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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Conformation-specific synthetic intrabodies modulate mTOR signaling with subcellular spatial resolution

Subcellular compartmentalization is integral to the spatial regulation of mechanistic target of rapamycin (mTOR) signaling. However, the biological outputs associated with location-specific mTOR signaling events are poorly understood and challenging to decouple. Here, we engineered synthetic intracellular antibodies (intrabodies) that are capable of modulating mTOR signaling with genetically programmable spatial resolution. Epitope-directed phage display was exploited to generate high affinity synthetic antibody fragments (Fabs) against the FKBP12–Rapamycin binding site of mTOR (mTOR FRB ). We determined high-resolution crystal structures of two unique Fabs that discriminate distinct conformational states of mTOR FRB through recognition of its substrate recruitment interface. By leveraging these conformation-specific binders as intracellular probes, we uncovered the structural basis for an allosteric mechanism governing mTOR complex 1 (mTORC1) stability mediated by subtle structural adjustments within mTOR FRB . Furthermore, our results demonstrated that synthetic binders emulate natural substrates by employing divergent yet complementary hydrophobic residues at defined positions, underscoring the broad molecular recognition capability of mTOR FRB . Intracellular signaling studies showed differential time-dependent inhibition of S6 kinase 1 and Akt phosphorylation by genetically encoded intrabodies, thus supporting a mechanism of inhibition analogous to the natural product rapamycin. Finally, we implemented a feasible approach to selectively modulate mTOR signaling in the nucleus through spatially programmed intrabody expression. These findings establish intrabodies as versatile tools for dissecting the conformational regulation of mTORC1 and should be useful to explore how location-specific mTOR signaling influences disease progression.

Science & Technology - Other Topics↗
DOE OSTI2020

Study of turbulence-induced refraction of lower hybrid waves using synthetic scrape-off layer filaments

Here, turbulence-induced refraction effects to lower hybrid (LH) wave propagation and current drive are studied using synthetic scrape-off layer (SOL) blob/filament fields. A synthetic 3D, field-following, blob turbulence model is implemented in the ray-tracing/Fokker-Planck (RTFP) codes GENRAY/CQL3D. In Alcator C-Mod, the blob field is shown to significantly affect LH ray-trajectories, leading to increased on-axis damping and smoother current profiles. This effect depends on the average blob size and amplitude. In addition, the diffusion of ray-trajectories in phase-space caused by turbulence increases the robustness of the RTFP model. A modified $N_{||}$ launch spectrum, acting as a proxy for parametric decay instability (PDI) effects, is included in simulations with the blob model. A synergy between the modified launch spectrum and turbulence-induced refraction results in synthetic hard x-ray profiles that agree with experiment. Lastly, the blob model is used to predict the effect of SOL turbulence on DIII-D high-field side (HFS) LH launch. Assuming low turbulence amplitude in the HFS SOL (~5%), turbulence-induced refraction is predicted to have little effect on current drive efficiency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Synthetic modeling of soft x-ray emissivity for magnetic island analysis in LTX-β

We present a synthetic soft x-ray (SXR) forward-modeling framework that characterizes the emissivity structure of rotating magnetic islands in the Lithium Tokamak eXperiment-β across space and time. Magnetic islands associated with tearing modes produce modulations in line-integrated SXR brightness. Traditional tomographic methods struggle to resolve these structures in devices with limited sightlines, resulting in an under-determined inversion problem, or otherwise necessitate equilibrium reconstruction data, preventing their use in active control. Our approach removes this challenge by combining a fully three-dimensional ray-tracing model with a time-dependent emissivity prescription of a $m/n$ = 2/1 magnetic island. The model is validated through helical island geometry, rotation measured by magnetic diagnostics, and equilibrium constraints from PSI-Tri reconstructions. Synthetic brightness signals are generated for each photodiode sightline from a modeled emissivity profile and directly compared with experimental data from a tangential midplane SXR array. By fitting the synthetic diagnostic output to the observed brightness across time, we simultaneously infer key geometric parameters-such as island radial location, island width, and rotation frequency-without requiring full tomographic reconstruction, all with an average absolute deviation of less than 3%. This work demonstrates that forward modeling with a single tangential array can extract key island parameters in a spherical tokamak, providing a computationally efficient alternative to conventional SXR tomography and providing a pathway toward real-time magnetic-island characterization in future devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Synthetic spectra for Lyman- α forest analysis in the Dark Energy Spectroscopic Instrument

Synthetic data sets are used in cosmology to test analysis procedures, to verify that systematic errors are well understood and to demonstrate that measurements are unbiased. In this work we describe the methods used to generate synthetic datasets of Lyman-α quasar spectra aimed for studies with the Dark Energy Spectroscopic Instrument (DESI). In particular, we focus on demonstrating that our simulations reproduces important features of real samples, making them suitable to test the analysis methods to be used in DESI and to place limits on systematic effects on measurements of Baryon Acoustic Oscillations (BAO). We present a set of mocks that reproduce the statistical properties of the DESI early data set with good agreement. Additionally, we use a synthetic dataset to forecast the BAO scale constraining power of the completed DESI survey through the Lyman-α forest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Intentional carbon doping reveals CH as an abundant charged impurity in nominally undoped synthetic WS 2 and WSe 2

Understanding the physical properties and controlling the generation of intrinsic and extrinsic defects is central to the technological adoption of 2D materials in devices. Here we identify a charged carbon-hydrogen complex at a chalcogen site (CH X ) as a common, charged impurity in synthetically grown transition metal dichalcogenides (TMDs). This conclusion is drawn by comparing high resolution scanning probe microscopy measurements of nominally undoped and intentionally carbon doped TMD samples. While CH impurity densities in undoped CVD-grown WS 2 and MOCVD-grown WSe 2 can range anywhere from parts per million to parts per thousand, CH densities in the percentage levels were selectively generated by a post-synthetic methane plasma treatment. Our study indicates that methane plasma treatment is a selective and clean method for the controlled introduction of a charged carbon-hydrogen complex at a surface chalcogen site, adefect that is commonly present in synthetic TMDs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of the impact of broad absorption lines on quasar redshift measurements with synthetic observations

ABSTRACT Accurate quasar classifications and redshift measurements are increasingly important to precision cosmology experiments. Broad absorption line (BAL) features are present in 15–20 per cent of all quasars, and these features can introduce systematic redshift errors, and in extreme cases produce misclassifications. We quantitatively investigate the impact of BAL features on quasar classifications and redshift measurements with synthetic spectra that were designed to match observations by the Dark Energy Spectroscopic Instrument (DESI) survey. Over the course of 5 yr, DESI aims to measure spectra for 40 million galaxies and quasars, including nearly three million quasars. Our synthetic quasar spectra match the signal-to-noise ratio and redshift distributions of the first year of DESI observations, and include the same synthetic quasar spectra both with and without BAL features. We demonstrate that masking the locations of the BAL features decreases the redshift errors by about 1 per cent and reduces the number of catastrophic redshift errors by about 80 per cent. We conclude that identifying and masking BAL troughs should be a standard part of the redshift determination step for DESI and other large-scale spectroscopic surveys of quasars.

(galaxies:) quasars: absorption↗
DOE OSTI2022

Quantum membrane phases in synthetic lattices of cold molecules or Rydberg atoms

Here, we calculate properties of dipolar interacting ultracold molecules or Rydberg atoms in a semisynthetic three-dimensional configuration—one synthetic dimension plus a two-dimensional real-space optical lattice or periodic microtrap array—using the stochastic Green's function quantum Monte Carlo method. Through a calculation of thermodynamic quantities and appropriate correlation functions, along with their finite-size scalings, we show that there is a second-order transition to a low-temperature phase in which two-dimensional “sheets” form in the synthetic dimension of internal rotational or electronic states of the molecules or Rydberg atoms, respectively. Simulations for different values of the interaction V, which acts between atoms or molecules that are adjacent both in real and synthetic space, allow us to compute a phase diagram. We find a finite-temperature transition at sufficiently large V as well as a quantum phase transition—a critical value V c below which the transition temperature vanishes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗
DOE OSTI2022

SING (Synthetic dIstribution Network Generator) [SWR-22-57]

Synthetic dIstribution Network Generator is a standalone python module that is able to create synthetic distribution models for OpenDSS using GIS datasets. The software uses road and building information from OpenStreetMaps to generate these synthetic models.

Latif, Aadil↗
DOE OSTI2023

Assessing North Atlantic Tropical Cyclone Rainfall Hazard Using Engineered-Synthetic Storms and a Physics-Based Tropical Cyclone Rainfall Model

In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Synthetic method of analogues for emerging infectious disease forecasting

The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Leveraging a synthetic biology approach to enhance BCG-mediated expansion of Vγ9Vδ2 T cells

There is an urgent need to develop a more efficacious anti-tuberculosis vaccine as the current live-attenuated vaccine strain BCG fails to prevent pulmonary infection in adults. In this study, we leverage a synthetic biology approach to engineer BCG to produce more (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP), an intermediate of bacterial—but not host—isoprenoid biosynthesis via the methylerythritol phosphate (MEP) pathway. HMBPP strongly activates and expands Vγ9Vδ2 T cells, which are unique to higher-order primates and protect against Mycobacterium tuberculosis infection. BCG has been engineered to produce specific ligands and antigens to some success; in contrast, our strategy exploits a self-nonself recognition mechanism in the host via HMBPP sensing, which has not been attempted before. To inform the design of our recombinant strains, we performed synteny analyses of >63 mycobacterial species and found that isoprenoid biosynthetic genes are not operonic across all the 356 surveyed genomes, but some genes are frequently found in pairs. Thus, we generated synthetic loci with the goal of specifically overproducing HMBPP and tested the ability of these engineered strains to induce human Vγ9Vδ2 expansion in an in vitro stimulation assay. We found that BCG expressing a synthetic MEP locus significantly enhanced Vγ9Vδ2 T cell expansion over the wild-type vaccine strain, and overexpression of the HMBPP synthase GcpE alone potently induced Vγ9Vδ2 T cell expansion with no downregulation of other pathway genes. Together these engineered strains present two successful strategies to accumulate HMBPP and overcome feedback inhibition of the MEP pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology (Final Technical Report)

The 5th International Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology will be held virtually from November 15 to 17, 2021. The meeting is organized by the American Institute of Chemical Engineers (AIChE), which has an excellent and long-standing record convening scientific conferences and events. This conference has been held annually since 2016 and it is expected to as successful as previous conferences. The planned sessions featured BER-relevant topics including mew technologies, bioenergy, bioproducts and materials, plant metabolic engineering, crops for future climates, and biosafety. The event brought together scientists and engineers from universities, industry, and government working in all aspects of plant synthetic biology, plant bioengineering and plant biotechnology. The conference provided a forum for leading scientists to interact with new investigators and students and engage in discussions on the latest scientific advances and technology developments in plant synthetic biology and genome engineering. The organizing committee and meeting participants include diverse members of the BER research community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Complex-Valued Signal Denoising and Bayesian Optimization for Detection of Synthetic Opioids [Slides]

Overseas manufacturers ship synthetic opioids into the United States through international mail. Synthetic opioids are largely responsible for the overdose crisis in the United States. Nuclear Quadrupole Resonance (NQR) spectroscopy is a chemical analysis technique used for detection. The ultimate goal is to develop a technology capable of detecting the presence of synthetic opioids in unopened packages using NQR spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

6 th International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology (Workshop Report)

The objective of the project is to hold a Conference to bring together scientists and engineers from universities, industry and government working in all aspects of plant synthetic biology, plant bioengineering and plant biotechnology. To meet this objective, AIChE will host the 6th International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology on December 9-11, 2022 in Fort Lauderdale, Florida. The conference will feature five technical sessions: (1) Genome and Epigenome Editing, (2) Specialized Metabolism, (3) Engineering Plant Resilience, (4) Engineering more efficient plants, and (5) New tools and Technologies. The conference will allow leading scientists and engineers from universities, industry and government to discuss new tools and technologies being developed as well as their applications, and gaps in the field. The conference will allow students to learn about these new technologies, engage in a discussion with professionals in their field of interest, and showcase their work. The scope, theme, and aims of the Conference on Plant Synthetic Biology, Bioengineering, and Biotechnology fall squarely within the mission of DOE which includes securing US leadership in energy technology and to maintaining a vibrant US effort in science and engineering as a cornerstone for our economic prosperity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SyPro Poplar: Improving Poplar Biomass Production under Abiotic Stress Conditions: an Integrated Omics, Bioinformatics, Synthetic Biology and Genetic Engineering Approach (Final Report)

SyPro Poplar: Improving Poplar Biomass Production under Abiotic Stress Conditions: an Integrated Omics, Bioinformatics, Synthetic Biology and Genetic Engineering Approach In the SyPro Poplar project, we aimed to integrate omics, bioinformatics, synthetic biology, and genetic engineering approaches to develop transgenic poplar trees with sustained photosynthetic activity and increased biomass production under individual and the simultaneous occurrence of water deficit, increased soil salinity, and elevated temperatures. Specifically, we intended to (i) study the functions of selected stress-responsive genes at tissue and cell type-specific levels, (ii) discover novel motifs and construct stress-responsive synthetic promoters, and (iii) use these promoters to drive the expression of genes shown to confer abiotic stress tolerance in a variety of crops and develop abiotic stress-tolerant poplars in a coordinated fashion.

09 BIOMASS FUELS↗
DOE OSTI2024

Utilizing Physics-Informed Synthetic Data to Train a Digital Twin for Predicting Reactor Operations

Understanding techniques to strengthen the nuclear nonproliferation regime is crucial in reducing the creation of nuclear weaponry on the basis of advancements in the nuclear energy industry. Prior to construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant’s reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. The following research conducted serves as a validation to training a digital twin on synthetic data fabricated via means of reactor physics simulations based on parameters of Idaho State University’s AGN-201 reactor. The synthetic data is utilized to train a long short-term memory (LSTM) recurrent neural network model. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and if these methods should be used in the future to inform inspectors of a reactor’s proliferation capabilities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Engineering Synthetic Anaerobic Consortia Inspired by the Rumen for Biomass Breakdown and Conversion

Lignocellulosic plant biomass is a widely-abundant renewable resource that can be harnessed for value-added production of fuels & chemicals. While microbes have been engineered to breakdown lignocellulose and turn released sugars into products, this remains an energy-intensive process that requires expensive pre-treatment and separation steps. Furthermore, it is difficult to engineer all desirable traits for breakdown and conversion into one organism. This project developed a new strategy that relies on microbial partnerships formed in the herbivore rumen to liberate sugars from crude plant biomass and convert that sugar to value-added chemicals. Microbial consortia consisting of fungi, bacteria, and archaea form tight associations in the herbivore rumen, which divide-and-conquer the difficult tasks of biomass breakdown. This project leveraged a “synthetic rumen” consortium composed of anaerobic fungi and chain-elongating bacteria to study which metabolites are shared and exchanged between microbes and identify strategies to bolster lignocellulose conversion to value-added products. Our approach developed high-throughput systems and synthetic biology approaches to realize stable synthetic consortia that route lignocellulosic carbon into short and medium chain fatty acids (SCFAs/MCFAs) rather than methane. Key research objectives were to (1) design and predict anaerobic fungal and bacterial consortia that efficiently convert lignocellulosic biomass into medium-chain fatty acids (MCFAs), (2) understand how fermentation parameters and microbe-microbe interactions regulate and drive microbiome metabolic fluxes, and (3) use genomic editing to alter the fermentation byproducts of anaerobic fungi and bolster MCFA titers and yields.

09 BIOMASS FUELS↗