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Results for “Synthetic acceleration”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering

Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.

Chen, Junyu [Univ. of Illinois at Urbana-Champaign↗
DOE OSTI2022

Narrowing the Phase Distribution of Quasi-2D Perovskites for Stable Deep-Blue Electroluminescence

Solution-processed quasi-2D perovskites contain multiple quantum wells with a broad width distribution. Inhomogeneity results in the charge funneling into the smallest bandgap components, which hinders deep-blue emission and accelerates Auger recombination. Here, a synthetic strategy applied to a range of quasi-2D perovskite systems is reported, that significantly narrows the quantum well dispersity. It is shown that the phase distribution in the perovskite film is significantly narrowed with controlled, simultaneous evaporation of solvent and antisolvent. Modulation of film formation kinetics of quasi-2D perovskite enables stable deep-blue electroluminescence with a peak emission wavelength of 466 nm and a narrow linewidth of 14 nm. Light emitting diodes using the perovskite film show a maximum luminance of 280 cd m –2 at an external quantum efficiency of 0.1%. This synthetic approach will serve in producing new materials widening the color gamut of next-generation displays.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Polymer Hole Transport Material Functional Group Tuning for Improved Perovskite Solar Cell Performance

As lead halide perovskites (LHPs) continue to achieve success as a light-harvesting material in perovskite solar cells (PSCs), exploring and understanding other materials in the device stack become increasingly important. Particularly, selection of suitable hole transport materials (HTMs) that demonstrate high performance and stability is imperative in the design of P-I-N PSCs. Presented here are a family of 12 structurally related polymers based on either fluorene or carbazole main chains with select aromatic side groups that introduce tunable properties for use in PSCs. How properties such as the highest occupied molecular orbital energy level, conductivity, glass-transition temperature, and wettability of the HTM affect the PSC performance is explored. Devices that incorporate the polymer HTMs perform well relative to PTAA in benchmark P-I-N PSC architectures while exhibiting similar or superior stability under accelerated aging studies. The relative synthetic simplicity and resultant performance of the HTMs in PSCs coupled with the ability to customize properties with different functional groups demonstrates the potential of this family of HTMs for a variety of LHP materials.

14 SOLAR ENERGY↗
DOE OSTI2020

Generation and evaluation of synthetic patient data

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗
DOE OSTI2020

Modular cell-free expression plasmids to accelerate biological design in cells

Industrial biotechnology aims to produce high-value products from renewable resources. This can be challenging because model microorganisms—organisms that are easy to use like Escherichia coli—often lack the machinery required to utilize desired feedstocks like lignocellulosic biomass or syngas. Non-model organisms, such as Clostridium, are industrially proven and have desirable metabolic features but have several hurdles to mainstream use. Namely, these species grow more slowly than conventional laboratory microbes, and genetic tools for engineering them are far less prevalent. To address these hurdles for accelerating cellular design, cell-free synthetic biology has matured as an approach for characterizing non-model organisms and rapidly testing metabolic pathways in vitro. Unfortunately, cell-free systems can require specialized DNA architectures with minimal regulation that are not compatible with cellular expression. In this work, we develop a modular vector system that allows for T7 expression of desired enzymes for cell-free expression and direct Golden Gate assembly into Clostridium expression vectors. Utilizing the Joint Genome Institute’s DNA Synthesis Community Science Program, we designed and synthesized these plasmids and genes required for our projects allowing us to shuttle DNA easily between our in vitro and in vivo experiments. We next validated that these vectors were sufficient for cell-free expression of functional enzymes, performing on par with the previous state-of-the-art. Lastly, we demonstrated automated six-part DNA assemblies for Clostridium autoethanogenum expression with efficiencies ranging from 68% to 90%. We anticipate this system of plasmids will enable a framework for facile testing of biosynthetic pathways in vitro and in vivo by shortening development cycles.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Genetic and molecular dosimetry of HZE radiation (US-1 RADIAT)

In order to estimate radiation exposure in space, experiments were conducted during the 1st International Microgravity Laboratory (IML-1) mission in order to isolate genetic changes in animal cells caused by cosmic rays. The space measurements were evaluated against results from synthetic cosmic rays produced by particle accelerators on the ground. The biological material used was the tiny soil nematode, Caenorhabditis elegans. The measurements were made by thermoluminescent detectors and plastic nuclear track detectors. The development and the chromosome mechanics in microgravity were studied, and the mutagenesis induced by radiation exposure was analyzed. The results showed that there are no obvious differences in the development, behavior and chromosome mechanics, as a function of gravity unloading (reproduction, self-fertilization and mating of males with hermaphrodites, gross anatomy, symmetry and gametogenesis, pairing, disjoining and recombination of chromosomes). A variety of mutants were isolated, and it was noted that mutants isolated from regions of identified high particles were more severely affected than those isolated by random screening. Linear energy transfer particles seem to favor large scale genetic lesions.

Nelson, Gregory A.↗
DOE OSTI2020

Biosystems Design to Accelerate C 3 -to-CAM Progression

Global demand for food and bioenergy production has increased rapidly, while the area of arable land has been declining for decades due to damage caused by erosion, pollution, sea level rise, urban development, soil salinization, and water scarcity driven by global climate change. In order to overcome this conflict, there is an urgent need to adapt conventional agriculture to water-limited and hotter conditions with plant crop systems that display higher water-use efficiency (WUE). Crassulacean acid metabolism (CAM) species have substantially higher WUE than species performing C 3 or C 4 photosynthesis. CAM plants are derived from C 3 photosynthesis ancestors. However, it is extremely unlikely that the C 3 or C 4 crop plants would evolve rapidly into CAM photosynthesis without human intervention. Currently, there is growing interest in improving WUE through transferring CAM into C 3 crops. However, engineering a major metabolic plant pathway, like CAM, is challenging and requires a comprehensive deep understanding of the enzymatic reactions and regulatory networks in both C 3 and CAM photosynthesis, as well as overcoming physiometabolic limitations such as diurnal stomatal regulation. Recent advances in CAM evolutionary genomics research, genome editing, and synthetic biology have increased the likelihood of successful acceleration of C 3 -to-CAM progression. Here, we first summarize the systems biology-level understanding of the molecular processes in the CAM pathway. Then, we review the principles of CAM engineering in an evolutionary context. Lastly, we discuss the technical approaches to accelerate the C 3 -to-CAM transition in plants using synthetic biology toolboxes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Cross-correlation analysis of on-orbit residual accelerations in Spacelab

To characterize the low-gravity environment within orbiting space laboratories, researchers are operating sensitive accelerometer systems. Transient accelerations recorded in spacecraft can have magnitudes up to 0.01 g over a wide range of frequencies. These accelerations have components in the 0.001-100-Hz range no greater in magnitude than 0.001 g. Some experiments can be sensitive to such accelerations, so investigators are interested in identifying causal relationships between accelerations and experimental results. Here, two forms of synthetic experiment data created using Spacelab 3 accelerometer data are evaluated using cross-correlation analysis. The results show that the use of cross-correlation techniques is appropriate, for specific experiment classes, for assessing relationships between both transient and oscillatory residual accelerations and experimental results recorded in a low-gravity environment.

Rogers, Melissa J. B.↗
NASA NTRS2001

Complex vestibular macular anatomical relationships need a synthetic approach

Mammalian vestibular maculae are anatomically organized for complex parallel processing of linear acceleration information. Anatomical findings in rat maculae are provided in order to underscore this complexity, which is little understood functionally. This report emphasizes that a synthetic approach is critical to understanding how maculae function and the kind of information they conduct to the brain.

Non-NASA Center↗
DOE OSTI2026

ML-Shock-Time-Series-Synthesis

Open-source machine learning tools for GPU-batched synthetic shock time-series generation, GPU-accelerated batched Shock Response Spectrum (SRS) computation, and standardized benchmark datasets.

Watts, Adam↗
DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2025

Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime distribution of target HPC applications, and the costs associated with different search orchestration mechanisms that have been overlooked in previous studies. Compared to sequential BO, our novel methodology achieves comparable quality while demonstrating robust scalability in search time as the amount of parallel resources increases; it also outperforms a state-of-the-art tuner, which supports parallelization, achieving up to 3.67x faster search time. We provide high-value insights for practitioners seeking to leverage the power of parallel computing for efficient HPC application tuning. Additionally, to further assist researchers in accelerating the performance tuning of their HPC applications, we provide an extension of an existing open-source tuning framework that incorporates our methods.

Bayesian optimization↗
NASA NTRS2012

Radiation from Accelerated Particles in Shocks and Reconnections

Plasma instabilities are responsible not only for the onset and mediation of collisionless shocks but also for the associated acceleration of particles. We have investigated particle acceleration and shock structure associated with an unmagnetized relativistic electron-positron jet propagating into an unmagnetized electron-positron plasma. Cold jet electrons are thermalized and slowed while the ambient electrons are swept up to create a partially developed hydrodynamic-like shock structure. In the leading shock, electron density increases by a factor of about 3.5 in the simulation frame. Strong electromagnetic fields are generated in the trailing shock and provide an emission site. These magnetic fields contribute to the electrons transverse deflection and, more generally, relativistic acceleration behind the shock. We have calculated, self-consistently, the radiation from electrons accelerated in the turbulent magnetic fields. We found that the synthetic spectra depend on the Lorentz factor of the jet, its thermal temperature and strength of the generated magnetic fields. Our initial results of a jet-ambient interaction with anti-parallelmagnetic fields show pile-up of magnetic fields at the colliding shock, which may lead to reconnection and associated particle acceleration. We will investigate the radiation in a transient stage as a possible generation mechanism of precursors of prompt emission. In our simulations we calculate the radiation from electrons in the shock region. The detailed properties of this radiation are important for understanding the complex time evolution and spectral structure in gamma-ray bursts, relativistic jets, and supernova remnants.

Nishikawa, K. I.↗
DOE OSTI2024

Parametric reduced-order modeling for component-oriented treatment and localized nonlinear feature inclusion

Abstract We propose coupling a physics-based reduction framework with a suited response decomposition technique to derive a component-oriented reduction (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over a domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes to capture localized features while additionally relying on reduced modes of a global nature to approximate the system’s dynamics accurately. The global modes are derived from a linear monolithic system, defined as a result of a coordinate separation scheme, which permits the proposed COR-ROM to naturally couple the response between linear and nonlinear subdomains. The derived low-order representation utilizes a proper orthogonal decomposition projection and is additionally reinforced with the inclusion of a hyper-reduction technique to capture the underlying high-fidelity model response while providing accelerated computations. The resulting approach is exemplified in the synthetic case studies of a four-story shear frame with multiple nonlinear regions driven by hysteresis and a large-scale kingpin connection featuring plasticity.

Vlachas, Konstantinos (ORCID:000000029124219X)↗
DOE OSTI2023

Establishing a versatile toolkit of flux enhanced strains and cell extracts for pathway prototyping

Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, in this study, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Creep Testing of Vectran Yarn

NASA and industry are developing inflatable space structures to provide deployable habitable volumes for future exploration missions in space and on the surface of the Moon and Mars. These structures must withstand the constant pressure loading of multi-year missions. These missions therefore require excellent creep resistance in the constituent cordage and webbings that provide the primary structure of the vessel, which is typically constructed using high-strength synthetic materials such as Vectran. Real-time and accelerated creep testing of Vectran webbings has been performed at NASA Langley Research Center (LaRC), however, acceptable correlation between the results of the two approaches has not yet been achieved. Given the additional complexity of the webbing architecture, creep testing at the yarn level is being studied to reduce the number of interacting properties and focus on the core creep behavior to see if better correlation can be achieved between real-time and accelerated tests. To achieve this objective a creep test stand that is able to support simultaneous testing of five Vectran yarns had been previously designed and built. The test stand utilizes two clamp-wrap grips to mount the yarn and has a 10:1 load ratio, via a lever-arm, henceforth, known as the lever-arm test stand. Real-time creep testing (without measuring the strain of the yarn) in this fixture produces yarn ruptures in the gauge section. However, a large scatter in the times to failure was seen in the results across multiple loading conditions. For example, a test at 70% of the ultimate tensile strength (UTS) was found to have specimens that failed within a matter of hours and others that took over 6 weeks to fail. Prior test data on Vectran webbings had indicated high variability in the material strength, leading to higher than average scatter in the creep testing of webbings, which could translate to the scatter in creep behavior at the yarn level as well. However, there was still the possibility that the lever-arm test stand was causing the high scatter in data; specifically, due to potential variability in the loading of each specimen. Therefore, an additional creep test setup was designed and built to attempt to determine if the large scatter in the results was due to inherent material properties, the lever-arm test stand, or both. The newly designed creep test stand, henceforth known as the conventional test stand, utilized the more typical method for creep testing which involved hanging a weight from the yarn with no additional load amplification. The conventional test stand was designed and fabricated in-house at LaRC. High speed cameras were used to validate that the specimens in the conventional test stand failed in the gauge section. Once it was determined that the conventional test stand would provide valid failures in the gauge section, creep tests on the Vectran yarns were run to compare to the lever-arm test stand results. Preliminary results for a 70% UTS creep test have some specimens failing within a number of hours and some failing in excess of 6 weeks. These results are similar to those seen on the lever-arm test stand, pointing towards a large variability inherent in the material. The poster/presentation will focus on the design, fabrication, and validation of the conventional test stand as well as present preliminary results comparing the creep data from the conventional and lever-arm test stands

creep testing↗