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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Catalytically Active Site Mapping Realized through Energy Transfer Modeling

Abstract The demands of a sustainable chemical industry are a driving force for the development of heterogeneous catalytic platforms exhibiting facile catalyst recovery, recycling, and resilience to diverse reaction conditions. Homogeneous‐to‐heterogeneous catalyst transitions can be realized through the integration of efficient homogeneous catalysts within porous matrices. Herein, we offer a versatile approach to understanding how guest distribution and evolution impact the catalytic performance of heterogeneous host–guest catalytic platforms by implementing the resonance energy transfer (RET) concept using fluorescent model systems mimicking the steric constraints of targeted catalysts. Using the RET‐based methodology, we mapped condition‐dependent guest (re)distribution within a porous support on the example of modular matrices such as metal–organic frameworks (MOFs). Furthermore, we correlate RET results performed on the model systems with the catalytic performance of two MOF‐encapsulated catalysts used to promote CO 2 hydrogenation and ring‐closing metathesis. Guests are incorporated using aperture‐opening encapsulation, and catalyst redistribution is not observed under practical reaction conditions, showcasing a pathway to advance catalyst recyclability in the case of host–guest platforms. These studies represent the first generalizable approach for mapping the guest distribution in heterogeneous host–guest catalytic systems, providing a foundation for predicting and tailoring the performance of catalysts integrated into various porous supports.

Thompson, William J.↗
DOE OSTI2025

Scalable Hyperpolarized MRI Enabled by Ace‐SABRE of [1‐ 13 C]Pyruvate

Abstract Hyperpolarized (HP) MRI using [1– 13 C]pyruvate is emerging as a promising molecular imaging approach. Among hyperpolarization methods, Signal Amplification By Reversible Exchange (SABRE) is attractive because SABRE polarizes the substrates directly in room‐temperature solutions avoiding complex hardware. Most SABRE experiments have historically been performed in methanol, a relatively toxic and difficult‐to‐remove solvent. Here we demonstrate the use of a 80/20 acetone/water (A/W) solvent system (Ace‐SABRE) to provide hyperpolarized [1– 13 C]pyruvate with up to 17% polarization, then implement a solvent processing protocol to achieve injectable solutions retaining 74% of the initial polarization, and lastly we demonstrate HP in vivo spectroscopy and imaging using the Ace‐SABRE platform to showcase metabolic tracking in a hepatocellular carcinoma (HCC) tumor as well as HP‐MRI, both in direct comparison to dissolution dynamic nuclear polarization (d‐DNP) experiments. The Ace‐SABRE technique promises faster adoption of SABRE hyperpolarization in biological experiments, overall lowering the barriers to entry for HP‐NMR and HP‐MRI.

Chemistry↗
DOE OSTI2025

High‐Performance Al–S Batteries by Spin Polarization Modulation via Catalytic Ni‐MoS 2 Nanosheets

Aluminum–sulfur (Al–S) batteries catalysts with adsorption and catalytic capabilities can effectively improve the slow redox kinetics, but the current research often ignores the effect of optimizing the electronic structure of the catalyst on improving charge transfer and adsorption. Here, in this study, Ni-doped monolayer MoS 2 nanosheets are synthesized and used as a catalytic additive for the sulfur cathode. The addition of Ni promotes spin splitting of 4d orbital of Mo, thereby affecting polarization degree of the basal plane sulfur and making it change from a low spin state to a high spin one. This high spin configuration raises the electron energy level and provides an active electron state to react with aluminum polysulfides (AlPSs), which optimizes the adsorption energy. At the same time, it accelerates electron transfer and lowers the energy barrier for the overall conversion of the polysulfides. Benefiting from these features, Al–S batteries based on rationally designed S@Ni-MoS 2 /C cathodes exhibit a high initial capacity (1603.0 mAh g −1 at 0.5 A g −1 ) and extraordinary cycling stability (0.035% capacity decay rate during 2000 cycles). This study showcases a spin-polarized electronic structure control strategy to enhance catalytic activity, providing a viable approach for developing efficient catalysts for practical Al–S batteries.

Aluminum-sulfur batteries↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Biocatalytic Carboxylic Acid Reduction and Transamination in Cell‐Free Lysates at High Substrate Loading

Chemoselective reduction of stable carboxylic acids to reactive aldehydes is of interest across many industries. While carboxylic acid reductases (CARs) are promising biocatalysts for this chemistry, poor chemoselectivity and low yield are commonly obtained when using less expensive crude lysate preparations and prerequisite ATP and NADPH regeneration systems. Here, in this work, we developed a highly chemoselective multienzyme cascade featuring a CAR and an ω-transaminase (TA) in crude lysate format, with conversion of the dicarboxylic acid terephthalic acid (TPA) into the diamine para -xylylenediamine (pXDA) as the model chemistry. We improved chemoselectivity for pXDA using engineered aldehyde-stabilizing Escherichia coli strains, though desired product yields remained modest. We next found that CAR activity was limited at high substrate loadings and overcame this bottleneck by modulating the ratio of polyphosphate (polyP 6 ) to Mg 2+ , enabling volumetric scaling and increased substrate loading up to 50 mM TPA. We then showcased the portability of this platform across substrates, resulting in the synthesis of four other high-value amines from carboxylate precursors. The combination of high carboxyl group turnover, up to 93.5 mM under the tested conditions, and the simplicity of crude enzyme preparation is a promising platform for sustainable functional group interconversion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Morphology‐controlled synthesis of multi‐metal‐based spinel oxide nanocatalysts and their performance for oxygen reduction

Abstract We present a one‐pot colloidal synthesis method for producing monodisperse multi‐metal (Co, Mn, and Fe) spinel nanocrystals (NCs), including nanocubes, nano‐octahedra, and concave nanocubes. This study explores the mechanism of morphology control, showcasing the pivotal roles of metal precursors and capping ligands in determining the exposed crystal planes on the NC surface. The cubic spinel NCs, terminated with exclusive {100}‐facets, demonstrate superior electrocatalytic activity for the oxygen reduction reaction (ORR) in alkaline media compared to their octahedral and concave cubic counterparts. Specifically, at 0.85 V, (CoMn)Fe 2 O 4 spinel oxide nanocubes achieve a high mass activity of 23.9 A/g and exhibit excellent stability, highlighting the promising ORR performance associated with {100}‐facets of multi‐metal spinel oxides over other low‐index and high‐index facets. Motivated by exploring the correlation between ORR performance and surface atom arrangement (active sites), surface element composition, as well as other factors, this study introduces a prospective approach for shape‐controlled synthesis of advanced spinel oxide NCs. It underscores the significance of catalyst shape control and suggests potential applications as nonprecious metal ORR electrocatalysts.

Li, Can↗
DOE OSTI2026

The 2025 “Hacking Limnology” Workshop Series and DSOS Virtual Summit: A Half Decade of Data‐Intensive Aquatic Science

The 5th Aquatic Ecosystem MOdeling Network—Junior (AEMON-J) “Hacking Limnology” Workshop and 6th Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) convened 21–25 July 2025. As in previous years (Fig. 1; Meyer and Zwart 2020; Meyer et al. 2021b, 2021c, 2022, 2024), the virtual workshops and summit were free of charge, the content was formatted to allow for broad engagement from a globally distributed audience, and workshop materials and recordings were made available on the AEMON-J/DSOS archive (Meyer et al. 2021a). In contrast to previous years, which primarily focused on inland aquatic ecosystems, this year's workshops and summit showcased a notable plurality of ecosystem types, with workshops spanning marine, riverine, and lacustrine environments. The weeklong event brought together researchers and practitioners interested in the nexus of data science, open science, and the aquatic sciences, hosting between 47 and 65 attendees at a single time and a higher number of registrants (n = 389), who might opt to access the material asynchronously.

Meyer, Michael F. [US Geological Survey, Portland,↗
DOE OSTI2026

Synergistic Alignment of Low Aspect‐Ratio π‐Conjugated Molecules Enables Exceptional UV–vis–NIR Polarization Detection

Abstract Polarization detection enhances signal contrast and is widely utilized in diverse advanced applications. An ongoing challenge is the development of high‐performance polarization‐sensitive photodetectors based on optically anisotropic organic semiconductors, particularly in the near‐infrared (NIR) region. While uniaxially aligned π‐conjugated polymers with high aspect ratios exhibit strong linear dichroism and have shown promise, their limited NIR performance and heavy reliance on polymer material now represent critical limitations. Here, a breakthrough is reported in achieving giant linear dichroism and exceptional polarization detection with low aspect‐ratios (AR) non‐fullerene small‐molecule (NFSM) acceptors, extending polarization sensitivity from the UV–vis to the NIR range. An impressive dichroic ratio of 27.1 at 605 nm and 12.0 at 780 nm is demonstrated. The maximum polarization photocurrent ratio is 11.2 at 780 nm under parallel versus perpendicular polarized light. This unprecedented performance originates from synergistic molecular alignment, wherein NFSMs significantly enhance the uniaxial orientation of both the polymer matrix and the NFSMs themselves during self‐assembly and thermal annealing. Besides, such a linear‐polarization‐sensitive photodetectors (LPS‐PDs) are showcased in generating degree‐of‐linear‐polarization imaging. The work establishes NFSMs as a viable material system for next‐generation of organic LPS‐PDs and provides fundamental insights into structural origins of polarization sensitivity in low AR organic semiconductors.

Xue, Yingying↗
DOE OSTI2025

What Is a Polyolefin? A Critical Overview of Ethylene Copolymers Used as Solar Photovoltaic Module Encapsulants

In recent years, photovoltaic (PV) encapsulant films marketed as polyolefins (POs), more specifically as PO elastomers (POEs) and thermoplastic POs (TPOs), have gained significant market share and are projected to become the dominant encapsulation films by 2030. Relative to other industries, there are significant misconceptions about the term PO in the PV industry. Both in the scientific literature as well as in sales and advertising, the terms PO, POE, and TPO are often misused to describe the same type of material with comparable properties, while in reality these may each consist of separate material classes. This paper provides a comprehensive literature and market review, to showcase a broad range of PO and other ethylene copolymer encapsulants from recent studies, and discusses the materials' properties to clarify what constitutes a “polyolefin.” In addition, to promote a clearer comparison of encapsulant properties, we propose a two‐dimensional taxonomy to categorize polymers used in module manufacturing, including POs. In terms of improving the reliability of solar PV modules, PO‐based encapsulants have several advantages (including lower water uptake and ion diffusion), but might come with disadvantages too, such as a more complex processing and a higher sensitivity to the storage conditions and shelf life. All this might prospectively impact adhesion properties of the encapsulant to other materials' interfaces (glass, cells etc.) and end‐product quality. Because the track record of field‐deployed PV modules containing PO encapsulants is also limited, we hope to contribute to better material understanding and precision in communication in PV to secure quality.

14 SOLAR ENERGY↗
DOE OSTI2026

Analyzing the Free States of one Quantum Resource Theory as Resource States of Another

In the context of quantum resource theories (QRTs), free states are defined as those that can be obtained at no cost under a certain restricted set of conditions. However, when taking a free state from one QRT and evaluating it through the optics of another QRT, it might well turn out that the state is now extremely resourceful. Such realization has recently prompted numerous works characterizing states across several QRTs. Here, in this work, we contribute to this body of knowledge by analyzing the resourcefulness in free states for—and across witnesses of—the QRTs of multipartite entanglement, fermionic non-Gaussianity, imaginarity, realness, spin coherence, Clifford non-stabilizerness, $S_n$-equivariance, and non-uniform entanglement. We provide rigorous theoretical results as well as present numerical studies that showcase the rich and complex behavior that arises in this type of cross-examination.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗
DOE OSTI2023

Polycarbonate‐Based Solid‐Polymer Electrolytes for Solid‐State Sodium Batteries

Solid-polymer electrolytes comprised of polypropylene carbonate (PPC) and varied sodium bis(fluorosulfonyl)imide (NaFSI) salt concentrations are investigated for implementation as a conductive solid polymer electrolyte into solid-state cathode composites utilizing a sodium-layered oxide active material. The ionic conductivity generally increases with NaFSI salt content, reaching ≈1 mS cm −1 at 80 °C at the highest salt concentration (PPC:NaFSI = 0.5:1). Through an all-in-one slurry casting method, Na 2/3 Ni 1/3 Mn 2/3 O 2 cathode composites are fabricated in which the dispersed PPC electrolyte acts as the primary binder. Enabled by a bilayer polymer electrolyte system, cycling performance with the PPC cathode electrolyte is optimized with respect to salt concentration and anode material. The best cyclability is achieved with a moderate salt concentration electrolyte (PPC:NaFSI = 5:1), showcasing an initial capacity of 83 mA h g −1 with a remarkable 80% capacity retention after 150 cycles at C/5 rate and 60 °C. The superior performance of the lower salt concentration electrolyte is attributed to better electrochemical stability, as confirmed by linear sweep voltammetry and online electrochemical mass spectrometry measurements. In conclusion, these results underscore the potential of carbonate-based polymer electrolytes and the importance of balancing electrolyte conductivity and stability in cell design.

25 ENERGY STORAGE↗
DOE OSTI2025

Spatial‐Uniformity–Driven Bayesian Optimization for Rapid Development of Printed Perovskite Solar Cells

Printed metal halide perovskites can enable rapid, roll-to-roll manufacturing of a broad class of optoelectronics—flexible solar cells and imagers among them—while promising cost and speed advantages over incumbent silicon. However, though current methods offer high throughput and patterning capabilities, perovskite films’ spatial heterogeneity remains a challenge for large-area devices. Here, a spatial-uniformity-driven Bayesian optimization (BO) approach is leveraged to accelerate the development of printed perovskite solar cells and improve large-area device performance. Using a BO surrogate model, a 6D design space of ink chemistry and printing physics is explored via extensive iterative experimentation (≈100) informed by an objective function capturing spatial photoluminescence (PL) variance. It is discovered that optimizing for uniformity drives rapid advances in photovoltaic performance, yielding ≈20% power conversion efficiency (PCE) for small area (0.134 cm 2 ) devices and > 16% for large area (1 cm 2 ) devices. This machine-learning approach simultaneously enables rheological comparison of ink formulations that accelerate the leveling of Saffman-Taylor artifacts and improve film uniformity. Here, this showcases uniformity-driven BO as an efficient approach for uncovering the key printing physics and mitigating spatial heterogeneity to enable device scaling beyond small cell areas.

14 SOLAR ENERGY↗
DOE OSTI2026

Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning

Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.

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

Toward Standardized Microscale Tensile Testing for Two‐Photon Polymerization‐Fabricated Materials in Liquid

Two-photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP-fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate-based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross-sectional areas ranging from 1 to 36 μm 2 . Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

mechanical characterization↗
DOE OSTI2025

Zwitterionic Photocurable Resin for High‐Resolution 3D Printing of Ultralow‐Fouling Microstructures

High‐resolution 3D printing technologies are enabling a new generation of microstructured materials for applications where biocompatibility is critical. However, most conventional 3D‐printable resins yield materials that exhibit trade‐offs between antifouling properties and mechanical robustness, limiting their applicability in living systems. In nature, zwitterionic surface groups form tightly bound hydration layers that act as effective barriers against protein and cell attachment. Inspired by this strategy, a zwitterionic acrylamide‐based photoresist—carboxybetaine di‐methacrylamide (CBDA)—is developed for projection‐based vat photopolymerization, enabling the fabrication of complex microarchitectures with exceptional antifouling properties. The bifunctional monomer allows the formation of dense, cross‐linked networks that resist swelling while maintaining a high density of zwitterionic groups. Printed structures exhibit strong resistance to protein and cell adhesion, as confirmed by porcine blood assays, alongside robust mechanical performance. As a demonstration, a tubular structure featuring a negative Poisson's ratio lattice is printed to showcase structural fidelity and versatility. This resin formulation offers a broadly applicable strategy for fabricating microscale devices and surfaces where antifouling performance and structural integrity are both essential—spanning biomedical interfaces, soft robotics, and beyond.

3D printing↗
DOE OSTI2025

PtCoO 2 for Scaled Interconnects

Copper (Cu) interconnects are an increasingly important bottleneck in integrated circuits due to energy consumption and latency caused by the notable increase in Cu resistivity as dimensions decrease, primarily due to electron scattering at surfaces. Herein, the potential of a directional conductor, PtCoO 2 , which has a low bulk resistivity and a distinctive anisotropic structure that mitigates electron surface scattering is showcased. Thin films of PtCoO 2 of various thicknesses are synthesized by molecular beam epitaxy (MBE) coupled with a postdeposition annealing process and the superior quality of PtCoO 2 films is demonstrated by multiple characterization techniques. The thickness‐dependent resistivity curve illustrates that PtCoO 2 significantly outperforms effective Cu (Cu with TaN barriers) and Ru in resistivity below 20.0 nm with a more than 6x reduction compared to effective Cu below 6.0 nm, having a value of only 6.32 μΩ cm at 3.3 nm. It is determined that grain boundary scattering can still be improved for even lower resistivities in this material system through a combination of experiments and theoretical simulations. PtCoO 2 is therefore a highly promising alternative material for future interconnect technologies promising lower resistivities, better stability, and significant improvements in energy efficiency and latency for advanced integrated circuits.

Li, Yansong [Department of Electrical Engineering ↗
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗