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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (M pro ) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC 50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for M pro -ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to M pro . Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Synthesis of high-density olivine LiFePO 4 from paleozoic siderite FeCO 3 and its electrochemical performance in lithium batteries

The lithium-ion cathode material olivine LiFePO 4 (LFP) has been synthesized for the first time from natural paleozoic iron carbonate (FeCO 3 ). The ferrous carbonate starting material consists of the mineral siderite at about 92 wt. % purity. Because FeCO 3 has divalent iron, the reaction with lithium dihydrogen phosphate (LiH 2 PO 4 ) provides a unique method to develop iron-(II) containing LFP in an inert atmosphere. Since siderite FeCO 3 is a common mineral that can be directly mined, it may, therefore, provide an inexpensive route for the production of LFP. After carbon-coating, the LFP yields a capacity in the range of 80–110 mAh g -1 LFP (in one chosen specimen sample), which is lower than commercially available LiFePO 4 (150–160 mAh g -1 LFP ). However, the tap density of LFP derived from siderite is noticeably high at 1.65 g cm -3 . The material is likely to be improved with powder purification, nanosized processing, and more complete carbon-coating coverage with increased optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

How to design a focusing guide: The large moderator case1

As continuously shaped super-mirrors are becoming available, the conceptual design of focusing guides should explore a wider range of possibilities to accomplish an efficient neutron beam extraction. Starting from a desired phase-space volume at the sample position and using an upstream ray-tracing approach, the acceptance diagram of any focusing guide can be calculated at the moderator position. To ensure high brilliance transfer and homogeneous coverage, the acceptance diagram should be fully included in the neutron source emission phase-space volume. Following this idea, the guide system can be scaled into dimensionless geometric figures that convey performance limits for a desired cross-section reduction. Moreover, if we impose a monotonic increase of the reflection angle with divergence angle at the sample position, the shape of the mirror is analytically determined. This approach was applied in the design of a focusing guide for SNAP instrument at SNS, at ORNL, USA. The results of McStas simulations are presented with different options included. Our approach facilitates finding an optimal solution for connecting multiple guide pieces to avoid excessive losses and ensure a homogeneous phase space coverage.

Frost, Matthew↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗
DOE OSTI2023

A minimum assumption approach to MEG sensor array design

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Thermal characterization of the build chamber in electron beam melting

Electron beam powder bed fusion, commonly termed electron beam melting (EBM), offers great versatility in multiple-part processes and can produce high quality as-built components due to low residual stresses. The EBM process is complex and requires careful thermal management, including uniform and consistent preheating of the powder bed, in order to ensure quality and consistency of the product. However, most of the simulations in the literature focus on the selective melting stage of the process. As of today, optimal conditions for initial pre-heating temperatures are only available for specific materials. Thus, in order to extend the EBM technology to other desired build materials, a much better understanding of the pre-heating stages is required. In this work, numerical and experimental approaches are combined in order to investigate the effects and sensitivities of heat removal from the build plate during EBM pre-heating stages. For this purpose, a carefully reconstructed numerical model of the build chamber of an ARCAM Q20 + machine is developed. It includes all main parts of the chamber and all relevant heat transfer mechanisms, whereas special attention is paid to radiation heat exchange between various bodies. In order to validate the model, dedicated experiments are performed, in which a system of thermocouples is installed in the build chamber, allowing direct measurement of the local temperatures of the start-plate and heat shields. A good agreement between the simulation and experimental findings is achieved, leading to a better understanding of the thermal processes characteristic to the pre-heating stages. This basic analysis is followed by a representative pre-heating case, where a powder bed is also considered. The energy required to obtain the desired pre-heating temperatures is evaluated, and the role of the powder bed in heat transfer within the chamber is assessed. The pre-heating stage, simulated in the present work, is supposed to create proper conditions for sintering and consequent melting of the powder, which are highly dependent on the local temperatures and heat transfer features. Thus, the findings of the reported work present a step towards a better understanding of the thermal processes that characterize EBM. The reported model can be further used to provide realistic boundary condition inputs for other meso- or macro-scale models as a function of time and geometry. The model can serve also for verification of machine settings, i.e., jump-safe and melt-safe ones which actually provide desired preheat, and for development of settings for new powders.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of Mission Profiles for Humidity Models in the Reliability Testing of PV Inverters: Preprint

To understand the impacts of humidity on PV inverters, mission profiles were developed to accurately describe the different processes and rates based on the environmental factors of temperature, relative humidity, and irradiance. The operating environment of the device introduces stress factors dependent on component temperature, relative humidity, and component voltage. The development of profiles started from looking at historical climate conditions at Cocoa, Florida, a hot and humid location. The profile developed from field location was compared with existing humidity test profiles in industrial and military standards to determine acceleration factors. Prior to the evaluation of the mission profiles, critical components in the PV inverter were identified based on vendor discussions along with failure modes of interest. The profiles considered in this study was demonstrated using a 4-kVA string inverter. The measurements from the devices were substituted in device manufacturer specified equation to determine the severity of each test. The severity of each test was compared against the severity of the 24-hour result to determine the acceleration factor of each test. The acceleration factors were determined to be over a range of 1.2-6 for the existing humidity tests from standards, when compared against the mission profile developed from field data. This optimal humidity/thermal profile will be passed to the IEC standards development for inverter qualification testing.

humidity models↗
DOE OSTI2026

Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search

Optimization problems become fundamentally challenging as the number of variables increases. Because the volume of the search space grows exponentially, classical algorithms frequently fail to locate the global minimum of non-convex functions. While quantum optimization offers a potential alternative, mapping continuous problems onto near-term quantum hardware introduces severe scaling limits and barren plateaus. To bridge this gap, we propose the Distributed Quantum-Enhanced Optimization (D-QEO) framework. Instead of forcing the quantum processor to find the exact minimum, we use it simply as a topographical preconditioner. The QPU maps the landscape to locate the most promising basin of attraction, generating high-quality seed points for a classical GPU-accelerated solver to refine. To make this approach viable for utility-scale problems, we exploit the mathematical structure of separable functions. This allows us to cut a 50-qubit (i.e., $2^{50}$) global search space into independent and manageable sub-spaces using 5-qubit subcircuits. By executing these fragments concurrently with CUDA-Q, we completely bypass the overhead of cross-register entanglement and classical tensor knitting for separable functions. Benchmarks on the 10-dimensional Rastrigin and Ackley functions show that D-QEO prevents the exponential failure rates observed in purely classical algorithms. Furthermore, this quantum warm-start significantly reduces the number of classical BFGS iterations required to converge, providing a highly practical blueprint for utilizing near-term quantum resources in complex global search.

Soos, Dominik [Old Dominion U.]↗
DOE OSTI2021

Morphology, microstructure, and doping behaviour: A comparison between different deposition methods for poly-Si/SiO x passivating contacts

In this work, we study how crystallographic structures, optoelectronic properties, and nanoscale surface morphologies of ex situ phosphorus-doped polycrystalline silicon (poly-Si)/SiO x passivating contacts, formed by different deposition methods (sputtering, plasma-enhanced chemical vapour deposition [PECVD], and low-pressure chemical vapour deposition [LPCVD]), are investigated and compared. Across all these deposition technologies, we noted the same trend: higher diffusion temperatures yield films that are more crystalline but that have rougher surface morphologies due to bigger surface crystal grains. Also, the recrystallization process of the as-deposited Si films starts from the SiO x interface, rather than from the film surface and bulk. However, there are some distinct differences among these technologies. First, the LPCVD method yields the lowest deposition rate, roughest surfaces, and smallest degree of crystallinity on finished poly-Si films. In contrast, the PECVD method has the highest deposition rate and smoothest surfaces for both as-deposited Si and annealed poly-Si films. Second, as-deposited sputtered and PECVD Si films contain only an amorphous phase, whereas as-deposited LPCVD films already has some crystalline phase. Third, the LPCVD phosphorus in-diffusion into the substrate depends strongly on the initial film thickness, whereas for the other two methods, it is weakly dependent on thickness. Finally, the passivation quality of every poly-Si film type has different responses to the film thickness and diffusion temperature, suggesting that the ex situ doping optimization should be performed independently.

14 SOLAR ENERGY↗
DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Origin of High Activity and Durability of Twisty Nanowire Alloy Catalysts under Oxygen Reduction and Fuel Cell Operating Conditions

The ability to control the surface composition and morphology of alloy catalysts is critical for achieving high activity and durability of catalysts for oxygen reduction reaction (ORR) and fuel cells. Here, this report describes an efficient surfactant-free synthesis route for producing a twisty nanowire (TNW) shaped platinum–iron (PtFe) alloy catalyst (denoted as PtFe TNWs) with controllable bimetallic compositions. PtFe TNWs with an optimal initial composition of ~24% Pt are shown to exhibit the highest mass activity (3.4 A/mg Pt , ~20 times higher than that of commercial Pt catalyst) and the highest durability (<2% loss of activity after 40000 cycles and <30% loss after 120000 cycles) among all PtFe-based nanocatalysts under ORR or fuel cell operating conditions reported so far. Using ex situ and in situ synchrotron X-ray diffraction coupled with atomic pair distribution function (PDF) analysis and 3D modeling, the PtFe TNWs are shown to exhibit mixed face-centered cubic (fcc)-body-centered cubic (bcc) alloy structure and a significant lattice strain. A striking finding is that the activity strongly depends on the composition of the as-synthesized catalysts and this dependence remains unchanged despite the evolution of the composition of the different catalysts and their lattice constants under ORR or fuel cell operating conditions. Notably, dealloying under fuel cell operating condition starts at phase-segregated domain sites leading to a final fcc alloy structure with subtle differences in surface morphology. Due to a subsequent realloying and the morphology of TNWs, the surface lattice strain observed with the as-synthesized catalysts is largely preserved. This strain and the particular facets exhibited by the TNWs are believed to be responsible for the observed activity and durability enhancements. These findings provide new insights into the correlation between the structure, activity, and durability of nanoalloy catalysts and are expected to energize the ongoing effort to develop highly active and durable low-Pt-content nanowire catalysts by controlling their alloy structure and morphology.

25 ENERGY STORAGE↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2020

Development of Methods for In-line Monitoring of Yield Stress During the Transfer of Radioactive Waste - 20435

Radioactive waste slurries at the Savannah River Site (SRS) and Hanford often behave as non-Newtonian fluids with a yield stress. The yield stress increases the size of equipment needed to transport or mix these slurries and can increase the risk of process upsets. This property is often measured by collection of samples and transportation to a laboratory, which can increase the risk of personnel exposure and even change the rheology. In addition, slurry rheology has been observed to change with time. Real-time in-line monitoring (RTIM) of rheological properties of slurries can have substantial benefits to critical processes which are currently in operation or will be part of future plans in Department of Energy (DOE) complex processes. The need for an appropriate RTIM technology that can potentially minimize or eliminate numerous sampling operations was recently identified. This research at Florida International University focuses on addressing the need for a reliable RTIM technology at US Department of Energy sites. The objective of this work is to investigate and develop appropriate and reliable technologies that can perform accurate and easy measurements of yield stress with minimal changes to the flow conditions at US Department of Energy sites. Use of ultrasound in longitudinal and shear modes was considered as a method for monitoring of yield stress variations with no disturbance to the flow. Measurement of shear wave velocity (in shear mode) and monitoring of sound velocity variations with start of flow from rest (longitudinal mode) are the underlying principles. Measurement of pressure loss and liquid rise (due to static differential pressure) during flow of slurries were two other methods that were considered and investigated. In addition, analysis of stability depth of a penetrometer in slurry medium (penetrometer method) revealed a potential suitability. This paper presents efforts associated with investigation of different methods mentioned earlier for kaolin-water mixtures at different concentrations. Theoretical analysis showed that all methods under investigation were capable of monitoring yield stress in a desirable range of 0 to 70 pascal. A bench scale test setup was constructed for investigation of pressure loss and liquid rise methods. Simulants at different concentrations were created for initial testing campaigns. For ultrasonic testing, effects of yield stress on sound propagation speed in longitudinal and shear modes were investigated for simulants placed inside PVC pipes. Parameters such as simulant concentration and pipe diameter were varied to find optimal values. It was found that spacing (or medium thickness) of 1/8 inch or less was necessary to observe echoes with sufficient strength. In addition, a significant effect of yield stress on sound propagation velocity was observed. For the liquid rise method, kaolin-water simulant at 1.17 specific gravity was pumped through a 1/4 inch pipe and liquid height was measured in a vertical riser branching off a three-way connection. Static gauge pressure was varied within [0.3 to 1.1] psi range by increase of pumping pressure and flow. Results showed consistent yield stress data within 10% deviation from values reported by a rheometer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Early experiences on the OLCF Frontier system with AthenaPK and Parthenon–Hydro

The Oak Ridge Leadership Computing Facility (OLCF) has been preparing the nation's first exascale system, Frontier, for production and end users. Frontier is based on HPE Cray's new EX architecture and Slingshot interconnect and features 74 cabinets of optimized 3rd Gen AMD EPYC CPUs for HPC and AI and AMD Instinct 250X accelerators. As a part of this preparation, “real-world” user codes have been selected to help assess the functionality, performance, and usability of the system. This article describes early experiences using the system in collaboration with the Hamburg Observatory for two selected codes, which have since been adopted in the OLCF test harness. Experiences discussed include efforts to resolve performance variability and per-cycle slowdowns. Results are shown for a performance portable astrophysical magnetohydronamics code, AthenaPK, and a mini-application stressing the core functionality of a performance portable block-structured adaptive mesh refinement framework, Parthenon-Hydro. Here, these results show good scaling characteristics to the full system. At the largest scale, the Parthenon-Hydro miniapp reaches a total of $1.7$ $\times$ $10^{13}$ zone-cycles/s on 9216 nodes (73,728 logical GPUs) at ≈92% weak scaling parallel efficiency (starting from a single node using a second-order, finite-volume method).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell's survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. In this work, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13 C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Controlling homogenization length scales and microstructure in additively manufactured Ti-Ta functionally graded materials

Materials with smooth compositional gradients or functionally grade materials (FGMs) produced via additive manufacturing (AM), enables joining dissimilar materials and optimizing multiple properties in advanced engineering applications. However, as-printed AM microstructures exhibit micro-segregation and solidification defects which, when combined with controlling macroscale gradient properties, complicates necessary post-processing. Here, we use CALPHAD-informed diffusion modelling to design post-processing heat treatments for lightweight to refractory FGMs. Ti-Ta (0 to 85 at. % Ta) FGMs were fabricated using laser-based directed energy deposition AM. Post-processing heat treatments at 1000° C and 1500° C were designed to promote homogenization across specific length scales and experimentally validated. Investigation of chemical segregation and microstructures demonstrated that the length scale of homogenization is controlled as a function of time, temperature, and local composition. Ta-rich regions exhibited incomplete homogenization compared to Ti-rich layers. Unmelted Ta particles were found to completely dissolve at 1500 °C. By controlling cooling rate (200 °C/min), martensitic structures were produced between 14–36 at. % Ta, consistent with martensite-start temperatures calculations, while furnace cooling (2 °C/min) produced α+β morphologies. This work establishes a validated predictive framework for designing post-processing to tailor microstructure and chemical architecture in AM FGMs, facilitating their deployment in demanding environments.

Materials science↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗