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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MXenoids: Generalization of MXene-Inspired Covalent Surface Modifications Across Two-Dimensional Materials

The ability to perform versatile covalent surface modifications in two-dimensional (2D) inorganic materials marks a significant advance in the functionalization of this broad family of materials. One particularly successful example of 2D materials with chemically modifiable surfaces are 2D transition metal carbides and nitrides (MXenes). MXenes' strong in-plane metal-carbon bonds and labile surface metal-halide bonds create altogether unprecedented opportunities for versatile postsynthetic modifications and assembling complex materials, including various organic-inorganic hybrids. Here, we demonstrate the general applicability of this surface modification strategy to non-MXene halide-terminated 2D materials, termed MXenoids. These surface modifications enable compositional and electronic structure engineering, introduce chiral hybrid organic-inorganic structures, and photoluminescence ranging from near-IR to blue. This study highlights the avenue of surface chemistry-driven materials design, enhancing the functional capabilities of 2D materials.

Zhou, Chenkun [University of Chicago, IL (United S↗
DOE OSTI2025

Cracking in polymer substrates for flexible electronic devices and its mitigation

Mechanical reliability plays a critical role in determining the durability of flexible electronic devices because of the significant mechanical stresses they experience during manufacturing and operation. Many such devices are built on sheets comprising stiff transparent-conducting oxide (TCO) electrode films on compliant polymer substrates, and it is generally assumed that the high-toughness polymer substrates do not crack. Contrary to this assumption, here we show extensive cracking in the polymer substrates during bending of a variety of TCO/polymer sheets, and a device example — flexible perovskite solar cells. Such substrate cracking, which compromises the overall mechanical integrity of the entire device, is driven by the amplified stress-intensity factor caused by the elastic mismatch at the film/substrate interface. To mitigate this substrate cracking, an interlayer-engineering approach is designed and experimentally demonstrated. This approach is potentially applicable to myriad flexible electronic devices, with stiff films on compliant substrates, for improving their durability and reliability.

42 ENGINEERING↗
DOE OSTI2025

Pulse: An Outlier Sensitive Downsampling Algorithm For Timeseries Data

Pulse is a downsampling algorithm for timeseries data. Frequently datasets become so large that visualization tools and web browsers cannot effectively render graphics due to memory constraints. Downsampling algorithms are commonly applied to minimize the quantity of data required to visualize important features or trends in the data, but some datasets are composed by distinct enough features and trends that most existing downsampling algorithms fail to preserve them. Pule was developed to downsample timeseries data for galvanostatic stack test data at the Idaho National Laboratory. These datasets were composed by approximately 4 million records, most of them being extremely uniform. However, during relatively brief time periods when the stack test changes state, for example when the test article is powered on, or a load is added, the data produce sparse asymptotes. No existing downsampling algorithm was capable of preserving the sparse asymptotes in electrolysis stack test data. Instead, we develop a downsampling algorithm that preserves important outliers in data, and otherwise aggressively downsamples uniform data. The algorithm has applications in other domains like seismology, in the measurement of earthquakes, or astronomy, in the measurement of quasars or transit photometry.

Woodruff, Nathan [Idaho National Laboratory (INL),↗
DOE OSTI2024

Abrasive Waterjet Machining

The abrasive waterjet machining process was introduced in the 1980s as a new cutting tool; the process has the ability to cut almost any material. Currently, the AWJ process is used in many world-class factories, producing parts for use in daily life. A description of this process and its influencing parameters are first presented in this paper, along with process models for the AWJ tool itself and also for the jet–material interaction. The AWJ material removal process occurs through the high-velocity impact of abrasive particles, whose tips micromachine the material at the microscopic scale, with no thermal or mechanical adverse effects. The macro-characteristics of the cut surface, such as its taper, trailback, and waviness, are discussed, along with methods of improving the geometrical accuracy of the cut parts using these attributes. For example, dynamic angular compensation is used to correct for the taper and undercut in shape cutting. The surface finish is controlled by the cutting speed, hydraulic, and abrasive parameters using software and process models built into the controllers of CNC machines. In addition to shape cutting, edge trimming is presented, with a focus on the carbon fiber composites used in aircraft and automotive structures, where special AWJ tools and manipulators are used. Examples of the precision cutting of microelectronic and solar cell parts are discussed to describe the special techniques that are used, such as machine vision and vacuum-assist, which have been found to be essential to the integrity and accuracy of cut parts. The use of the AWJ machining process was extended to other applications, such as drilling, boring, milling, turning, and surface modification, which are presented in this paper as actual industrial applications. To demonstrate the versatility of the AWJ machining process, the data in this paper were selected to cover a wide range of materials, such as metal, glass, composites, and ceramics, and also a wide range of thicknesses, from 1 mm to 600 mm. The trends of Industry 4.0 and 5.0, AI, and IoT are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Scalable Interior‐Point Gauss–Newton Method for PDE‐Constrained Optimization With Bound Constraints

Here, we present a scalable approach to solve a class of partial differential equation (PDE)‐constrained optimization problems with bound constraints. This approach utilizes a robust full‐space interior‐point (IP)‐Gauss–Newton optimization method. To cope with the poorly‐conditioned IP‐Gauss–Newton saddle‐point linear systems that need to be solved approximately, once per optimization step, we propose two spectrally related preconditioners. These preconditioners leverage the limited informativeness of data in regularized PDE‐constrained optimization problems. A block Gauss–Seidel preconditioner is proposed for the GMRES‐based solution of the IP‐Gauss–Newton linear systems. It is shown, for a large‐class of PDE‐ and bound‐constrained optimization problems, that the spectrum of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix is asymptotically independent of discretization and is not impacted by the ill‐conditioning that notoriously plagues interior‐point methods. We exploit symmetry of the IP‐Gauss–Newton linear systems and propose a regularization and log‐barrier Hessian preconditioner for the preconditioned conjugate gradient (PCG)‐based solution of the equivalent IP‐Gauss–Newton–Schur complement linear systems. The eigenvalues of the block Gauss–Seidel preconditioned IP‐Gauss–Newton matrix, that are not equal to one, are identical to the eigenvalues of the regularization and log‐barrier Hessian preconditioned Schur complement matrix. The scalability of the approach is demonstrated on two example problems. The numerical solution of these optimization problems is shown to require a discretization independent number of IP‐Gauss–Newton linear solves. Furthermore, the linear systems are solved in a discretization and IP ill‐conditioning independent number of preconditioned Krylov subspace iterations. The parallel scalability of the preconditioner, achieved via algebraic multigrid component solvers when applicable, and the aforementioned algorithmic scalability permits a parallel scalable means to compute solutions of a large class of PDE‐ and bound‐constrained problems.

PDE-constrained optimization↗
DOE OSTI2025

Engineering surface charges of nanofiltration membranes to maximize Li + /Mg 2+ separation properties

Polyamide-based nanofiltration (NF) membranes are attractive for Li + /Mg 2+ separation for lithium recovery from salt brine (containing mainly Mg 2+ ). However, Li + and Mg 2+ have similar hydration radii, resulting in a low separation factor (SF Li/Mg ). Herein, we demonstrate that SF Li/Mg can be significantly enhanced by optimizing the membrane surface positive charges. This results in an unexpected maximum SF Li/Mg at a solution pH slightly below its isoelectric point (IEP). Specifically, NF270 membrane was surface-grafted with 2-(methacryloyloxy)ethyltrimethylammonium chloride (META) or polyethylenimine (PEI) using bio-adhesive dopamine, forming a thin, stable, charged layer (20–40 nm) on the surface. The effects of solution pH and surface modification on the surface zeta potential (ZP) and single- and mixed-salt Li + /Mg 2+ separation properties are thoroughly investigated. For example, the META grafting increases the ZP from 9.2 to 16 mV and SF Li/Mg by 130 % from 35 to 80 at pH 4, superior to the state-of-the-art commercial NF membranes. Furthermore, this surface modification occurs at ≈22 °C in aqueous solutions and can be utilized to enhance commercial modules for practical applications.

Li+/Mg2+ separation↗
DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2025

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗
DOE OSTI2025

Leveraging Intramolecular π-Stacking to Access an Exceptionally Long-Lived 3 MC Excited State in an Fe(II) Carbene Complex

The ability to manipulate excited-state decay cascades using molecular structure is essential to the application of abundant-metal photosensitizers and chromophores. Ligand design has yielded some spectacular results elongating charge-transfer excited state lifetimes of Fe(II) coordination complexes, but triplet metal-centered ( 3 MC) excited states - recently demonstrated to be critical to the photoactivity of isoelectronic Co(III) polypyridyls - have to date remained elusive, with temporally isolable examples limited to the picosecond regime. Here, with this report, we show how strong-field donors and intramolecular π-stacking can conspire to stabilize a long-lived 3 MC excited state for a remarkable 4.1 ± 0.3 ns in fluid solution at ambient temperature. Analysis of variable-temperature time-resolved absorption data using theoretical models ranging from Arrhenius to semiclassical Marcus theory, combined with computational modeling and X-ray crystallography, reveal a Jahn−Teller stabilized excited state with a high activation barrier for ground-state recovery. The net result is a chromophore with a 3 MC excited-state lifetime that is orders of magnitude longer than anything yet observed for an Fe(II) complex.

carbene compounds↗
DOE OSTI2025

Duality defect in a deformed transverse-field Ising model

Physical quantities with long lifetimes have both theoretical significance in the study of quantum many-body systems and practical implications for quantum technologies. In this manuscript, we investigate the roles played by topological defects in the construction of quasiconserved quantities, using as a prototypical example the Kramers-Wannier duality defect in a deformed one-dimensional quantum transverse-field Ising model. We construct the duality defect Hamiltonian in three different ways: half-chain Kramers-Wannier transformation, utilization of techniques in the Ising fusion category, and defect-modified weak integrability breaking deformation. The third method is also applicable for the study of generic integrable defects under weak integrability breaking deformations. We also work out the deformation of defect-modified higher charges in the model and study their slower decay behavior. Furthermore, we consider the corresponding duality defect twisted deformed Floquet transverse-field Ising model and investigate the stability of the isolated zero mode associated with the duality defect in the integrable Floquet Ising model, under such weak integrability breaking deformation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Evaluation of Hydrogen Storage Quantity Limits for Safety Requirements

As hydrogen storage facilities increase in size and capacity, it may be necessary to evaluate codes and standards that regulate hydrogen, especially in relation to allowable aggregate quantities. Existing regulations for other substances with comparable hazards such as oxygen, natural gas, and petroleum were reviewed; most fuels do not have aggregate quantity limits despite sometimes having individual tank capacity restrictions or limits for certain non-industrial, indoor, or small-scale applications. This precedent suggests that specifying a hydrogen quantity limit may not be necessary. Several possible methods for identifying an appropriate limit are presented to illustrate different approaches for a limit basis. These methods are based on overall risk, or the specific consequences inflicted by a jet fire or an explosion on people and infrastructure. However, these example metrics tend to require assumptions about potential leak sizes, suggesting that a general aggregate quantity limit would be difficult to justify without more system-specific requirements.

08 HYDROGEN↗
DOE OSTI2025

Theory and numerics of subspace approximation of eigenvalue problems

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to mitigate the computational burden associated with high-fidelity systems. Furthermore, we provide general error estimates under non-simple eigenvalue conditions, establishing some theoretical foundations for understanding the convergence behavior of subspace approximations. Numerical examples, including problems with one-dimensional to three-dimensional spatial domain and one-dimensional to two-dimensional parameter domain, are presented to demonstrate the efficacy of reduced basis method in handling parametric variations in boundary conditions and coefficient fields to achieve significant computational savings while maintaining high accuracy, making them promising tools for practical applications in large-scale eigenvalue computations.

Eigenvalue problems↗
DOE OSTI2024

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. We show an example where, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO 2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.

58 GEOSCIENCES↗
DOE OSTI2026

Comparative mitogenomics of kingdom Fungi – evolutionary insights and metagenomic applications

Mitochondria are essential components of eukaryotic cells, responsible for ATP production through oxidative phosphorylation. Despite their biological importance, unique challenges have hindered the adoption of automated mitochondrial genome (mitogenome) annotation methods, obstructing mitochondrial comparative genomics in a broad evolutionary context. Using Fungi as a study system and a Joint Genome Institute (JGI) annotated high-quality reference set, we observed broad patterns of mitochondrial evolution across the kingdom. We found that the median fungal mitogenome size is 58 kb and identified exceptionally large examples over 1 Mb in Pezizomycetes. All 14 expected oxidative phosphorylation protein-coding genes, plus rps3, were generally conserved. We found evidence of major evolutionary transitions within the Ascomycota, including the transfer of mitochondrially encoded atp8 and atp9 to the nuclear genomes across the Pezizomycotina and shifts in mitogenome tRNA patterns across the kingdom. We found substantial concordance between mitochondrial and nuclear evolution, enabling us to document 3131 total fungal mitogenomes from JGI-derived metagenomic datasets. We also identified 6467 total undeclared mitogenomes embedded in Genbank fungal nuclear assemblies. We provide interactive tools for mitogenome analysis through the JGI MycoCosm platform. Collectively, this work generated nearly 10 000 new fungal mitogenome annotations, providing a foundation and resources for future exploration of comparative fungal mitogenomics.

Ahrendt, Steven R. [USDOE Joint Genome Institute (↗
DOE OSTI2025

A snapshot of high-entropy alloy processing techniques and their effects on resulting mechanical properties

High-entropy alloys (HEAs) exhibit exceptional strength, corrosion resistance, and thermal stability, making them promising candidates for nuclear, aerospace, and other extreme applications. While most prior work has focused on compositional design, manufacturing techniques themselves can alter microstructure and mechanical properties as dramatically as alloy chemistry. This review compiles and compares the effects of processing routes—including arc melting, induction melting, mechanical alloying with spark plasma sintering, and additive manufacturing—on the structure and properties of HEAs. Quantitative comparisons highlight, for example, that SPS-processed alloys can achieve ∼20–45 % higher yield and tensile strength than arc-melted counterparts, while Bridgman solidification produces nearly single-crystal structures with elongation to failure exceeding 80 %. Additive manufacturing routes such as selective laser melting offer fine microstructures but also introduce anisotropy and porosity, leading to yield strengths spanning 100–600 MPa for the same composition. By synthesizing such results, this review provides actionable insight into how processing routes interact with HEA core effects (high entropy, lattice distortion, sluggish diffusion, and cocktail effect) to determine performance, thereby offering a practical guide for optimizing manufacturing strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reassessing early-age strength development of high-volume fly ash concretes for precast buildings

Increasing beneficial use of fresh or landfilled fly ash as a replacement for Portland cement can be more challenging for the construction of precast buildings or similar applications requiring rapid strength development. Therefore, the framework presented in this paper aims to reassess high-volume fly ash concretes but in the context of facilitating more sustainable precast buildings. More specifically, the framework was used to characterize strength development of concrete mixes with a target minimum 24-hour compressive strength of 24.1 MPa (3500 psi), selected as an example strength development metric to demonstrate the framework, and comprised of 40% Class C, Class F, and landfilled (harvested) fly ash – as a high-volume replacement of Type III or Type IL cement. High-early strength was driven by optimized dosages of commercial grade gypsum and accelerating admixtures, in addition to optimal aggregate packing and mix proportioning strategies. Early-age mechanical properties including compressive strength, modulus of rupture, and modulus of elasticity were reevaluated within 24 hours of batching with respect to common precast production demands. Simple data analyses were then used to highlight cases where currently accepted design provisions for the aforementioned properties are either overly-conservative or unconservative with respect to test data. Furthermore, the framework and demonstration of example mixes presented herein aim to promote confidence for using larger fractions of fresh or landfilled fly ashes for precast buildings to further enhance environmental benefits without sacrificing pertinent early-age structural performance.

42 ENGINEERING↗
DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗
DOE OSTI2026

FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]

The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.

Sirkis, Leah [National Laboratory of the Rockies (↗