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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine Learning Eliminates Reanalysis Warm Bias and Reveals Weaker Winter Surface Cooling Over Arctic Sea Ice

The surface energy budget governs Arctic sea-ice growth/melt, yet observations are sparse, and reanalysis data sets suffer from systematic biases. Here, we train a neural network with observational data to bias-correct hourly ERA5 fluxes over Arctic ice-covered regions (≥70°N; sea-ice concentration >80%) for 1994–2024. Training data cover two full seasonal cycles and different sea-ice regimes. The neural network reduces RMSE for net shortwave radiation by ∼40%, downward longwave radiation by ∼16% and the total surface energy budget by ∼55%, eliminating the wintertime warm bias of ∼4 K in ERA5. Wintertime surface cooling is reduced by ∼50%, yielding thermodynamic ice-growth estimates of ∼80–120 cm, consistent with SMOS–CryoSat satellite thickness increases and in contrast to the 150–200 cm growth implied by ERA5. Our bias-corrected data capture the observed clear/cloudy states of the winter boundary layer and can be used to study Arctic climatology, evaluate climate models and drive sea-ice-ocean models.

Hossain, Akil [Alfred Wegener Institute for Polar ↗
DOE OSTI2025

A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof-of-concept framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_e$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet-18 performed best, achieving $R^2$ values of 0.99 and 0.98 for $ρ_e$ and $A_e$, respectively, and MAE of 0.10 for mathematical equation in single view tasks. Stereo view ResNet-18 further reduced RMSE by 40% for $ρ_e$ and $A_e$ and reduced MAE by 0.08 for $N_b$. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Ko, J. [Columbia Univ., New York, NY (United State↗
DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗
DOE OSTI2024

Silicon ion radiation as a viable surrogate for emulating neutron radiation damage in silicates

Abstract Nuclear power plants are aging around the world, and a precise assessment of irradiation damage in their components is needed. One key component, concrete, and specifically the silicates in its aggregates, can undergo significant expansion upon neutron radiation, which can lead to cracking and, ultimately, structural failure. However, assessing and predicting the extent of damage via neutron radiation is challenging due to reasons such as residual radioactivity and, most importantly, the high time involved. Here, we evaluate whether ion radiation can be a viable surrogate. Specifically, by employing Si 2+ ion radiations and a comprehensive multi-modal imaging protocol, we report mineral-specific responses for key silicates such as quartz, albite, anorthite, and microcline. We find that 10 MeV Si 2+ ions result in mineral expansions that are remarkably comparable to neutron radiation equivalent expansions (R 2 = 0.86, RMSE = 1.29%), opening up pathways towards rapid assessment of silicates subject to irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions

Abstract Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12// ω B97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol −1 ) relative to those calculated at ω B97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients $${k}_{\infty }(T)$$ k ∞ ( T ) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.

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

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT

NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.

Sun, He↗
DOE OSTI2025

Peatland fires in Alaska will double by the end of the century

During recent summers, warm and dry conditions have increased the occurrence of wildfires and potentially peat-fires across Alaska. Limitations in resolving the fine-scale distribution of peatlands and climate observations have constrained our ability to accurately predict peat-fire dynamics. Using a new high-resolution peatland map of Alaska, we evaluated the climate and environmental controls of past and future peat-fire activity. Ensemble machine learning models identified reduced soil moisture, higher temperatures, and evapotranspiration as key predictors of annual total burned peatland area (tenfold CV R 2 = 0.62, RMSE = 221.1 km 2 ). By the end of the twenty-first century, models forced with climate datasets from representative concentration pathways (RCPs) 4.5, 6.0, and 8.5 emission scenarios project a statewide doubling of burned peatlands (increasing 61–121%), with regional increases ranging from 25–165% in polar, 61–95% in boreal, and 102–106% in maritime ecoregions. These projections indicate that wildfires will progressively encroach further into organic-rich moist and wet peaty soils, potentially amplifying soil carbon release across Alaska.

climate-change ecology↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2022

A classical model for three-body interactions in aqueous ionic systems

We present a classical induction model to evaluate the three-body ion–water–water (I–W–W) and water–water–water (W–W–W) interactions in aqueous ionic systems. The classical description of the induction energy is based on electrostatic distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole on the O and H atoms of water. The monatomic ions were described by a point charge and a dipole–dipole polarizability, while for the polyatomic ions, distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole were used. The accuracy of the classical model is benchmarked against an accurate dataset of 936 (I–W–W) and 2184 (W–W–W) three-body terms for 13 different monatomic and polyatomic cation and anion systems. The classical model shows excellent agreement with the reference second order Moller–Plesset and coupled-cluster single double and perturbative triple [CCSD(T)] three-body energies. The Root-Mean-Square-Errors (RMSEs) for monatomic cations, monatomic anions, and polyatomic ions were 0.29, 0.25, and 0.12 kcal/mol, respectively. The corresponding RMSE for 1744 CCSD(T)/aVTZ three-body (W–W–W) energies, used to train MB-pol, was 0.12 kcal/mol. The accuracy of the proposed classical model demonstrates that the three-body term for aqueous ionic systems can be accurately modeled classically. This approach provides a fast, efficient, and as-accurate path toward modeling the three-body term in aqueous ionic systems that is fully transferable across systems with different ions without the need to fit to tens of thousands of ab initio calculations for each ion to extend existing many-body force fields to interactions between water and ions.

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

Chlorophyll- a dynamics in the lower Amazon River: insights from in situ and hyperspectral remote sensing using OCI-PACE

Chlorophyll-a concentration (Chla) is a key indicator of phytoplankton biomass and aquatic trophic status. However, satellite-derived Chla in sediment-rich waters, such as those found in the Lower Amazon River, remains challenging. The present study characterizes in situ Chla levels and their relationships with geographic, physical, and biogeochemical parameters in the Lower Amazon. Data collected between 2014 and 2017 across four hydrological seasons included measurements of Chla, remote sensing reflectance, and water quality parameters such as total suspended sediment, conductivity, water surface temperature, dissolved oxygen, pH, dissolved organic carbon and coloured dissolved organic matter. An empirical model was developed to estimate Chla using simulated hyperspectral bands from NASA’s PACE mission, achieving high performance (R 2 = 0.76; RMSE = 0.11 μg·L −1 ). Red bands proved particularly effective for Chla retrieval, while the addition of ultraviolet bands further enhanced model accuracy. The application of the developed model to satellite imagery yielded results consistent with in situ observations for the same hydrologic season. Seasonal variation and geographic location were major factors influencing Chla dynamics. This study provides a novel contribution to Chla estimation in optically complex, highly turbid waters and highlights the potential of the PACE mission to enhance global aquatic ecosystem monitoring. In conclusion, by offering freely available hyperspectral data with high radiometric resolution, PACE represents a significant advancement in the realm of remote sensing of aquatic environments.

Amazon River↗
DOE OSTI2023

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environment, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. Here, the results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Deep learning the properties of inorganic perovskites

We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Changes in precipitation and air temperature contribute comparably to permafrost degradation in a warmer climate

Abstract Surface energy budgets of high-latitude permafrost systems are poorly represented in Earth system models (ESMs), yet permafrost is rapidly degrading and these dynamics are critical to future carbon-climate feedback predictions. A potentially important factor in permafrost degradation neglected so far by ESMs is heat transfer from precipitation, although increases in soil temperature and thaw depth have been observed following increases in precipitation. Using observations and a mechanistic ecosystem model, we show here that increases in precipitation hasten active layer development beyond that caused by surface air warming across the North Slope of Alaska (NSA) under recent and 21st century climate (RCP8.5). Modeled active layer depth (ALD) in simulations that allow precipitation heat transfer agreed very well with observations from 28 Circumpolar Active Layer Monitoring sites (R 2 = 0.63; RMSE = 10 cm). Simulations that ignored precipitation heat transfer resulted in lower spatially-averaged soil temperatures and a 39 cm shallower ALD by 2100 across the NSA. The results from our sensitivity analysis show that projected increases in 21st century precipitation deepen the active layer by enhancing precipitation heat transfer and ground thermal conductivity, suggesting that precipitation is as important an environmental control on permafrost degradation as surface air temperature. We conclude that ESMs that do not account for precipitation heat transfer likely underestimate ALD rates of change, and thus likely predict biased ecosystem responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO 2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ecological Insights from Transferable Plant Biomass Mapping across the Arctic using High-resolution Structure-from-Motion and LiDAR Data

Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of Unoccupied Aerial Systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based Structure-from-Motion (SfM) or Light Detection and Ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and MODIS, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a Random Forest (RF) model (overall RMSE: 0.336 kg/m2), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within 2 years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings demonstrate the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the potential of our approach to be broadly applied to generate high-quality AGB data for ecological monitoring and model benchmarking across the Arctic.

Yang, Daryl [ORNL] (ORCID:0000000317057823)↗
DOE OSTI2025

Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity

The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (K OW ) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc) 0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗