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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2025

Global Framework for Emulation of Nuclear Calculations

We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.

FOS: Computer and information sciences↗
DOE OSTI2024

Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics

Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.

Li, Jiali↗
DOE OSTI2025

Deuteration Effects on the Physical and Optoelectronic Properties of Donor–Acceptor Conjugated Polymers

The significant differences in scattering cross sections between deuterium and protium are unique to neutron scattering techniques and have been a long-standing area of interest within the neutron scattering community. Researchers have explored selective deuteration to manipulate scattering contrast in soft matter systems, leading to the widespread use of deuterium labeling in materials development. As deuteration changes the atomic mass, it alters physical properties such as molecular volume, polarizability, and polarity, which in turn may affect noncovalent interactions and crystal ordering. Despite previous studies, there remains a limited understanding of how deuteration impacts donor–acceptor (DA) conjugated polymers. To address this, we synthesized deuterated DPP polymers and systematically investigated the effects of side-chain deuteration on their thermal stability, crystal packing, morphology, and optoelectronic properties. We found that deuteration increased the melting and crystallization temperatures of DPP polymers, although it did not significantly alter their morphology, molecular packing, or charge mobility. These properties were assessed by using atomic force microscopy (AFM), X-ray scattering, and thin-film transistor device measurements, respectively, for DPP polymers. Our work shows that deuterium labeling could be a powerful method for controlling scattering length density, enabling neutrons to study the structure and dynamics of conjugated polymers without impacting their electronic performance.

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

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗
DOE OSTI2025

Self‐Healable Poly(ionic liquid) Copolymers Driven by Polar and Dipolar Forces

Abstract Commodity aliphatic and aromatic acrylic‐based copolymers self‐heal due to ubiquitouskey‐and‐lock,ring‐and‐lock, andfluorophilic‐σ‐lockvan der Waals (vdW) interactions. However, the role of these interactions in the presence of covalently copolymerized ionic liquid (IL) is not known. This study is driven by the hypothesis that covalently incorporated cation–anion pairs to form poly(ionic liquid) copolymers (PILCs) can perturb inter‐ or intra‐chain vdW interactions reflected in mechanical and electrical responses. To test this hypothesis, we synthesized a series of PILCs comprising of pentafluorostyrene (PFS) and imidazolium‐based IL monomers with variable‐length aliphatic tails (methyl and butyl). Using a combination of 2D 1 H‐ 1 H and 19 F ‐ 19 F NOESY NMR and FTIR measurements supplemented by molecular dynamic (MD) simulations, these studies demonstrate that preferentially alternating/random PILCs topologies facilitate self‐healing. The introduction of cation–anion moieties modifies thefluorophilic‐σ‐lockinteractions and, along with longer aliphatic tails ─(CH 2 ) 3 CH 3 covalently attached to the imidazolium cation, enhances cation‐anion mobility, thus faster recovery from mechanical damage occurs. These findings underline how precise control over dipolar and ionic interactions through copolymer composition enables self‐healing in PILCs. These insights may open pathways for designing sustainable, mechanically resilient materials for applications in energy storage and energy harvesting.

Chemistry↗
DOE OSTI2025

Designing Physicochemically‐Ordered Interphases for High‐Performance Composites

To enhance the mechanical properties of carbon fiber‐reinforced polymer composites, a physicochemical scaffold is designed incorporating microscopically architected chemically reactive nanofibers that act as a multiscale bridge between the carbon fibers and the matrix. Thermally activated nanofibers leverage their morphologically driven mechanochemical properties to form covalent bonds with adjacent polymer molecules, creating a co‐continuous network that dramatically enhances fiber‐matrix load transfer. By meticulously controlling the nanofiber architecture through variable surface area, functional group availability, and polymer chain alignment effects, the extent of covalent bonding between nanofibers and the matrix is manipulated ultimately resulting in improved carbon fiber‐matrix adhesion. Further, the concept was validated using polyacrylonitrile nanofibers within an acrylonitrile butadiene styrene matrix in a discontinuous carbon fiber‐reinforced composite system. Nanomechanical studies using atomic force microscopy and low‐field nuclear magnetic resonance spectroscopy confirmed immobilized, chemically transferred, and ordered nanostructures at the interphase. The resulting composites demonstrated ≈56% and ≈175% improvements in tensile strength and toughness, respectively, compared to composites without nanofiber. Comprehensive thermal, rheological, and X‐ray scattering analyses, along side all‐atomic molecular dynamics simulations, revealed the fundamental mechanisms behind these improvements in mechanical behavior. The versatility and efficacy of the approach have the potential to address longstanding interphase challenges in the composite industry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dynamics of Downdrafts Around a Growing Convective Cloud: A Numerical Study

We examine the dynamics of cloud-edge downdrafts over the growth phase of isolated cumuli, combining Eulerian and Lagrangian analyses. As in previous studies, our results show that growing cumuli are surrounded by downdrafts linked to cloud-scale quasi-toroidal circulations at all times at middle and upper cloud levels consistent with the thermal chain description of convective clouds. These toroidal circulations are responsible for the most intense cloud-edge downdrafts in our simulations. In the upper cloud half, roughly 30%–50% of the upward mass flux is typically compensated within a radius of about twice the updraft radius in quasi-laminar simulations forced by a warm bubble in an initially quiescent flow. In a turbulent cloud forced by surface fluxes, this compensation fraction is around 10%–30% over the same region. In contrast to the buoyancy-centered view of subsiding shells, Eulerian and Lagrangian vertical momentum budget analyses show that the most intense cloud-edge downdrafts in the turbulent setup, and after spin-up of the toroidal circulation in the quasi-laminar experiments, are predominantly mechanically forced (i.e., driven by dynamic pressure accelerations). This is consistent throughout the entire growth phase of the cumulus clouds and across tests with varying assumptions, including drier and moister environments. Despite dynamic pressure perturbations being the main driver of toroidal downdrafts, the downdraft speed (relative to the corresponding updraft velocity) exceeds the prediction of the non-buoyant Hill's spherical vortex—a simple model frequently used for cloud-scale circulations—by more than 30%.

Pardo, Lianet Hernández [Goethe Univ., Frankfurt (↗
DOE OSTI2024

Roles of non-axisymmetric perturbations in free drift vertical displacement events on EAST

Abstract The safe operation of most tokamaks, especially the large ones, relies on the feedback control of vertical displacement events (VDEs). However, most of these feedback control systems are based on axisymmetric VDE models. In this study, we use NIMROD simulations to study the role of non-axisymmetric perturbations in free drift vertical displacement events on EAST. The high- n modes in the non-axisymmetric VDE grow first, which drives the formation of high- n magnetic island chains. Subsequently, the magnetic island chains grow and overlap with each other, leading to the destruction of the magnetic flux surface, which induces a minor disruption and accelerates the start of the following major disruption. The magnetic island and the stochastic magnetic field allow the toroidally non-axisymmetric poloidal plasma current to jet towards the hoop force direction, forming finger-like and filamentary structures. Such a plasma current non-axisymmetry strongly depends on the anisotropy in the thermal transport coefficients.

Physics↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mechanisms of direct and converse piezoelectricity in ferroelectric polymers

Within the linear regime of mechanical and electrical responses, it is commonly accepted that direct and converse piezoelectric coefficients should be the same. However, we observed a consistently higher converse d 31 (∼54 pm/V) than the direct d 31 (∼42 pC/N) for a quenched, stretched, annealed, and electrically poled poly(vinylidene fluoride-co-trifluorethylene) [P(VDF-TrFE)] 52/48 mol.% sample (abbreviated as coP-52/48QSAP). On the contrary, the direct and converse d 31 values were the same for coP-65/35QSAP and coP-55/45QSAP. Small-angle X-ray scattering results showed that coP-52/48QSAP had a higher amount of relaxor-like secondary crystals (SCs) in the oriented amorphous fraction (OAF) (SC OAF ) than coP-55/45QSAP and coP-65/35QSAP. To explain the experimental observation, we performed molecular dynamics (MD) simulation of the pure PVDF (without TrFE) to estimate direct and converse piezoelectricity for the PVDF OAF. Based on the MD simulation, the direct d 31 had a plateau value around 350 pC/N for the transverse (i.e., along the chain direction) strain up to 1 %, whereas the simulated converse d 31 could be lower (for electric field E < 0.8 MV/m), equal (for E = 0.8 MV/m), or higher (for E > 0.8 MV/m) than the direct d 31 , depending on the poling electric field. From the MD simulation, both mechano-electrostriction and electrostatic interaction were identified in the OAF as the driving force for enhanced piezoelectricity in ferroelectric PVDF. In conclusion, when ferroelectric domains were formed in the OAF by electric poling, the simulated converse d 31 became higher than the direct d 31 . Combining both experimental and MD simulation results, the higher converse d 31 than direct d 31 for coP-52/48QSAP was understood qualitatively.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chain Flexibility and Structure of a Polyimide Copolymer: Revisiting the Freely Rotating Chain Model

Poly(4–4′-oxydiphenylene-pyromellitimide)-based polyimides─trade name Kapton─have wide-ranging engineering applications owing to their thermal and mechanical stability, but little is known about underlying chain-level characteristics. While theoretical models have conceptualized Kapton as inflexible polycyclic rods separated by freely rotating diphenyl ether hinge groups, the model’s core predictions remain untested and subtleties of the relaxation behavior are missed, which atomistic modeling can resolve. To these ends, we examine all-atom Kapton structures in crystalline and glassy amorphous configurations using a DFT-validated class II force field. Constructing amorphous configurations is challenging, as the fused-ring-containing backbone has slow relaxation dynamics and scaling suggestive of entanglements even in oligomers. In conclusion, we find larger backbone rearrangements of the linear polycyclic segments about ether groups that are consistent with the rod-hinge picture on the monomer scale, whereas a ring rotation analysis suggests partially flexible rod-like segments and involves multiple facile rotational relaxation modes.

Liesen, Nicholas T. [Lawrence Livermore National L↗
DOE OSTI2025

Microscopic insights into the solvation of polyethylene glycol chains in water: A machine learning potential approach

Polyethylene glycol (PEG) is a structurally simple, nontoxic, and water-soluble polymer widely utilized in medical and pharmaceutical applications. Notably, when a PEG chain is immersed in water, the surrounding water molecules play a key role in driving conformational changes of this macromolecule. In this study, we explore the solvation behavior of PEG under mechanical strain using molecular dynamics simulations, with an interatomic potential obtained from machine learning. Our focus is on the transition from the favored coil-like conformation to an extended one under external force. Through analyses of radial distribution functions, hydrogen bonding, and solvation dynamics, we uncover how mechanical stretching influences the local hydration environment. Furthermore, we disentangle the enthalpic and entropic contributions to the conformational stability of PEG in water. Surprisingly, our neural network potential model identifies dewetting of PEG C-atoms, and not water H-bonding with PEG O-atoms, as the main enthalpic driving force for the coiling of PEG in water.

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

Watching for light: An enterprise roadmap for trustworthy laser threat warning (LTW) to protect national assets

Comprehensive space force protection must include effective and trustworthy laser threat warning (LTW). Effective LTW will detect and characterize threats to space assets and thus enhance space deterrence. LTW must be trustworthy: able to categorize threats and non-threats by being both sensitive to true events and resistant to false alarms. Outside of the laboratory, the statistics and even the roles of lasers become unclear. In the chain of events leading to an attack, the laser may be the last link to be understood. Human situational awareness and informal reasoning must blend statistics with circumstantial evidence to visualize these chains before they are clear. This paper sets out an industrial model for an enterprise that will routinely produce trustworthy LTW. By incorporating psychology and economics, this enterprise can overcome the difficulties and perils of cooperation in networked defense and intelligence. This roadmap suggests how the enterprise can incentivize distracted actors with different goals to share what they know and coordinate what they do.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

phosaa14SB and phosaa19SB: Updated Amber Force Field Parameters for Phosphorylated Amino Acids

Phosphorylated amino acids are involved in many cell regulatory networks; proteins containing these post-translational modifications are widely studied both experimentally and computationally. Simulations are used to investigate a wide range of structural and dynamic properties of biomolecules, such as ligand binding, enzyme-reaction mechanisms, and protein folding. However, the development of force field parameters for the simulation of proteins containing phosphorylated amino acids using the Amber program has not kept pace with the development of parameters for standard amino acids, and it is challenging to model these modified amino acids with accuracy comparable to proteins containing only standard amino acids. In particular, the popular ff14SB and ff19SB models do not contain parameters for phosphorylated amino acids. Here, the dihedral parameters for the side chains of the most common phosphorylated amino acids are trained against reference data from QM calculations adopting the ff14SB approach, followed by validation against experimental data. Finally, library files and corresponding parameter files are provided, with versions that are compatible with both ff14SB and ff19SB.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

The effect of stretching on polyamide – 6: assessment of crystallinity, mechanical properties, and surface roughness

Polyamide-6 (PA6), or nylon-6, is a widely used low-cost polymer material for various applications. Manufacturing PA6 into fibers involves a spinning process such as melt extrusion, which orients the polymer chains into a semicrystalline structure. When stretching the produced fibers to high strains, the PA6 fibers transition the metastable gamma phase back into the stable gamma phase. The effects of these large stretches on mechanical properties have not been explored after the stretch has been released. In this study, the effects on mechanical properties were studied by stretching the PA6 fibers from 10% to 50% tensile strain and subsequently releasing the fibers. The stretched fibers were characterized by X-ray diffraction and atomic force microscopy to determine the changes to the crystal structure and surface roughness. The stretched fibers were then tensile tested to failure and the tensile properties were examined. The stress-strain curves on the post-stretched PA6 fibers showed clear softening in the fibers with lower initial moduli after 30% stretching. Furthermore, these results may hold a clue to softening of fibers in applications with repeated loading and have implications for mechanical stability and fatigue of this polymeric material.

36 MATERIALS SCIENCE↗