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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Significant Efficiency Enhancements in Non‐Y Series Acceptors by the Addition of Outer Side Chains

Abstract Most current highly efficient organic solar cells utilize small molecules like Y6 and its derivatives as electron acceptors in the photoactive layer. In this work, a small molecule acceptor, SC8‐IT4F, is developed through outer side chain engineering on the terminal thiophene of a conjugated 6,12‐dihydro‐dithienoindeno[2,3‐d:2′,3′‐d′]‐s‐indaceno[1,2‐b:5,6‐b′]dithiophene (IDTT) central core. Compared to the reference molecule C8‐IT4F, which lacks outer side chains, SC8‐IT4F displays notable differences in molecule geometry (as shown by simulations), thermal behavior, single‐crystal packing, and film morphology. Blend films of SC8‐IT4F and the polymer donor PM6 exhibit larger carrier mobilities, longer carrier lifetimes, and reduced recombination compared to C8‐IT4F, resulting in improved device performance. Binary photovoltaic devices based on the PM6:SC8‐IT4F films reveal an optimal efficiency over 15%, which is one of the best values for non‐Y type small molecule acceptors (SMAs). The resultant devices also show better thermal and operational stability than the control PM6:L8‐BO devices. SC8‐IT4F and its blend exhibit a higher relative degree of crystallinity and π coherence length, compared to C8‐IT4F samples, beneficial for charge transport and device performance. The results indicate that outer side chain engineering on existing small electron acceptors can be a promising molecular design strategy for further pursuing high‐performance organic solar cells.

He, Qiao [Department of Chemistry and Centre for P↗
DOE OSTI2024

Polymer Passivated All Inorganic Micro‐Structured CsPbI x Br y Perovskite Toward Highly Efficient Photodetectors

Abstract Solution‐processed inorganic perovskites cause chemical and structural defects unfavorable for photodetector application. Using a binary solvent, defects in CsPbI x Br y (CPIB) perovskite are passivated with poly 4‐vinylpyridine (PVP) and Poly methyl methacrylate (PMMA) polymers. X‐ray photoelectron spectroscopy and FTIR spectra reveal a Lewis base‐acid interaction between Pb 2+ and polymer, confirming the passivation of CPIB perovskite. Scanning electron microscopy analysis shows a dual‐surface morphology with microribbons and microcrystals in perovskites. After PMMA treatment, CPIB perovskite exhibits a blue shift in the bandgap (1.8 to 1.95 eV), while the PVP induced a redshift, reducing the bandgap to 1.7 eV. Blue shift in PL analysis indicates modification of grain boundaries. A higher lifetime obtained for CPIB/PVP confirms the restraint of non‐radiative recombinations. Photodetectors are fabricated with pristine CPIB, CPIB/PVP, and CPIB/PMMA perovskites. The passivated CPIB/PVP‐based photodetector exhibits a quick rise time of ≈23 ms and a decay time of ≈17 ms. It also demonstrates a remarkable photoresponsivity of 23 mA W −1 , an internal quantum efficiency of 4.9%, and a detectivity of 15.0 × 10 10 Jones at 10 mW cm −2 light intensity. This approach shows the potential for environmentally stable polymers to passivate inorganic perovskites for high photodetection performance.

Shah, Shruti↗
DOE OSTI2025

Fine‐Tuning Li‐Ion Solvation Structure by Enhanced Solvent‐Diluent Interactions for Long‐Cycling Lithium Metal Batteries

Achieving durable lithium (Li) metal anodes in liquid electrolytes remains challenging, primarily due to the instability of the formed solid-electrolyte interphases (SEIs). Modulating the Li-ion solvation structures is pivotal in forming a stable SEI for stabilizing Li metal anodes. Here a strategy is developed to fine-tune the Li-ion solvation structures through enhanced dipole–dipole interactions between the Li-ion-coordinated solvent and the non-Li-ion-coordinating diluent, for creating a stable SEI in the developed binary salt electrolyte. The enhanced dipole–dipole interactions weaken the coordination between Li-ions and the solvents while strengthening the interaction between Li-ions and dual anions, thereby facilitating the Li-ion transport and a robust anion-derived SEI with a distinct bilayer structure. Consequently, the developed electrolyte exhibited exceptional electrochemical performance in high energy-density Li||LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cells, with long calendar life, stable cyclability at 1 C, and reliable operation between 25 and −20 °C, and it also demonstrat remarkable cycling stability for a Li||NMC811 pouch cell with projected energy density of 402 Wh kg −1 , maintaining 80% capacity retention over 606 cycles under practical conditions.

25 ENERGY STORAGE↗
DOE OSTI2025

Foaming prediction in pure liquids from dimensionless numbers inspired by the theory of fluid behavior for drops

Foaming prediction is critical for selecting materials and designing processes in industries such as bioprocessing and gas processing. Existing models lack the generality needed for a wide range of materials and overlook the foaming behavior in pure liquids. Here, this work presents a novel method for predicting foaming in pure liquids based on their density, surface tension, and viscosity, using Reynolds ( Re ) and Ohnesorge ( Oh ) numbers. A foaming prediction map, leveraging the theory of fluid drop behavior, was developed by plotting these numbers. This map delineates distinct non-foaming and foaming regions, functioning as a binary classifier for foaming predictions. The map was fitted and validated through shake test experiments on 46 liquids, demonstrating reliable predictions, except for a specific region characterized by small Oh and large Re numbers. This region corresponded to relatively low foam stability and high turbulence, making foaming predictions challenging for liquids in this category.

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

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Rewritable Surface‐Grafted Polymer Brushes with Dynamic Covalent Linkages

Abstract Surface grafting of polymer brushes drastically modifies surface properties, including wettability, compatibility, responsiveness, etc. A broad variety of functionalities can be introduced to the surface via different types of polymers. Bringing together properties of two or more types of polymer brushes to one surface opens up even more possibilities in brush‐modified materials. However, while it is generally feasible to introduce several chemical compositions along the brushes via copolymerization, it is challenging to vary the types of polymer brushes along a surface. Although previous studies have demonstrated binary brushes via orthogonal polymerization techniques or partial deactivation/regrafting, they commonly limit the number of polymer types to two. Here, we propose a strategy to introduce dynamic covalent diketoenamine linkages at the root of polymer brushes. The grafting density could be precisely tuned during surface functionalization. The surface‐anchored polymer brushes were cleaved by the addition of small molecule amines. New polymer brushes can be regrafted from the surface following refunctionalization of exposed sites. The maneuverability allows tuning of the types and densities of the polymer brushes, pointing the way to the preparation of a new generation of well‐defined brush‐modified materials with mixed grafts, with potential applications in the design of smart materials and surfaces.

Cui, Feichen↗
DOE OSTI2025

High Pressure Synthesis of Pr 2 O 5 – A Unique Lanthanoid(IV) Oxide Peroxide

Abstract Reacting praseodymium(IV) oxide with oxygen at 27 GPa in a diamond anvil cell yielded the oxide peroxide Pr 2 IV (O 2 )O 3 , which was characterized by single crystal X‐ray diffraction on multi‐grain samples, Raman spectroscopy and quantum theoretical calculations at various pressure points. The presence of tetravalent praseodymium ions is supported by electronic structure calculations, showing a band gap of ca. 1.2 eV, which is consistent with the anticipated chemical model of an ionic solid. Pr 2 (O 2 )O 3 thus far represents the most oxygen rich phase of any binary compound of a lanthanoid and oxygen and is the first example of a peroxide anion next to Pr 4+ . Additionally, these results demonstrate that instead of oxidizing the praseodymium ions past their +IV oxidation state, oxygen undergoes a comproportionation to form peroxide anions. Direct oxidation of the oxide anions by Pr 4+ ‐ions was ruled out by a control experiment in argon instead of oxygen, where no oxidation of oxide ions was observed.

Chemistry↗
DOE OSTI2025

Ternary Potassium‐Bismuth‐Telluride Intermetallic Support Promotes Electrochemical Stability in Potassium Metal Anodes

We employed accumulative roll bonding to fabricate self‐standing metallurgical composite of in situ formed alkaline potassium‐bismuth‐telluride intermetallic K 2 (Bi 2/6 Te 3/6 Vac 1/6 ) embedded in potassium metal. This newly discovered thermodynamically stable potassiophilic crystal, termed “KBT”, is fcc antifluorite with K 2 Te archetype. Symmetric cells achieve 880 h of cycling at 0.5 mA cm −2 and 0.5 mAh cm −2 . Potassium metal battery (KMB) with Prussian blue (PB) cathode in carbonate electrolyte retains 80% capacity after 200 cycles at 1C. In ether‐based electrolyte with organic cathode, it achieves 80% retention after 900 cycles at 2C. Combined synchrotron X‐ray nano‐tomography, cryogenic‐focused ion beam microscopy (Cryo‐FIB) and sputter‐down X‐ray photoelectron spectroscopy (XPS) demonstrate uniform electrodeposits, versus baseline of potassium filaments intermixed with pores and coarse SEI. Binary K 3 Bi‐K and K 2 Te‐K intermetallic supports also provide improved electrochemical performance, albeit to lesser extent. Multiscale simulation provides insight into role of support structure in adatom energetics, film nucleation, early‐stage SEI morphology and interfacial stability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthesis of Gold Hydride at High Pressure and High Temperature

Gold is an unreactive metal and its chemical interactions with hydrogen have only recently been explored. Here, in this study, we report the formation of gold hydride above 40 GPa and 2200 K in X-ray free electron laser heated diamond anvil cells using various hydrocarbons as hydrogen sources. Above 40 GPa, a hexagonal phase emerges close to the gold melting point, corresponding to a hydride with stoichiometry Au 2 H x , with x increasing from 0 to near 1 with pressure from 40 to 80 GPa. This is a high-temperature phase which reverts to face centered cubic gold on cooling to 295 K. Accompanying DFT-MD simulations are in excellent agreement with experiment and reveal the structure to consist of an hexagonal close packed gold lattice with atomic hydrogen disordered in the interstices. The hydrogen is superionic and exhibits high diffusivity through the crystalline gold lattice. Our results present the first solid-state binary compound of gold and hydrogen.

Ab initio calculations↗
DOE OSTI2025

Self‐Stabilized Heterometallic Pair Sites for Selective Ethanol Dehydrogenation on Pt–Cr–Ag Alloy Catalysts

Self‐stabilized, heterometallic pair‐sites can enable fine‐tuning of catalytic functionality while also mitigating dynamic structural changes that degrade catalytic performance. This study demonstrates the development and characterization of trimetallic Pt x Cr x Ag 1‐2x ( x ≤ 0.1) alloys with active Pt–Cr pair‐ensembles for non‐oxidative ethanol dehydrogenation, leveraging predictions that favorable bonding stabilizes Pt–Cr pairs diluted in Ag. Operando X‐ray absorption spectroscopy confirms the preferential formation and stability of Pt–Cr pairings dispersed throughout the Ag matrix, and ambient‐pressure X‐ray photoelectron spectroscopy shows that Pt–Cr sites have significant activity for ethanol dehydrogenation, while suppressing reaction processes that deactivate binary Pt–Ag and Cr–Ag alloys. This work demonstrates that stabilizing heterometallic pair sites within trimetallic alloys provides a new avenue for designing catalysts with discrete active sites that are durable and highly selective.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

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

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗
DOE OSTI2025

Classical eikonal from Magnus expansion

In a classical scattering problem, the classical eikonal is defined as the generator of the canonical transformation that maps in-states to out-states. It can be regarded as the classical limit of the log of the quantum S-matrix. In a classical analog of the Born approximation in quantum mechanics, the classical eikonal admits an expansion in oriented tree graphs, where oriented edges denote retarded/advanced worldline propagators. The Magnus expansion, which takes the log of a time-ordered exponential integral, offers an efficient method to compute the coefficients of the tree graphs to all orders. We exploit a Hopf algebra structure behind the Magnus expansion to develop a fast algorithm which can compute the tree coefficients up to the 12th order (over half a million trees) in less than an hour. In a relativistic setting, our methods can be applied to the post-Minkowskian (PM) expansion for gravitational binaries in the worldline formalism. We demonstrate the methods by computing the 3PM eikonal and find agreement with previous results based on amplitude methods. Importantly, the Magnus expansion yields a finite eikonal, while the naïve eikonal based on the time-symmetric propagator is infrared-divergent from 3PM on.

Black Holes↗
DOE OSTI2025

Spinning black hole scattering at $ \mathcal{O} $(G 3 S 2 )(G 3 S 2 ): Casimir terms, radial action and hidden symmetry

We resolve subtleties in calculating the post-Minksowskian dynamics of binary systems, as a spin expansion, from massive scattering amplitudes of fixed finite spin. In particular, the apparently ambiguous spin Casimir terms can be fully determined from the gradient of the spin-diagonal part of the amplitudes with respect to S 2 = −s(s+1)ħ 2 , using an interpolation between massive amplitudes with different spin representations. From two-loop amplitudes of spin-0 and spin-1 particles minimally coupled to gravity, we extract the spin Casimir terms in the conservative scattering angle between a spinless and a spinning black hole at $ \mathcal{O} $(G 3 S 2 ), finding agreement with known results in the literature. This completes an earlier study [Phys. Rev. Lett. 130 (2023), 021601] that calculated the non-Casimir terms from amplitudes. We also illustrate our methods using a model of spinning bodies in electrodynamics, finding agreement between scattering amplitude predictions and classical predictions in a root-Kerr electromagnetic background up to $ \mathcal{O} $(α 3 S 2 ). For both gravity and electrodynamics, the finite part of the amplitude coincides with the two-body radial action in the aligned spin limit, generalizing the amplitude-action relation beyond the spinless case. Surprisingly, the two-loop amplitude displays a hidden spin-shift symmetry in the probe limit, which was previously observed at one loop. We conjecture that the symmetry holds to all orders in the coupling constant and is a consequence of integrability of Kerr orbits in the probe limit at the first few orders in spin.

Classical Theories of Gravity↗