Engineering Papers⌕ Search

SEARCH · Engineering Papers

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

New Opportunities for Neutrons in Environmental and Biological Sciences

The use of neutron methods in environmental and biological sciences is rapidly emerging and accelerating with the development of new instruments at neutron user facilities. This article, based on a workshop held at Oak Ridge National Laboratory (ORNL), offers insights into the application of neutron techniques in environmental and biological sciences. Here we highlight recent advances and identify key challenges and potential future research areas. These include soil and rhizosphere processes, root water dynamics, plant-microbe interactions, structure and dynamics of biological systems, applications in synthetic biology and enzyme engineering, next-generation bioproducts, biomaterials and bioenergy, nanoscale structure, and fluid dynamics of porous materials in geochemistry. We provide an outlook on emerging opportunities with an emphasis on new capabilities that will be enabled at the Spallation Neutron Source Second Target Station currently under design at ORNL. The mission of scientific neutron user facilities worldwide is to enable science using state-of-the-art neutron capabilities. We aim to encourage researchers in the environmental and biological research community to explore the unique capability afforded by neutrons at these facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗
DOE OSTI2026

Aviation Fuel Characterization at Operationally Relevant Conditions

To accelerate approval and potentially expand the allowable property range for aviation fuels, we are using high performance computing simulations to reveal fuel property effects on aviation combustor performance. These simulations are supported by fuel property measurements over temperatures and pressures that the fuel experiences in an aircraft engine and by validated chemical kinetics models for SAF combustion. Here we report density, viscosity, and surface tension results for conventional jet fuel and multiple synthetic fuels from -30 degrees Celsius to 200 degrees Celsius (-40 degrees Celsius for viscosity) and 1 atm to 70 atm including an assessment of method repeatability. Properties of surrogate mixtures are also investigated. Distillation, ICN, LHV, flashpoint, and Cp are also reported, and data are being used to develop models to predict fuel properties from composition (GCxGC).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Allometric Trophic Networks From Individuals to Socio-Ecosystems: Consumer–Resource Theory of the Ecological Elephant in the Room

A well-known parable is that of the blind men studying an elephant each of which assert the elephant is the part they first hold in their hands, e.g., “rope!” says the tail holder while the leg holder asserts “tree!” The various subdisciplines of ecology appear similar in that we each engage in our enthusiastic but at least somewhat myopic study with remarkably limited agreement or even discussion about the overall system which we all study. Allometric trophic network (ATN) theory offers a path out of this dilemma by integrating across scales, taxa, habitats and organizational levels from physiology to ecosystems based on consumer-resource interactions among co-existing organisms. The network architecture and the metabolic and behavioral processes that determine the structure and dynamics of these interactions form the first principles of ATN theory, which in turn provides a synthetic overview and powerfully predictive framework for ecology from organisms to ecosystems. Beyond ecology, ATN theory also synthesizes eco-evolutionary and socio-ecological research still largely based on consumer-resource mechanisms but respectively integrated with different processes including natural selection and market mechanisms. This paper briefly describes foundations, advances, and future directions of ATN theory including predicting an ecosystem’s phenotype from its community’s genotype in order to accelerate more predictive and unified understanding of the complex systems studied by ecologists and other environmental scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗
DOE OSTI2025

Designing Cyclic Nitrogen‐Bridged Sulfonamides with Anti‐Cancer Activity

The N-bridgehead heterocyclic structure is an abundant motif in a multitude of natural products. This structural feature is of high interest because it is present in many different bioactive molecules, many of which are well-established pharmaceuticals. The introduction of a sulfone group into the N-bridgehead system yields a new core structure containing a N-bridgehead sulfonamide. While linear sulfonamides can be found in natural products, only artificial cyclic sulfonamides are known to date. Applications of related cyclic sulfonamide compounds include matrix metalloproteinase inhibitors, potential HIV and cancer therapeutics, and anti-inflammatory compounds. To explore the potential bioactivity of the N-bridgehead sulfonamide scaffold, a synthetic route toward these scaffolds is developed and their bioactivity is explored against different cancer cell lines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Technology Transfer Excellence Award NTXBio

While a scientist at Los Alamos National Laboratory, Dr. Alex Koglin developed two compounds to create a better vaccine for Tuberculosis (TB). In 2015, Dr. Koglin took an entrepreneurial leave of absence from the Laboratory to start NTXBio, LLC. His objective was to develop and commercialize a TB vaccine based on two compounds he created while a scientist at the Los Alamos. In 2018, a total of 1.5 million people died from TB. According to the World Health Organization TB is the leading infectious disease resulting in death worldwide. Approximately one-quarter of the world’s population is infected with mycobacterium tuberculosis, the bacteria that causes TB. Dr. Koglin non-exclusively licensed the two compounds from Los Alamos and began further development of the technology in collaboration with the Government of South Africa and universities to address the need for a vaccine to prevent TB. In their research and development of the compounds there was no evidence of cross resistance between other bacteria and the bacteria that causes TB. The compounds were tested in mouse models and demonstrated they can treat TB. Also, the tests have not shown any of the massive side effects that the current treatments for TB are showing. The company started production of the compounds using current manufacturing processes. Using these processes NTXBio could not produce the compounds at a reasonable price to be available for patients in third world countries. The company shifted to developing a new methodology “invitro synthetic biology” that can increase the production of these compounds and other vaccine molecules in a more affordable way. Dr. Koglin’s startup NTXBio is working to positively influence and secure the world vaccine supply by developing rapid on-demand production of the full spectrum of protein vaccines. Starting with the TB vaccine and advancing to common childhood vaccines, emergency and experimental vaccines such as those needed for COVID-19. NTXBio is capable of accelerated prototyping and manufacturing with increased purity and greater stability while reducing both cost and production time. The newly developed production methodology can be applied to not only the TB compounds licensed from Los Alamos, but also to their compounds that advance vaccine production for other diseases. Their technology produces vaccines fast enough to be fully deployable in areas without the need of specialized storage conditions and response times.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Distinguishing photo-induced oxygen attack on alkyl chain versus conjugated backbone for alkylthienyl-benzodithiophene (BDTT)-based push–pull polymers

Synthetic design has enabled increasing power conversion efficiency advances in organic photovoltaics (OPV). One continuing knowledge gap is detailed understanding of single-material chemical (photo)stability. Many considerations are based on prior OPV-relevant donor homopolymer systems rather than the next-generation push–pull architectures. Generally, energetic offsets between the lowest occupied molecular orbital of the donor and molecular oxygen are assumed to dictate kinetics of photo-induced charge transfer to a super oxide radical. Herein we determine the ambient-induced photo-degradation pathways, presented as proposed site-specific arrow-pushing mechanisms, for five donor polymers all containing the same push unit – alkylthienyl-substituted-benzodithiophene (BDTT) – but with chemically distinct pull units. The donor-only polymer films were subject to controlled photobleaching in air as an accelerated degradation approach, coupled with simultaneous monitoring of absorptance. Sample subsets were periodically removed and analyzed with X-ray photoelectron spectroscopy, to evaluate near-surface chemical composition and oxygen additions to hetero reporter atoms on either the BDTT push unit or distinct pull unit (i.e., sulphur and nitrogen). Here, this methodology allows us to distinguish between different mechanisms of bond cleavage and formation. Overall, we find that neither polymer redox properties nor individual push or pull unit stability are sufficient to predict photo-oxidative degradation of these polymers. Rather, there is a greater dependence on the susceptibility of unique structural groups within a polymeric system. Alkyl chain oxygen addition is generally the first attack site and direct sulfur oxidation on the conjugated backbone occurs after saturation of the alkyl chain initiation sites. This work provides a standard that could be used to evaluate relative photo-oxidative (in)stability for new OPV materials quickly – prior to time-consuming device optimization – and demonstrates an effective methodology for correlating optical degradation with chemical structure alterations via spectroscopic signatures to guide synthetic design.

14 SOLAR ENERGY↗
DOE OSTI2022

Diagnosing the DARHT Electron Beam-Target Interaction and Hydrodynamic Expansion [Dissertation]

Quantitative electron beam-target interaction studies on the Dual-Axis Radiographic Hydrodynamic Test (DARHT) electron linear induction accelerators have only been performed recently. This includes characterization of the temperature, density, pressure, extent, and expansion velocity of the plasma plume. The results presented in this dissertation present a detailed and unified overview of the accelerator systems, target heating physics, beam transport, and diagnostic tools. Additional information includes calibration sources, radiation hydrodynamics, spectroscopic-quality radiation transport modeling, experimental measurements, and analyses of electron beam driven aluminum experiments. The first set of spatially and temporally resolved spectroscopic measurements of electron beam driven aluminum are presented. Contamination quantification analyses are used to understand the origin of the strong Na-I 3p-3s lines that are observed in absorption within the aluminum plasma continuum. These results inform the creation of the first spectroscopic-quality radiation transport model that links several atomic physics codes to interpret the conditions from which the Na-I lines originate. A good agreement is found between the surface analysis results and the model which confirms the concentration of the sodium present within the aluminum alloy foil material. It also demonstrates, for the first time on electron beam-driven target experiments, the ability to interpret plasma conditions from measured absorption lines. In a second experimental campaign which focuses on pure aluminum, the Al-I 3p-4s and 3p3d doublets are both measured in emission. A detailed analysis of the Al-I 3p-4s doublet reveals that the lines undergo moderate self-absorption. A simple model of the self-absorption effect is successfully used to match measured spectra at various temperature/density/plasma scale length combinations. These measurements led to the realization of the minimum density that can be resolved by the spectrometers for the Al-I 3p-4s lines due to the large slit width required to observe a signal on aluminum. These measurements also demonstrate the sensitivity of visible and long wave UV spectroscopy to minor changes in both temperature and density. The simple self-absorption model will be useful for analysis of other beam-target interaction experiments with spectra exhibiting either self-absorption or full self-reversal. Substantial headway has been made on the modeling front by linking together several codes needed to model both the energy deposition, hydrodynamic motion, and atomic kinetics to produce synthetic spectral calculations that are compared with experimental measurements. There exists ample space for improvement, especially with benchmarking the hydrodynamics codes and equation-of-state tables with experimental measurements. The X-ray diagnostics required to make these new measurements along with the simulation capabilities required to interpret the results are under development and will be the subject of future studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗
DOE OSTI2025

Neural refinement of sample weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Artificial neural networks↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Diagnostics of Magnetohydrodynamic Modes in the Interstellar Medium through Synchrotron Polarization Statistics

One of the biggest challenges in understanding magnetohydrodynamic (MHD) turbulence is identifying the plasma mode components from observational data. Previous studies on synchrotron polarization from the interstellar medium (ISM) suggest that the dominant MHD modes can be identified via statistics of Stokes parameters, which would be crucial for studying various ISM processes such as the scattering and acceleration of cosmic rays, star formation, and dynamo. In this paper, we present a numerical study of the synchrotron polarization analysis (SPA) method through systematic investigation of the statistical properties of the Stokes parameters. We derive the theoretical basis for our method from the fundamental statistics of MHD turbulence, recognizing that the projection of the MHD modes allows us to identify the modes dominating the energy fraction from synchrotron observations. Based on the discovery, we revise the SPA method using synthetic synchrotron polarization observations obtained from 3D ideal MHD simulations with a wide range of plasma parameters and driving mechanisms, and present a modified recipe for mode identification. We propose a classification criterion based on a new SPA+ fitting procedure, which allows us to distinguish between Alfvén mode and compressible/slow mode dominated turbulence. We further propose a new method to identify fast modes by analyzing the asymmetry of the SPA+ signature and establish a new asymmetry parameter to detect the presence of fast mode turbulence. Additionally, we confirm through numerical tests that the identification of the compressible and fast modes is not affected by Faraday rotation in both the emitting plasma and the foreground.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A flexible proton beam imaging energy spectrometer (PROBIES) for high repetition rate or single-shot high energy density (HED) experiments (invited)

The PROBIES diagnostic is a new, highly flexible, imaging and energy spectrometer designed for laser-accelerated protons. The diagnostic can detect low-mode spatial variations in the proton beam profile while resolving multiple energies on a single detector or more. When a radiochromic film stack is employed for “single-shot mode,” the energy resolution of the stack can be greatly increased while reducing the need for large numbers of films; for example, a recently deployed version allowed for 180 unique energy measurements spanning ~3 to 75 MeV with <0.4 MeV resolution using just 20 films vs 180 for a comparable traditional film and filter stack. When utilized with a scintillator, the diagnostic can be run in high-rep-rate (>Hz rate) mode to recover nine proton energy bins. Here, we also demonstrate a deep learning-based method to analyze data from synthetic PROBIES images with greater than 95% accuracy on sub-millisecond timescales and retrained with experimental data to analyze real-world images on sub-millisecond time-scales with comparable accuracy.

3D printing↗
DOE OSTI2021

Advanced manufacturing for electrosynthesis of fuels and chemicals from CO 2

Advanced manufacturing (AM) represents an appealing approach for creating novel electrochemical systems for chemicals synthesis. In this work, we demonstrate AM for rapid development and testing for improved performance for the carbon dioxide reduction reaction across an evolution of vapor-fed reactor designs. In our final design, we observe activation- and mixed-control regimes for a variety of operating conditions via inlet CO 2 flow rate and electrochemical potential. Furthermore, we define a dimensionless number (Da) to identify mass transport regimes by exploring the impact of hypothesized underlying mass transport mechanisms, including consumption of CO 2 via OH - , increased local temperatures, and partial penetration of electrolyte into gas diffusion layer. The accelerated pace of reactor design and development led to high geometric current densities (500 mA cm -2 ), heightened selectivity (85.5% FE C 2+ products), and increased carbon dioxide conversion (16.6%) and cathodic energy efficiency (49.6% CO 2 R). Using AM vapor-fed reactors, we attain high ethylene (3.67%) and record ethanol (3.66%) yields compared to the literature. Finally, this work underscores the promise of AM for accelerating reactor design, understanding of governing phenomena, and improving the performance of catalytic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis

Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.

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

Mixed-valence halide perovskites

Mixed-valence compounds—which feature an element in at least two different oxidation states—can display emergent optical and transport phenomena stemming from electron transfer between the different valences (intervalence charge-transfer; IVCT). As halide perovskites show promise as active materials in numerous optoelectronic devices, it is an opportune moment to incorporate and study the effects of mixed-valence in this versatile materials family, to access tunable electronic structures ranging from insulators to semiconductors to metals. Herein, we introduce the basic concepts of mixed-valence in molecules and discuss how these concepts may be extended to mixed-valence in extended solids. We then review the few studies of mixed valence in 3D and 2D halide perovskites and halide perovskites with mixed-valence impurities, ranging from studies in the early 1900s to the present day. In conclusion, through judicious choice of metal ion, its coordinating ligands and their geometry, and overall structural dimensionality, chemists can exert powerful synthetic control over electronic delocalization in mixed-valence perovskites, and we hope to see this intriguing materials class expand to encompass new compositions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗