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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
DOE OSTI2025

Resolving Lonsdaleite's decade-long controversy: Atomistic insights into a metastable diamond polymorph

Lonsdaleite, a theoretically proposed hexagonal diamond polymorph, has remained at the center of a five-decade scientific controversy since its 1967 identification. While some studies claim it exhibits superior hardness through compression-induced structural changes, others contend it is merely a stacking-faulted cubic diamond. Meteoritic samples and synthetic preparations have yielded conflicting evidence, with even advanced characterisation techniques like XRD and TEM failing to provide definitive proof. In this work, we employ first-principles density functional theory (DFT) and molecular dynamics (MD) simulations to generate unambiguous theoretical fingerprints through XRD, Raman, and SAED patterns that distinguish true Lonsdaleite from cubic diamond and its defective variants. Our atomistic approach quantifies the thermodynamic metastability of Lonsdaleite under realistic pressure-temperature conditions, reveals distinct spectral signatures through simulated Raman and resolves the structural ambiguity through generalised stacking fault energy analysis. By establishing clear criteria for definitive identification, this study provides long-awaited clarity to the Lonsdaleite debate while offering a robust computational framework for characterising metastable carbon phases in meteoritic, synthetic and industrial materials.

DFT↗
DOE OSTI2019

Development and application of a machine learning based multi-objective optimization workflow for CO 2 -EOR projects

Carbon dioxide-Enhanced Oil Recovery (CO 2 -EOR) is known as one of techniques for hydrocarbon production improvement as wells as an important candidate to reduce greenhouse gas emissions. Thus, an ideal development strategy for a CO 2 -EOR project would consider multiple objectives including to maximize oil recovery, CO 2 storage volume and project economic outcomes. This work proposes a robust computational framework that couples artificial neural network (ANN) and multi-objective optimizers to optimize the aforementioned objectives in CO 2 -EOR processes simultaneously. Expert ANN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. The robustness of the development optimization protocol is confirmed via a synthetic injection-pattern-base case study. Afterward a field implementation to Morrow-B formation to optimize the tertiary recovery stage of the field development is discussed. This work compares the optimum solution found using an aggregate objective function and the solution repository (Pareto front) generated by the multi-objective optimization process. The comparison indicates the existence of potential multi-solutions satisfying certain criteria in a CO 2 -EOR project designing, which cannot be found using traditional weighted sum method. The optimization results provide significant insight into the decision-making process of CO 2 -EOR project when multiple objective functions are considered.

02 PETROLEUM↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2014

Multiscale eddy simulation for moist atmospheric convection: Preliminary investigation

A multiscale computational framework is designed for simulating atmospheric convection and clouds. In this multiscale framework, large eddy simulation (LES) is used to model the coarse scales of 100 m and larger, and a stochastic, one-dimensional turbulence (ODT) model is used to represent the fine scales of 100 m and smaller. Coupled and evolving together, these two components provide a multiscale eddy simulation (MES). Through its fine-scale turbulence and moist thermodynamics, MES allows coarse grid cells to be partially cloudy and to encompass cloudy–clear air mixing on scales down to 1 m; in contrast, in typical LES such fine-scale processes are not represented or are parameterized using bulk deterministic closures. To illustrate MES and investigate its multiscale dynamics, a shallow cumulus cloud field is simulated. The fine-scale variability is seen to take a plausible form, with partially cloudy grid cells prominent near cloud edges and cloud top. From earlier theoretical work, this mixing of cloudy and clear air is believed to have an important impact on buoyancy. However, contrary to expectations based on earlier theoretical studies, the mean statistics of the bulk cloud field are essentially the same in MES and LES; possible reasons for this are discussed, including possible limitations in the present formulation of MES. One difference between LES and MES is seen in the coarse-scale turbulent kinetic energy, which appears to grow slowly in time due to incoherent stochastic fluctuations in the buoyancy. This and other considerations suggest the need for some type of spatial and/or temporal filtering to attenuate undersampling of the stochastic fine-scale processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Battery degradation↗
DOE OSTI2025

Seismic response of vertical dry storage casks under three-dimensional earthquake motions

Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.

Das, Tonmoy↗
DOE OSTI2023

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Using sensor arrays to decode NO x /NH 3 /C 3 H 8 gas mixtures for automotive exhaust monitoring

In this work, an array of four mixed-potential sensors is employed to identify and quantify gases in complex mixtures of unknown composition which mimic diesel engine exhaust. The sensors use dense metal and metal oxide electrodes with a porous ceramic electrolyte, yttria-stabilized zirconia (YSZ). Since the sensors exhibit cross-specificity toward target gases, we develop a computational model for predicting gas concentrations in the mixtures. Our model is based on fundamental principles of gas-sensor interactions and, furthermore, takes into account the non-linearity of the observed sensor voltage response. Our approach enables accurate predictions of gas concentrations from the voltage output of the sensor array exposed to an extensive set of mixtures involving C 3 H 8 , NH 3 , NO and NO 2 . We find that our predictions remain accurate even if the model is trained using a reduced set of mixtures, or if the number of sensors is decreased to three or two. Finally, our experimental and computational framework can be used to decipher contents of complex gas mixtures of unknown composition in numerous industrial, automotive, and national security settings.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Thermomechanical analysis and modeling of a high-temperature light trapping planar cavity solar receiver

The development of durable particle-based high-temperature solar receivers is critical for advancing concentrating solar-thermal (CST) technologies to enable high-efficiency power generation and industrial process heat. Here, this study presents a computational framework to evaluate the thermomechanical performance of a proposed enclosed light-trapping planar cavity receiver designed for particle-based thermal energy systems. The receiver incorporates absorptive cavities and fluidized particle-bed channels to enhance heat capture and reduce thermal losses. Finite element analysis (FEA) is employed to assess stress, strain, and creep-fatigue behavior under concentrated solar flux using realistic thermal boundary conditions derived from coupled system models and experimental assembly parameters. The analysis investigates the influence of particle-to-wall heat transfer coefficients (HTC) ranging from 800 to 1800 W/m 2 .K on the thermomechanical response of six candidate high-temperature alloys: Alloy 740H, Alloy 282, Alloy 617, 316H, Alloy 230, and 800H. Results show that increasing HTC reduces thermal gradients, leading to lower stresses and strains and extended minimum predicted creep life. While all materials satisfy fatigue life requirements under the investigated conditions, significant differences in creep resistance are observed. Alloy 740H consistently exhibits the longest minimum predicted creep life and the most favorable durability margins, followed by Alloy 282, with the remaining materials showing reduced creep resistance under identical loading. The reported creep lives are conservative lower-bound estimates intended for comparative material evaluation. This framework highlights the critical roles of material selection and geometry optimization in improving mechanical durability and reliability of solar-thermal receivers, forming a foundation for future experimental validation and design optimization.

14 SOLAR ENERGY↗
DOE OSTI2020

Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer

For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Lastly, codes and results are available at http://epigenomics.snu.ac.kr/PL/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A Human Life-Stage Physiologically Based Pharmacokinetic and Pharmacodynamic Model for Chlorpyrifos: Development and Validation

Sensitivity to chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to computationally predict disposition of CPF and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, age-dependent body weight was calculated from a generalized Gompertz function, and compartments (liver, brain, fat, blood, diaphragm, rapid, and slow) were scaled based on body weight from polynomial functions on a fractional body weight basis. Blood flows among compartments were calculated as a constant flow per compartment volume. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Model simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ≥ 0.55 mg/kg of chlorpyrifos (significantly higher than environmental exposure levels), 6 mo old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent oral doses of chlorpyrifos. At lower doses that are more relevant to environmental exposures, the model predicts that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict CPF disposition and biological response over various postnatal life-stages.

Smith, Jordan N.↗
DOE OSTI2017

Simulating Charge, Spin, and Orbital Ordering: Application to Jahn–Teller Distortions in Layered Transition-Metal Oxides

The degrees of freedom associated with orbital, spin, and charge ordering can signifcantly affect the properties of many crystalline solids, including battery materials, high-temperature superconductors, and naturally occurring minerals. Our research reports on the development of a computational framework to systematically explore the ordering of electronic degrees of freedom and presents results on orbital ordering associated with Jahn–Teller distortions in four layered oxides relevant for Li- and Na-ion batteries: LiNiO 2 , NaNiO 2 , LiMnO 2 , and NaMnO 2 . Our calculations reveal a criterion for the stability of orbital orderings in these layered materials: each oxygen atom must participate in two short and one long transition-metal/oxygen bond. The only orderings that satisfy this stability criterion are row orderings, such as the “zigzag” ordering. The near degeneracy of such row-orderings in LiNiO 2 suggests that boundaries between domains with distinct but symmetrically equivalent Jahn–Teller distortions will be relatively low in energy. Based on this finding, we speculate that a microstructure consisting of nanoscale Jahn–Teller domains could be responsible for the apparent absence of a collective distortion in experiments on LiNiO 2 .

25 ENERGY STORAGE↗
DOE OSTI2019

Assessing Correlations of Perovskite Catalytic Performance with Electronic Structure Descriptors

Electronic structure descriptors are computationally efficient quantities used to construct qualitative correlations for a variety of properties. In particular, the oxygen p-band center has been used to guide material discovery and fundamental understanding of an array of perovskite compounds for use in catalyzing the oxygen reduction and evolution reactions. Yet, an assessment of the effectiveness of the oxygen p-band center at predicting key measures of perovskite catalytic activity has not been made and would be highly beneficial to guide future predictions and codify best practices. In this work, we have used density functional theory at the Perdew–Burke–Ernzerhof (PBE), PBEsol, PBE + U, strongly constrained and appropriately normed functional, and Heyd–Scuseria–Ernzerhof (HSE06) levels to assess the correlations of numerous measures of catalytic performance for a series of technologically relevant perovskite oxides, using the bulk oxygen p-band center as an electronic structure descriptor. We have analyzed correlations of the calculated oxygen p-band center for all considered functionals with the experimentally measured X-ray emission spectroscopy oxygen p-band center and multiple measures of catalytic activity, including high-temperature oxygen reduction surface exchange rates, aqueous oxygen evolution current densities, and binding energies of oxygen evolution intermediate species. Our findings show that the best correlations for all measures of catalytic activity considered here are made with PBE-level calculations, with strong observed linear correlations with the bulk oxygen p-band center (R 2 = 0.81–0.87). This study illustrates that strong linear correlations between numerous important measures of catalytic activity and the oxygen p-band bulk descriptor can be obtained under a consistent computational framework, and these correlations can serve as a guide for future experiments and simulations for development of perovskite and related oxide catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Charge Transport in Solvated Donor–Acceptor Functionalized Peptoids: Molecular Dynamics and Rate Theory

Scalable solar-energy conversion requires photoactive materials that combine the efficiency of natural photosynthetic systems with the stability and processability needed for practical applications. Achieving reliable charge transport in soft, self-assembled organic materials remains challenging, as structural fluctuations and environmental effects strongly influence charge-transfer (CT) rates. Here, we present a broadly applicable computational framework for evaluating CT rates in the condensed phase, combining Fermi’s golden rule rate theory with inputs from all-atom molecular dynamics (MD) simulations and first-principles electronic-structure calculations. The approach does not rely on system-specific parametrization and is applicable to a wide range of soft and disordered materials. We demonstrate the applicability and usefulness of the framework on redox-active peptoids functionalized with iron–porphyrin (Fe–P) complexes, a bioinspired platform with programmable donor–acceptor units and tunable three-dimensional organization. The calculated CT rates exhibit strong sensitivity to molecular conformation, with variations spanning several orders of magnitude. This dependence is shown to arise from the pronounced variation in diabatic electronic coupling with the relative orientations and separations of the Fe–P complexes across the conformational ensemble. The framework provides a consistent route for connecting atomistic structure to CT kinetics in the condensed phase and enables analysis of structure–rate relations in organic semiconducting systems.

Charge transfer↗
DOE OSTI2018

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. We report the approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗