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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Collaborative Research: Properties and Dynamics of the Shallow Crust (Final Report)

Ground motions recorded at one location are often extrapolated to nearby regions within a given radius. Here, we explore the appropriateness of spatial extrapolation using data from seven small aperture seismic network deployments in southern California. Six of these deployments are linear arrays of 4-13 stations, and one is a 2D array of 13 stations at Pinyon Flats Observatory. The spatial footprint array diameters are 3 km or less, and each array was operational for a year or more. From our base catalog (M2.5+ earthquakes; 4038 events; September 2010 - June 2023), automated methods remove temporally overprinted waveforms from nearby events (< 5 km) in quick succession (< 5 min) and data with nonviable waveforms. These 200 samples per second data are filtered at 0.5-25 Hz and must have signal-tonoise ratios (SNRs) of 2.5+. Peak ground acceleration (PGA) and peak ground velocity (PGV) are derived individually from the maximum absolute values of each of the 3-component waveforms (vertical, northsouth, and east-west). Five of the seven arrays traverse the San Jacinto fault, and two do not. Ground motion observations are compared with theoretical estimates from Abrahamson et al., 2014. On average, arrays deployed within and across fault zones consistently record ground motions above theoretical expectations, whereas off-fault arrays record ground motions at or slightly below theoretical expectations. We attribute these differences to site conditions because these trends prevail for the full data suites. For each network and each individual channel, the coefficient of variation indicates that the standard deviations are ~30±6% of the mean. Exploring relative ground motion contributions from all three channels (ternary plots), as expected, most data show that vertical ground motions are attenuated compared to horizontal ones. However, this is not always the case for RA array data, where vertical motions can be ~2-3 times larger than horizontal motions for select events near Cahuilla, CA. These anomalously high vertical motions are focal mechanism-related. These results suggest that ternary plots created using only a small amount of data can be used as a data quality metric and a tool to find anomalous features in three-component data.

58 GEOSCIENCES↗
DOE OSTI2024

Rhizosphere Soil Biogeochemical Data and Photosynthetic Data of Vicia Faba in a Rhizobox

Here we share the data in column format via csv files for pH, redox, and dissolved oxygen collected at hourly resolution from microelectrodes. Dissolved organic carbon concentrations collected from TOC are also provided in a similar format but are composited samples from hourly microdialysis collection. This provided resolution of diel rhizosphere dynamics belowground. Plant physiological data was also collected at every 5 min for 24 hr cycles in order to capture diel dynamics aboveground. This data was used to parameterize the reaction transport model eSTOMP-ROOTS, which examines the rhizosphere biogeochemistry of a growing Vicia faba plant. The aim was to investigate plant activity and belowground biogeochemical processes, particularly their impact on mineral-organic associations in the rhizosphere. We combined in-situ rhizosphere microsensor and plant physiological measurements with a 3-D plant-soil reactive transport model to explore the behavior of dissolved organic carbon (DOC) in the rhizosphere. Over several days, microdialysis probes placed at the root-soil interface in live soil showed distinct daily patterns of DOC concentration in the pore water. Spikes in DOC concentrations during the day aligned with peaks in leaf-level photosynthesis, accompanied by decreasing redox potential and dissolved oxygen levels, and increasing pH in the rhizosphere. This new mechanistic modeling framework, which integrates aboveground plant physiological data with non-destructive, high-resolution monitoring of rhizosphere processes, offers significant potential for studying the factors that control carbon storage in soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fuel Property Effects on Stochastic Preignition Events During Engine Load Transitions

Stochastic preignition (SPI) is an abnormal combustion phenomenon that can cause catastrophic engine damage. There have been several proposed mechanisms of SPI, where a uniform source is still not certain, however, SPI tendencies have been shown to be influenced by engine operating conditions, oil composition, engine age, and fuel chemical and physical properties. Laboratory research and testing for SPI propensity is challenging given the stochastic nature of events, as well as the potential for significant degradation of the engine platform and measuring equipment over time. Thus, SPI specific experiments are generally conducted under either sustained or cyclic patterning of steady-state operating conditions to avoid the influence of transient engine boundary conditions on test parameters of interest (e.g. oil additive package, fuel properties, engine speed/load, etc.). In this work a cyclically varying SPI test sequence involves a 5 min engine warmup period at a low engine load of around 4 bar gross indicated mean effective pressure (IMEPg), followed by a transition to high load (~20 bar IMEPg) at a constant 2000 rev/min engine speed for a total of 25 min. This individual test sequence load schedule is then sequentially repeated 10 times to generate significant statistical data for analysis. This work examines the influence of fuel chemical and physical properties on SPI tendency during the unsteady portion of the 10-cycle sequence (the first 5 min of the high load operation in each sequence of the loading cycle) which has been discarded from previous analyses due to the uncertainty in engine operating and thermal boundary conditions. Results from this analysis suggest an increasing trend in the ratio of SPI events during the unsteady test period relative to the steady test period with increasing fuel Reid Vapor Pressure (RVP), implying differences in uncontrolled ignition source terms, possibly from, fuel wall interactions and retention during the load transition phase of the test.

Splitter, Derek [ORNL] (ORCID:0000000174044047)↗
DOE OSTI2024

WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset

To satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have, we aimed at developing an updated version of the meteorological WIND Toolkit, named WIND Toolkit Long-term Ensemble Dataset (WTK-LED), which is a meteorological dataset providing time series every 5 min and 2 km, including model uncertainty of wind speed at every modeling grid point so that users are provided with a range of possible wind speeds every 2 km. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: 1) Numerical simulations covering the continental United States, Alaska, and Hawaii, with high-resolution data being available for 3 years (2018-2020). 2) Climate simulations from Argonne National Laboratories covering the North American continent, including Alaska, Canada, and most of Mexico and the Caribbean Islands. These simulations complement the new WTK-LED to offer a 4-km dataset covering 20 years, from 2001-2020. 3) Specific long-term,high-resolution offshore simulations have been conducted separately for the US coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind data set. This report focuses on a description of the land-based WTK-LED for CONUS, Hawaii, and Alaska, for the 3-year 2-km/5-min dataset and the 20-year 4-km/hourly dataset, as well as the uncertainty quantification method. We also provide limited validation results. Based on our results to date, we suggest use cases and applications for each dataset of the WTK-LED.

17 WIND ENERGY↗
DOE OSTI2023

Impacts of a Prescribed Fire on Air Quality in Central New Mexico

A short-duration but high-impact air quality event occurred on 28 November 2018 along the Rio Grande Valley of New Mexico. This fire occurred outside the typical wildfire season, and greatly impacted the air quality in Socorro, NM, and the surroundings. Measurements were taken during the event using an aerosol light scattering technique (integrating nephelometer) and a particulate mass concentration monitor (DustTrak PM optical monitor). The instruments sampled the ambient air during the event on the campus of the New Mexico Institute of Mining and Technology in Socorro, New Mexico. The peak values on a 5-min basis of light scattering and the PM mass concentration reached 470 Mm -1 and 270 µg/m 3 , respectively. We examined the meteorological context of the event using local meteorological data and back trajectories using the NOAA HYSPLIT model to determine atmospheric transport and possible sources. Several fires, both prescribed and wildfires, occurred in the region including a prescribed burn at Bosque del Apache National Wildlife Refuge (17 km south-southeast of the receptor site). The data suggest that the prescribed burn at Bosque del Apache was the dominant contributor due to transport evidence and the event’s narrow spatiotemporal extent. The increasing importance of restoring ecosystem function using prescribed fire in wildland fire management will likely lead to more frequent air quality impacts and sets up policy tradeoffs that require a balance between these public goals. This study examines the evidence of the effects of a prescribed fire in a protected wildland area impacting the air quality in a nearby populated area.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2026

Reduced-order CFD modeling of cryogenic hydrogen isotope extrusion for pellet fueling

This study presents a reduced-order model (ROM) for computational fluid dynamics (CFD) simulations of cryogenic hydrogen isotope extrusions, focusing on protium (H₂) and deuterium (D₂) piston extruders. Using a 2D axisymmetric ROM in ANSYS-Polyflow, significant computational savings were achieved (runtime reduced from 9∼24 h to 3∼5 min), with extrusion force discrepancies between the 2D ROM and 3D models being on the order of 1%. Parametric studies identified optimal cutoff shear rates in the viscosity model (0.01/s for H₂ and 0.001/s for D₂), providing recommendations for future simulations. Finally, a comprehensive comparison of ROM results with experimental data was performed across varying geometries, cryogenic materials, temperatures, extrusion lengths, and piston velocities. Predictions at low extrusion temperatures met the objective of providing quick and efficient solutions with an acceptable extrusion force error of approximately 10% or less, validating the effectiveness of the 2D ROM approach. However, at high temperatures closer to the triple point, extrusion force error grows, which necessitates developing an improved model that accounts for temperature effects, e.g. melting. Nevertheless, the findings still represent a significant improvement in efficiency of CFD modeling of cryogenic hydrogenic extrusion. The ROM framework can also be extended to tritium (T2) and screw extruders, which will ultimately provide a fast and effective tool for optimizing pellet injector design for ITER and future reactor systems.

Fan, Joy [ORNL] (ORCID:0000000229751735)↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Increased Static Charge–Induced Threshold Voltage Shifts and Memristor Activity in Pentacene OFETs Comprising Polystyrene–Based Gate Dielectrics Containing Electroactive Small Molecule Crystallites

Top-contact bottom-gate pentacene OFETs are fabricated with single layer dielectrics comprised of either polystyrene (PS), poly(4-methylstyrene) (P4MS), or poly(4-tert-butylstyrene) (P4TBS). The polystyrenes are blended with varying concentrations of two different small molecules, dibenzotetrathiafulvalene (DBTTF) and 2,8-difluoro-5,11-bis(triethylsilylethynyl)anthradithiophene (diF-TES-ADT), to form small, separated crystallites contained throughout the polymer dielectric layer. The OFET characteristics of these devices are investigated and their threshold voltage shifts are measured after –70 V static charging for 5 min. Two-terminal measurements are conducted using multiple different gate biases in the range of –50 to +50 V to investigate memristor behavior in the devices. OFETs containing DBTTF exhibited ΔVth increases as large as 330% relative to control OFETs containing no DBTTF, while OFETs containing at least 7.5 wt.% DBTTF exhibited memristor activity, with currents ranging from 20 nA to 44 µA depending on the applied bias. Furthermore, this work demonstrates that including small, separated crystallites in polymer dielectrics enhances their charge storage ability and can be promising for creating nonbinary memory devices for data processing. Additionally, the observed memristor activity indicates the OFETs in this work can be used in development of neuromorphic systems that aim to mimic the synaptic behavior of the human nervous system.

25 ENERGY STORAGE↗
DOE OSTI2023

Data for Construction of a Compact Array of Microplasma Jet Devices and Its Application for Random Mutagenesis of Rhodosporidium toruloides

A small and efficient DNA mutation-inducing machine was constructed with an array of microplasma jet devices (7 × 1) that can be operated at atmospheric pressure for microbial mutagenesis. Using this machine, we report disruption of a plasmid DNA and generation of mutants of an oleaginous yeast Rhodosporidium toruloides . Specifically, a compact-sized microplasma channel (25 × 20 × 2 mm3) capable of generating an electron density of greater than 1013 cm–3 was constructed to produce reactive species (N2*, N2+, O, OH, and Hα) under helium atmospheric conditions to induce DNA mutagenesis. The length of microplasma channels in the device played a critical role in augmenting both the volume of plasma and the concentration of reactive species. First, we confirmed that microplasma treatment can linearize a plasmid by creating nicks in vitro. Second, we treated R. toruloides cells with a jet device containing 7 microchannels for 5 min; 94.8% of the treated cells were killed, and 0.44% of surviving cells showed different colony colors as compared to their parental colony. Microplasma-based DNA mutation is energy-efficient and can be a safe alternative for inducing mutations compared to conventional methods using toxic mutagens. This compact and scalable device is amenable for industrial strain improvement involving large-scale mutagenesis.

Conversion↗
DOE OSTI2024

Two-dimensional heteronuclear single quantum coherence (HSQC) NMR spectra of lignin isolated from field grown transgenic poplar

Here we present a curated dataset of a series of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from a field grown transgenic poplar engineered with a monolignol 4-O-methyltransferase (MOMT4). The poplar was collected from a 2-year-old rotation trees within a three-year field trial experiment. The poplar was Soxhlet-extracted with toluene/ethanol and the extractives-free poplar was then ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was extracted twice with 96:4 (v/v) 1,4-dioxane/water mixture at room temperature overnight. The extracts were combined, rotary evaporated, and freeze-dried to recover the lignin. The dry lignin samples were dissolved in deuterated dimethyl sulfoxide for NMR experiments. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence (hsqcetgpsisp2.2) on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 220 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 1024 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. All the data was processed using the Bruker’s TopSpin 3.6 software. The NMR spectra provides structural characteristics information about lignin in field grown transgenic MOMT4 poplar. Additional meta data is embedded in the raw spectra figures.

Lignin structure, HSQC, poplar, field trial, MOMT4↗
DOE OSTI2025

Spectral distortions to momentum and scalar exchanges by non-turbulent motion and patchy landscape variability

Modifications to the spectra of turbulent velocity and scalars and co-spectra of vertical fluxes of momentum and scalars due to patchy landscape heterogeneity and non-stationarity are explored for a Mediterranean ecosystem. About 9 months of high frequency measurements of the three velocity components, water vapor concentration, carbon dioxide concentration, and air temperature were analyzed for different seasons (spring/summer) and prevalent wind directions (southeast/northwest). The two wind directions sampled a contrast of clumped and patchy landscape comprised of olive trees (southeast) and wall bounded flow disturbed by the presence of few upwind trees (northwest). The measured spectra and co-spectra were also compared to theoretical scaling forms from stationary, planar homogeneous flow, in the absence of subsidence as derived from the Kansas experiment. To assess the role of low frequency non-turbulent motion on the spectral and co-spectral content, a 5-min Fourier cutoff was introduced and the analysis was limited to near-neutral conditions where the boundary layer depth is shallow compared to its unstable counterpart. It was shown that the velocity statistics were not appreciably impacted by the low-frequency motion causing non-stationarity. Moreover, the turbulent scalar fluxes were also shown not to be significantly impacted by such low frequency motion. The scalar variances were impacted, especially the water vapor variance and its concomitant spectral shape. When the non-turbulent motion was filtered, the scalar spectra at low wavenumbers followed expectations from the so-called attached eddy hypothesis (i.e. exhibited a $k^{-1}_x$ scaling with $k_x$ defining the longitudinal wavenumber) applicable for near-neutral conditions. For momentum co-spectra, the canonical shapes from the Kansas experiment appear to describe well the measurements here and in both dominant directions and seasons with some adjustment to the integral time scales based on wind direction. For the scalar co-spectra, deviations from the Kansas experiment were prevalent. The most noticeable and surprising deviations were their slow decay with increased sampling frequency at inertial subrange scales. This slow decay was shown not to contribute appreciably to the overall scalar fluxes. At those fine scales, predictions from local isotropy were expected to hold. The scalar co-spectral deviations from local isotropy were then discussed using a simplified co-spectral budget model where scalar–scalar co-spectra naturally emerged and the interplay between landscape heterogeneity and a scale-dependent pressure-scalar de-correlation time was postulated. It is also envisaged that the findings here offer a preliminary template for analyzing eddy-covariance data in situations that deviate from ideal conditions, especially regarding low-frequency modulations of scalar spectra and vertical scalar flux co-spectra.

Canopy turbulence↗
DOE OSTI2024

Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations

Here, this paper explores the effects of day of week and season of year demand variations for shared rides, along with realistic travel party sizes, on shared autonomous vehicle (SAV) services across the Austin, Texas region. Using the agent-based POLARIS program, synthetic person-trips that reflect travel-party size (from one to four persons) and demand variations over days and months, as evident in the National Household Travel Survey data were simulated in each scenario over a 24 h travel day. Results show that realistic party sizes can bring considerable changes to SAV fleet performance, including up to 8.5% higher service rates (number of requests accepted within 15 min), 5 min shorter journey times (wait time + travel time), 28% higher vehicle occupancies on weekends, and roughly 4% lower empty fleet VMT. Weekend travel is most impacted by season of year, with weekday travel patterns looking more uniform (thanks to work and school trips). Various performance metrics for the Austin network, like total and empty VMT, change by up to 30% when considering realistic variations in party size and time of year. This paper underscores the value of recognizing day-to-day and month-to-month variations in travel demand, and the importance of agent-based model equations to reflect travel-party size. Such realism can help quantify SAV seat occupancies more accurately, highlighting the importance of shared mobility. However, it also creates demand and supply issues for operators that now need more information on party size to manage dynamic ride-sharing, or those that may wish to shift their fleet vehicles to other regions for special events to protect profits while offering reasonable wait times to customers throughout the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Super-Resolved Single-Molecule Tracking Studies of Rhodamine B Accumulation on Fresh and Aged Polyethylene Terephthalate

It is well-known that toxic organic micropollutants (OMs) accumulate on the surfaces of microplastics. However, much remains to be learned about the exact molecular level mechanisms of OM accumulation and how these evolve as the plastics age. In this work, super-resolved single-molecule tracking (SMT) is used for the first time to investigate the accumulation of Rhodamine B (RhB) dye on fresh and artificially aged polyethylene terephthalate (PET) surfaces. PET thin films serve as models for microplastics, while RhB serves as a proxy for the OMs they accumulate. Artificial aging of the films is accomplished by exposing them in a UV-ozone chamber. Water contact angle, spectroscopic ellipsometry, and carbonyl index measurements reveal a gradual decrease in film hydrophobicity, thickness, and carbonyl content with age. Atomic force microscopy (AFM) data reveal an increase in surface roughness and confirm that the films remain largely intact and continuous across the aging times explored. In SMT experiments, wide-field fluorescence videos acquired from the water/PET interface under 7.5 pM RhB reveal both mobile and immobile dye molecules. Measurements of the frame-to-frame displacements of the dye show that diffusion occurs by a desorption-mediated mechanism and that the diffusion rate varies with PET film age. The surface density of mobile dye molecules decreases with increasing PET age, while the population of immobile molecules becomes relatively larger, suggesting an age-dependent transformation of the mechanism(s) by which the dye is accumulated. SMT data reveal that both mobile and immobile molecules repeatedly adsorb over the same surface sites, consistent with the emergence of nanoscale PET surface heterogeneity also revealed by AFM. Estimates of the adsorption coefficients are obtained using a nearest-neighbor analysis, giving values from 9.9 × 10 5 to 2.1 × 10 6 M –1 for immobile molecules and from 1.8 × 10 5 to 2.5 × 10 5 M –1 for mobile molecules on fresh and 5 min aged PET, respectively. Here, anomalous age-dependent variations in the velocity of molecular motion on the PET surface and in the population of immobile molecules are shown to correlate with changes in the strength of RhB adsorption.

Adsorption↗
DOE OSTI2024

Day-to-day reliability of basal heart rate and short-term and ultra short-term heart rate variability assessment by the Equivital eq02+ LifeMonitor in US Army soldiers

Introduction The present study determined the (1) day-to-day reliability of basal heart rate (HR) and HR variability (HRV) measured by the Equivital eq02+ LifeMonitor and (2) agreement of ultra short-term HRV compared with short-term HRV. Methods Twenty-three active-duty US Army Soldiers (5 females, 18 males) completed two experimental visits separated by >48 hours with restrictions consistent with basal monitoring (eg, exercise, dietary), with measurements after supine rest at minutes 20–21 (ultra short-term) and minutes 20–25 (short-term). HRV was assessed as the SD of R–R intervals (SDNN) and the square root of the mean squared differences between consecutive R–R intervals (RMSSD). Results The day-to-day reliability (intraclass correlation coefficient (ICC)) using linear-mixed model approach was good for HR (0.849, 95% CI: 0.689 to 0.933) and RMSSD (ICC: 0.823, 95% CI: 0.623 to 0.920). SDNN had moderate day-to-day reliability with greater variation (ICC: 0.689, 95% CI: 0.428 to 0.858). The reliability of RMSSD was slightly improved when considering the effect of respiration (ICC: 0.821, 95% CI: 0.672 to 0.944). There was no bias for HR measured for 1 min versus 5 min (p=0.511). For 1 min measurements versus 5 min, there was a very modest mean bias of −4 ms for SDNN and −1 ms for RMSSD (p≤0.023). Conclusion When preceded by a 20 min stabilisation period using restrictions consistent with basal monitoring and measuring respiration, military personnel can rely on the eq02+ for basal HR and RMSSD monitoring but should be more cautious using SDNN. These data also support using ultra short-term measurements when following these procedures.

General & Internal Medicine↗
DOE OSTI2024

HSQC spectra of lignin isolated from poplar stems

Here we present a curated dataset of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from stems of genetically engineered poplar through auxin signaling gene modification. The plants were grown in greenhouse with temperatures between 21 and 23 °C. Plants were harvested and the aboveground stems were cut off an approximately five-inch-long segment from the bottom end of the plant stem, debarked and air-dried for three weeks. The dried stem samples were Wiley milled (mesh size 20), Soxhlet-extracted with toluene/ethanol for 24 h to remove extractives. The extracted biomass was ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was freeze-dried to recover the lignin. The dry stem lignin samples were dissolved in deuterated dimethyl sulfoxide (d6) and transferred into a 5 mm tube. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 230 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 2048 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. Spectra were processed using the Bruker TopSpin 3.6 software. Additional meta data is embedded in the raw spectra figures.

HSQC, lignin, poplar, stems, CBI↗