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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Baseband Digital Network Analyzer Upgrade for LLRF Controllers

Digital Network Analyzers (DNA) have been implemented in many Low-Level Radio Frequency (LLRF) systems, notably NSLS-II and CERN, to help tune feedback loops. DNA characterizes feedback loops by measuring the frequency-dependent magnitude and phase transfer functions. It enables the measurement of open loop gains, gain/phase margins, and loop delays to help fine-tune feedback loops. An FPGA-based DNA has been developed and integrated into the current Relativistic Heavy Ion Collider (RHIC) LLRF infrastructure. Its performance has been tested with an implementation of one-turn delay feedback (OTFB) on the bench to maximize gain and stability. The DNA has been used to characterize a RHIC 28 MHz cavity in a RHIC Accelerator Physics Experiment (APEX) to test transient beam loading compensation strategies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn t enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗
DOE OSTI2024

Automating Rabi & Ramsey Measurements via Machine Learning

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗
DOE OSTI2024

3D Lead-Organoselenide-Halide Perovskites and their Mixed-Chalcogenide and Mixed-Halide Alloys

Here, we incorporate Se into the 3D halide perovskite framework using the zwitterionic ligand: SeCYS ( + NH 3 (CH 2 ) 2 Se - ), which occupies both the X - and A + sites in the prototypical ABX 3 perovskite. The new organoselenide-halide perovskites: (SeCYS)PbX 2 (X=Cl, Br) expand upon the recently discovered organosulfide-halide perovskites. Single-crystal X-ray diffraction and pair distribution function analysis reveal the average structures of the organoselenide-halide perovskites, whereas the local lead coordination environments and their distributions were probed through solid-state 77 Se and 207 Pb NMR, complemented by theoretical simulations. Density functional theory calculations illustrate that the band structures of (SeCYS)PbX 2 largely resemble those of their S analogs, with similar band dispersion patterns, yet with a considerable band gap decrease. Optical absorbance measurements indeed show band gaps of 2.07 and 1.86 eV for (SeCYS)PbX 2 with X=Cl and Br, respectively. We further demonstrate routes to alloying the halides (Cl, Br) and chalcogenides (S, Se) continuously tuning the band gap from 1.86 to 2.31 eV–straddling the ideal range for tandem solar cells or visible-light photocatalysis. The comprehensive description of the average and local structures, and how they can fine-tune the band gap and potential trap states, respectively, establishes the foundation for understanding this new perovskite family, which combines solid-state and organo-main-group chemistry.

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

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: is qubit tune-up able to be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗
DOE OSTI2023

Sterically attenuated electronic communication in cobalt complexes of meridional isoquinoline-derived ligands for applications in electrocatalysis

The ability to synthetically tune the ligand frameworks of redox-active molecules is of critical importance to the economy of solar fuels because manipulating their redox properties can afford control over the operating potentials of sustained electrocatalytic or photoelectrocatalytic processes. The electronic and steric properties of 2,2′:6′,2″-terpyridine (Terpy) ligand frameworks can be tuned by functional group substitution on ligand backbones, and these correlate strongly to their Hammett parameters. The synthesis of a new series of tridentate meridional ligands of 2,4,6-trisubstituted pyridines that engineers the ability to finely tune the redox potentials of cobalt complexes to more positive potentials than that of their Terpy analogs is achieved by aryl-functionalizing at the four-position and by including isoquinoline at the two- and six-positions of pyridine (Aryl-DiQ). Their cobalt complex syntheses, their electronic properties, and their catalytic activity for carbon dioxide (CO2) reduction are reported and compared to their Terpy analogs. The cobalt derivatives generally experience a positive shift in their redox features relative to the Terpy-based analogs, covering a complementary potential range. Although those evaluated fail to produce any quantifiable products for the reduction of CO2 and suffer from long-term instability, these results suggest possible alternate strategies for stabilizing these compounds during catalysis. We speculate that lower equilibrium association constants to the cobalt center are intrinsic to these ligands, which originate from a steric interaction between protons on the pyridine and isoquinoline moieties. Nevertheless, the new Aryl-DiQ ligand framework has been engineered to selectively tune homoleptic cobalt complexes’ redox potentials.

Chemistry↗
DOE OSTI2021

Giant topological Hall effect in centrosymmetric tetragonal Mn 2–x Zn x Sb

Topological magnetism typically appears in noncentrosymmetric compounds or compounds with geometric frustration. Here, we report the effective tuning of magnetism in centrosymmetric tetragonal Mn 2–x Zn x Sb by Zn substitution. The magnetism is found to be closely coupled to the transport properties, giving rise to a very large topological Hall effect with fine-tuning of Zn content, which even persists to high temperature (~250K). The further magnetoentropic analysis suggests that the topological Hall effect is possibly associated with topological magnetism. Furthermore, our finding suggests Mn 2–x Zn x S is a candidate material for a centrosymmetric tetragonal topological magnetic system, offering opportunities for studying and tuning spin textures and developing near room temperature spin-based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nano on micro: tuning microbial metabolisms by nano-based artificial mediators to enhance and expand production of biochemicals

Advances in synthetic biology and metabolic engineering across the past few decades have enabled the successful production of many novel chemicals. However, bioproduction of such chemicals is often limited by low yield and titer due to disrupted metabolic homeostasis. Finely tuning cellular metabolism to restore robust metabolic functions entails various genetic modifications, which is often not practical. Alternatively, artificial mediators capable of tailoring microbial metabolisms open a new avenue for restoring physiological functions. In this context, nanoparticle-based artificial mediators have been pursued to tune cellular metabolisms. They can not only enhance production of molecules from endogenous metabolism, but also expand bioproducts spectrum. Here, we reviewed recent advances toward the employment of nano-based artificial mediators for the tuning of cellular metabolism, with a focus on their positive effects on electron transfer and pathway flux. Perspectives for potential applications of artificial mediators for mediating microbial metabolisms in the future were also provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Theoretical Investigation of the Hydrogenation of Cyclohexene Catalyzed by Supported Single-Atom Sites on Redox Noninnocent LiMn 2 O 4 and Li 2 Mn 2 O 4 Surfaces

Here, the tuning of catalyst activity via stereoelectronic modulation of the active-site structure remains a grand challenge in heterogeneous catalysis. In homogeneous catalysis, the redox noninnocent ligands can be introduced to organometallic fragments to donate electrons to the metal center and fine-tune the catalytic activity. Analogously, lithium-ion battery materials, such as lithium manganese oxide (LMO), lithium titanium oxides (LTO), etc., can serve as redox noninnocent catalyst support to modulate the electronic structures of the active site via lithiation, hence tuning the catalytic activity of supported active sites. Experimentally, the Ni single-atom site was supported on LiMn 2 O 4 via oxidative grafting and exhibited no catalytic activity toward the hydrogenation of cyclohexene. After introducing additional Li into the catalyst support forming Li 2 Mn 2 O 4 , the Ni single-atom site becomes active, with the catalytic rates increasing as a function of the lithiation for Li/Mn ratios >0.9. In this paper, density functional theory (DFT) calculations are performed to study the Ni site structure via X-ray absorption near edge structure (XANES) simulations and investigate the electronic properties of Ni single-atom site before and after the addition of intercalated lithium in the LMO spinel structure. Furthermore, the study of the reaction mechanism is also carried out to understand the thermodynamically and kinetically favored pathways. XANES simulation suggests that the Ni single-atom site is likely to stay in the Li channel of the spinel support structure and form an octahedral structure. After Li intercalation, the Ni site becomes less positively charged, indicating the partial reduction of the Ni site. The simulated reaction energy profile over the LiMn 2 O 4 support exhibits high-energy barriers (1.07 eV) for the hydrogenation of cyclohexene; however, the Li 2 Mn 2 O 4 support is able to better stabilize low-coordinated ion sites and improve ion mobility, leading to lower overall energy barriers (0.64 eV). The reduced and low-coordinated ion site, thus, can better stabilize the reaction intermediates and promote hydrogenation reaction. Similarly, other transition metal ions (Fe, Co, and Cu) are also considered over the LiMn 2 O 4 and Li 2 Mn 2 O 4 catalyst supports for hydrogenation reaction.

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

1,10-Phenanthroline-Iron Complex-Derived Fe-N-C Electrocatalysts: Enhanced Oxygen Reduction Activity and Stability Through Synthesis Tuning

The development of electrocatalysts composed of earth-abundant elements is essential for advancing the commercial application of Proton Exchange Membrane Fuel Cells (PEMFC). Among these, single-atom electrocatalysts, such as Fe-N-C, show great promise for the oxygen reduction reaction (ORR). This study aims to improve the ORR activity and stability of Fe-N-C electrocatalysts by fine-tuning the straightforward 1,10-phenanthroline-iron complexation synthesis method. Key parameters, including iron-to-phenanthroline ratio, carbon powder surface area, and pyrolysis temperature were systematically varied to evaluate their influence on the resulting electrocatalysts. The findings of this study revealed that the electrocatalysts synthesized with 1,10-phenanthroline (Phen) and high-surface-area Black Pearls (BP) possessed much better ORR activity than electrocatalysts prepared by using Vulcan carbon (lower surface area). Interestingly, electrocatalysts prepared with BP, but with a non-bidentate nitrogen-containing ligand molecule, such as imidazole, showed a much poorer activity, as the resulting material predominantly consisted of inactive structures, such as encapsulated iron nanoparticles and iron oxide, as evidenced by HR-TEM, EXAFS, and XRD. Therefore, the results suggest that only the synergistic combination of the bidentate ligand phenanthroline (Phen) and the high-surface-area carbon support (BP) favored the formation of ORR-active Fe-N-C single-atom species upon pyrolysis. The study also unveiled a significant enhancement in electrocatalyst stability during accelerated durability tests (and air storage) as the pyrolysis temperature was increased from 700 to 1300 °C, albeit at the expense of ORR activity, likely resulting from the generation of iron particles. Pyrolysis at 1050 °C yielded the electrocatalyst with the most favorable balance of activity and stability in rotating disk measurements, while maintaining moderate durability under PEM fuel cell operation. The insights obtained in this study may guide the development of more active efficient and durable electrocatalysts, synthesized via a simple method using earth-abundant elements, for application in PEMFC cathodes.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Uncovering supramolecular chirality codes for the design of tunable biomaterials

In neurodegenerative diseases, polymorphism and supramolecular assembly of β-sheet amyloids are implicated in many different etiologies and may adopt either a left- or right-handed supramolecular chirality. Yet, the underlying principles of how sequence regulates supramolecular chirality remains unknown. Here, we characterize the sequence specificity of the central core of amyloid-β 42 and design derivatives which enable chirality inversion at biologically relevant temperatures. We further find that C-terminal modifications can tune the energy barrier of a left-to-right chiral inversion. Leveraging this design principle, we demonstrate how temperature-triggered chiral inversion of peptides hosting therapeutic payloads modulates the dosed release of an anticancer drug. These results suggest a generalizable approach for fine-tuning supramolecular chirality that can be applied in developing treatments to regulate amyloid morphology in neurodegeneration as well as in other disease states.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

New-Generation Carbon-Capture Ionic Liquids Regulated by Metal-Ion Coordination

Development of efficient carbon capture-and-release technologies with minimal energy input is a long-term challenge in mitigating CO 2 emissions, especially via CO 2 chemisorption driven by engineered chemical bond construction. Herein, taking advantage of the structural diversity of ionic liquids (ILs) in tuning their physical and chemical properties, precise reaction energy regulation of CO 2 chemisorption was demonstrated deploying metal-ion-amino-based ionic liquids (MAILs) as absorbents. The coordination ability of different metal sites (Cu, Zn, Co, Ni, and Mg) to amines was harnessed to achieve fine-tuning on stability constants of the metal ion-amine complexes, acting as the corresponding cations in the construction of diverse ILs coupled with CO 2 -philic anions. The as-afforded MAILs exhibited efficient and controllable CO 2 release behavior with great reduction in energy input and minimal sacrifice on CO 2 uptake capacity. This coordination-regulated approach offers new prospects for the development of ILs-based systems and beyond towards energy-efficient carbon capture technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling spatial patterns of longleaf pine needle dispersal using long-term data

Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R 2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R 2 = 0.612), basal area model (R 2 = 0.488), OAI model (R 2 = 0.416), and the null model (R 2 = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R 2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R 2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions

Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving the LLMs to align existing foundation models with scientific disciplines, concepts and goals. In this work, we present SciTune as a tuning framework to improve the ability of LLMs to follow scientific multimodal instructions. To test our methodology, we use a human-generated scientific instruction tuning dataset and train a large multimodal model LLaMA-SciTune that connects a vision encoder and LLM for science-focused visual and language understanding. LLaMA-SciTune significantly outperforms the state-of-the-art models in the generated figure types and captions in multiple scientific multimodal benchmarks. In comparison to the models that are fine-tuned with machine generated data only, LLaMA-SciTune surpasses human performance on average and in many sub-categories on the ScienceQA benchmark.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Solid Electrolyte Bimodal Grain Structures for Improved Cycling Performance

Here, the application of solid-state electrolytes in Li batteries is hampered by the occurrence of Li-dendrite-caused short circuits. To avoid cell failure, the electrolytes can only be stressed with rather low current densities, severely restricting their performance. As grain size and pore distributions significantly affect dendrite growth in ceramic electrolytes such as Li 7 La 3 Zr 2 O 12 and its variants; here, a “detour and buffer” strategy to bring the superiority of both coarse and fine grains into play, is proposed. To validate the mechanism, a coarse/fine bimodal grain microstructure is obtained by seeding unpulverized large particles in the green body. The rearrangement of coarse grains and fine pores is fine-tuned by changing the ratio of pulverized and unpulverized powders. The optimized bimodal microstructure, obtained when the two powders are equally mixed, allows, without extra interface decoration, cycling for over 2000 h as the current density is increased from 1.0 mA·cm -2 , and gradually, up to 2.0 mA·cm -2 . The “detour and buffer” effects are confirmed from postmortem analysis. The complex grain boundaries formed by fine grains discourage the direct infiltration of Li. Simultaneously, the coarse grains further increase the tortuosity of the Li path. This study sheds light on the microstructure optimization for the polycrystalline solid-state electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

Orthogonal replication with optogenetic selection evolves yeast JEN1 into a mevalonate transporter

Abstract The in vivo continuous evolution system OrthoRep (orthogonal replication) is a powerful strategy for rapid enzyme evolution inSaccharomyces cerevisiaethat diversifies genes at a rate exceeding the endogenous genome mutagenesis rate by several orders of magnitude. However, it is difficult to neofunctionalize genes using OrthoRep partly because of the way selection pressures are applied. Here we combine OrthoRep with optogenetics in a selection strategy we call OptoRep, which allows fine-tuning of selection pressure with light. With this capability, we evolved a truncated form of the endogenous monocarboxylate transporterJEN1 (JEN1t)into a de novo mevalonate importer. We demonstrate the functionality of the evolvedJEN1t(JEN1t Y180C/G ) in the production of farnesene, a renewable aviation biofuel, from mevalonate fed to fermentation media or produced by microbial consortia. This study shows that the light-induced complementation of OptoRep may improve the ability to evolve functions not currently accessible for selection, while its fine tunability of selection pressure may allow the continuous evolution of genes whose desired function has a restrictive range between providing effective selection and cellular viability.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Thiol post-translational modifications modulate allosteric regulation of the OpcA–G6PDH complex through conformational gate control

In cyanobacteria, the redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and thereby precisely regulating carbon flux between anabolic and catabolic pathways. Although redox-sensitive cysteines in OpcA are known to regulate G6PDH, the mechanisms by which redox post-translational modifications (PTMs) on OpcA control G6PDH structure and activity remain unclear. Here, we combine computational modeling with experimental redox proteomics in Synechococcus elongatus PCC 7942 to dissect this mechanism. Experimentally, redox proteome analysis revealed differential redox PTM patterns, particularly on cysteines within the G6PDH-binding site of OpcA. These environmentally sensitive PTM changes at the interface suggest that thiol modifications in this region form a key regulatory node. More broadly, redox proteomics identified site-specific cysteine modifications under light/dark transitions and circadian cycling, linking distinct redox regimes to discrete PTM states. We employed PTM-Psi simulations to show that thiol PTMs near the OpcA–G6PDH interface are critical for allosteric regulation of G6PDH. The thiol PTMs on OpcA affect a putative gate region in G6PDH for substrate ingress and product egress as well as key hydrogen-bond networks within the active site. We infer that PTMs on OpcA tune the conformational landscapes of individual G6PDH subunits toward functionally relevant configurations according to environmental gradients, biasing the enzyme toward catalytically favorable states. Together, our results reveal a molecular mechanism in which thiol PTMs on OpcA modulate G6PDH structure and function through PTM-induced reorganization of conformational dynamics and allosteric communication. These findings demonstrate that PTM-level regulation provides a critical control layer from genotypes to phenotypes that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning.

Allosteric regulation↗