeling changes in Arctic lake ice regimes, lake heat balance, and thermal interactions with permafrost. The sensitivity analysis shows us that lake water temperature is not highly sensitive to small changes in air temperature or precipitation, while changes in shortwave radiation and large changes in precipitation produced larger effects. Snow depth and lake ice strongly affect water temperatures during the frozen season, which dominates the annual thermal regime of Arctic lakes. These findings suggest that reductions in lake ice thickness and duration could lead to more heat storage by lakes and enhanced permafrost degradation.

Clark, Jason A. (ORCID:0000000248105261)↗
DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗
DOE OSTI2022

Seamless Projections of Global Storm Surge and Ocean Waves Under a Warming Climate

For coastal adaptation purposes, it is important to estimate the climate related changes in extreme sea levels due to storm surges and ocean waves, in addition to mean sea level rise. In this study, we provide the first consistent and continuous estimation of projected changes in global storm surges and ocean waves from the past to the warmer future, based on an extremely high resolution global climate model. The spatial pattern in the trend of annual maximum sea surface heights and wave heights is predominantly driven by changes in tropical cyclone (TC) frequency. In the western North Pacific, future TC frequencies are projected to decrease, and the annual maximum sea surface heights and wave heights show decreasing trends (-20 cm/century and -200 cm/century). Although highly intense tropical cyclones are enhanced in the warmer climate, highly extreme storm surges and wave heights do not necessarily increase due to the large natural variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Characterizing the effect of incrementally increasing dry bulb temperature on linear and nonlinear measures of heart rate variability in nonpregnant, mid-gestation, and late-gestation sows

Abstract Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. HRV is a commonly used noninvasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) nonpregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1–37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19–20.99, 21–22.99, 23–24.99, 25–26.99, 27–28.99, 29–30.99, 31–32.99, 33–34.99, and 35–36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r–r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.

Agriculture↗
DOE OSTI2023

Duke Forest FACE (FACTS-I): Plant and Soil Response Data

This dataset describing the responses of plant and soil pools and fluxes to elevated atmospheric CO2 concentration and increased nitrogen supply was collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment from 1996 to 2012. The dataset includes data files for allometry (diameter at breast height, tree height, and height to live crown base), leaf area index, biomass (stem, branch, foliage, and root biomass, tree density, and basal area), net primary productivity (stem, branch, foliage, reproductive, and coarse root NPP), sap flux density, soil CO2 efflux, and stem temperature. Data files were formatted as .csv (Microsoft Excel or other spreadsheet programs can be used to read the format) and file descriptions, including variable name, unit, and data range, can be found in ‘FileDescription_[data_name].txt’ files. The Duke FACE experiment was in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plots 1-6 with a common protocol of N-fertilization in 2005 and continued until 2012. The levels of treatment in this dataset were expressed as ambient CO2 (AMB) or elevated CO2 (ELE) for CO2 treatment and control soil (CONT) or fertilized soil (FERT) for N treatment, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗
DOE OSTI2024

Hydropower Flexibility Framework (Final Technical Report)

The Hydropower Flexibility Framework (HFF) tool focuses on providing the hydropower community with an effective means of assessing optimized hydropower plant outcomes. This tool combines both site specific characteristics, which act to constrain plant operation, and the hydrologic and grid characteristics which drive hydropower plant operation. The hydropower community faces a confluence of factors which drive the importance of developing such a capability, including an aging hydropower fleet subject to a range of modernization opportunities, a large number of hydropower plant relicensing activities which may affect operational requirements, an electrical grid with increasing levels of variable resources which must be balanced to maintain grid stability, and climate change influencing riverine hydrologic patterns outside of design characteristics. With support from the hydropower community, the project team developed the HFF tool and demonstrated the tool through a series of Use Cases. This guidance was developed as a part of the larger HFF tool User’s Manual (see Appendix B), a resource designed to inform other users and to empower community uptake of the tool. The HFF tool, hosted at https://hfftool.com/, was developed with the support of the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). EPRI is currently exploring alternatives to support the continued maintenance and functionally of the online tool.

13 HYDRO ENERGY↗
DOE OSTI2023

Climate Change Risks to Freshwater Subsistence Fisheries in Arctic Alaska: Insights and Uncertainty from Broad Whitefish Coregonus nasus

Arctic freshwater ecosystems and fish populations are largely shaped by seasonal and long-term watershed hydrology. In this paper, we hypothesize how changing air temperature and precipitation will alter freeze and thaw processes, hydrology, and instream habitat to assess potential indirect effects, such as the change to the foraging and behavioral ecology, on Arctic fishes, using Broad Whitefish Coregonus nasus as an indicator species. Climate change is expected to continue to alter hydrologic pathways, flow regimes, and, therefore, habitat suitability, connectivity, and availability for fishes. Warming and lengthening of the growing season will likely increase fish growth rates; however, the exceedance of threshold stream temperatures will likely increase physiological stress and alter life histories. We expect these changes to have mixed effects on Arctic subsistence fishes and fisheries. Management and conservation approaches focused on preserving the processes that create heterogeneity in aquatic habitats, genes, and communities will help maintain the resilience of Broad Whitefish and other important subsistence fisheries. Long-term effects are uncertain, so filling scientific knowledge gaps, such as identifying important habitats or increasing knowledge of abiotic variables in priority watersheds, is key to understanding and potentially mitigating likely impacts to Arctic fishes in a rapidly changing landscape.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Recurrent pattern of extreme fire weather in California

Abstract Historical wildfire events in California have shown a tendency to occur every five to seven years with a rapidly increasing tendency in recent decades. This oscillation is evident in multiple historical climate records, some more than a century long, and appears to be continuing. Analysis shows that this 5–7 year oscillation is linked to a sequence of anomalous large-scale climate patterns with an eastward propagation in both the ocean and atmosphere. While warmer temperature emerges from the northern central Pacific to the west coast of California, La Niña pattern develops simultaneously, implying that the lifecycle of the El Niño-Southern Oscillation that takes multiple years to form could be a trigger. The evolving patterns of the Pacific-to-North America atmospheric teleconnection suggest the role of tropical and subtropical forcing embedded in this lifecycle. These results highlight the semi-cyclical hydrological behavior as a climate driver for wildfire variability in California.

Environmental Sciences & Ecology↗
DOE OSTI2023

A robust estimator of mutual information for deep learning interpretability

Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Unraveling the Dynamics of Nucleosome Arrays

The organization of genomic DNA into chromatin is a fundamental determinant of genome stability, regulation, and cellular function. Nucleosomes, the basic repeating units of chromatin, assemble into higher-order structures whose organization and heterogeneity remain difficult to characterize using conventional ensemble-averaged techniques. A key need in the field is the development of experimental approaches capable of directly visualizing nucleosome assemblies and their structural variability at the single-molecule level. This LDRD Lab-Wide project focused on establishing and evaluating atomic force microscopy (AFM)–based approaches for the characterization of nucleosome assemblies. The work emphasized experimental workflows for preparing, imaging, and assessing multi-nucleosome systems, rather than isolated single nucleosomes. Through method development and exploratory measurements, the project demonstrated the feasibility of applying scanning probe microscopy to investigate chromatin-relevant assemblies and provided preliminary insight into the strengths and limitations of this approach for future quantitative studies. Results and lessons learned from this effort were disseminated to the broader scientific community through multiple national conference presentations, helping to position LLNL for continued work in chromatin and genome organization research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Controlled Assembly of Upconverting Nanoparticles for Low-Threshold Microlasers and Their Imaging in Scattering Media

Micron-sized lasers fabricated from upconverting nanoparticles (UCNP) coupled to whispering gallery mode (WGM) microresonators can exhibit continuous-wave anti-Stokes lasing useful for tracking cells, environmental sensing, and coherent stimulation of biological activity. The integration of these microlasers into organisms and microelectronics requires even smaller diameters, however, which raises threshold pump powers beyond practical limits for biological applications. To meet the need for low lasing thresholds and high fidelity fabrication methods, we use correlative optical and electron microscopy to uncover the nanoparticle assembly process and structural factors that determine efficient upconverted lasing. We show that 5 μm microspheres with controlled submonolayer UCNP coatings exhibit, on average, 25-fold lower laser thresholds (1.7 ± 0.7 kW/cm 2 ) compared to the mean values of the lowest threshold UCNP lasers, and variability is reduced 30-fold. WGMs are observed in the upconversion spectra for TiO 2 -coated microspheres as small as 3 μm, a size at which optical losses had previously prevented such observations. Finally, we demonstrate that the WGM signatures of these upconverting microlasers can be imaged and distinguished through tissue-mimicking phantoms. These advances will enable the fabrication of more efficient upconverting lasers for imaging, sensing, and actuation in optically complex environments.

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

Capillary Water in 2-D Drying-Cracking Soil Sub-Grain Models: Morphology and Kinematics of Evaporation and Haines Jumps

Morphing of capillary water during the drying of a cluster of three wet grains is imaged and measured. The uniqueness of the tests is in the grains being long cylinders to make the system as close to a 2-D one as possible. In this way, the Laplace pressure depends on the only one curvature of the meniscus, which can easily be followed and is continuously image processed. The motion of liquid/gas interface, and its rate, as well as of contact angle and perimeter, are also monitored. The drying water body has been known to undergo two modes of re-morphing: a slow, evaporation rate-controlled one and a fast, inertia-driven instabilities of the interfaces. Two particular forms of dynamic re-morphing are being followed: one, called classically an “air entry,” which is a meniscus jump before its approaching the throat between the top and a bottom grain and another jump of the bottom contact, with a splitting of the meniscus into two between only two of the three grains. Associated dynamic variables, capillary pressure, and surface tension forces developing prior to and in conjunction with the instabilities of the menisci are presented in a companion paper by Hueckel et al..

54 ENVIRONMENTAL SCIENCES↗