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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Theoretical and experimental study on the O( 3 P) + 2,5-dimethylfuran reaction in the gas phase

In this work we report a joint experimental and computational study on the 2,5-dimethylfuran oxidation reaction in the gas phase initiated by atomic oxygen O( 3 P). The experiments have been performed by using vacuum-ultraviolet synchrotron radiation at the Advanced Light Source (ALS) of the Lawrence Berkeley National Laboratory (LBNL), at a temperature of 550 K and a pressure of 8 Torr. The experimental data were supported by quantum-chemical calculations along with a kinetic model, also taking into account the possible involvement of different magnetic states, performed in the framework of the RRKM theory. Propyne, acetaldehyde, methylglyoxal, dimethylglyoxal, 3-penten-2-one, 2,5-dimethylfuran-3(2 H )-one, and 1,2-diacetyl ethylene have been identified as the main primary products arising under the conditions of the experiment. Our computational model suggests that these species can be formed at the concentration and branching ratio experimentally observed only in the presence of a non-negligible fraction of non-thermalized intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Type Ia supernovae deflagration-to-detonation transition explosions powered by the Zel’dovich reactivity gradient mechanism

ABSTRACT Our aim in this work is to identify and explain the necessary conditions required for an energetic explosion of a Chandrasekhar-mass white dwarf. We construct and analyse weakly compressible turbulence models with nuclear burning effects for carbon/oxygen plasma at a density expected for the deflagration-to-detonation transition (DDT) to occur. We observe the formation of carbon deflagrations and transient carbon detonations at early times. As turbulence becomes increasingly inhomogeneous, sustained carbon detonations are initiated by the Zel’dovich reactivity gradient mechanism. The fuel is suitably preconditioned by the action of compressive turbulent modes with wavelength comparable to the size of resolved turbulent eddies; no acoustic wave is involved in this process. Oxygen detonations are initiated, aided either by reactivity gradients or by collisions of carbon detonations. The observed evolutionary time-scales are found to be sufficiently short for the above process to occur in the expanding, centrally ignited massive white dwarf. The inhomogeneous conditions produced prior to the DDT might be of consequence for the chemical composition of the outer ejecta regions of Type Ia supernovae from the single degenerate channel, and offer the potential for validation of the proposed model.

Brooker, E.↗
DOE OSTI2025

Interfacial oxide and other species in trimethylaluminum-pretreated atomic layer deposition-Al 2 O 3 /GaN characterized by sputter-assisted ToF-SIMS

Deposition techniques, like atomic layer deposition (ALD), are used to form high-quality dielectrics for GaN-based metal–oxide–semiconductor (MOS) gate structures due to the lack of a reliable thermal oxide in GaN. Moreover, interfacial GaO x from pre-existing native oxides is thought to adversely impact channel carrier dynamics and induce undesired threshold voltage shifts in GaN-based MOS gate structures. Exposure of the GaN surface to the trimethylaluminum (TMA) precursor prior to standard alumina ALD decreases the native oxide layer on GaN, but the extent of chemical modification has not been well studied in the context of interface composition in a MOS gate structure. Herein, we compare annealed 55 nm Al 2 O 3 dielectric films on GaN grown using either a water-first ALD process or a process including sequential pulses of TMA immediately before the initiation of Al 2 O 3 ALD. Time-of-Flight Secondary Ion Mass Spectrometry measures differences in the interfacial GaO x content between each ALD film. Here, it also detects surface contaminant species like Si, F, S, and C. Furthermore, we report the formation of an AlN species at the Al 2 O 3 /GaN interface, which is more prominent for the film grown using the TMA pre-pulse step. In general, this work demonstrates that the TMA pre-pulse step is an effective strategy for cleaning substrate surfaces prior to ALD.

Atomic layer deposition↗
DOE OSTI2020

Isomer-Dependent Reaction Mechanisms of Cyclic Ether Intermediates: cis -23-Dimethyloxirane and trans -23-Dimethyloxirane.

Oxiranes are a class of cyclic ethers formed in abundance during low-temperature combustion of hydrocarbons and biofuels, either via chain-propagating steps that occur from unimolecular decomposition of β-hydroperoxyalkyl radicals (β-˙QOOH) or from reactions of HÒO with alkenes. The cis- and trans-isomers of 2,3-dimethyloxirane are intermediates of n-butane oxidation, and while rate coefficients for β-˙QOOH → 2,3-dimethyloxirane + OH are reported extensively, subsequent reaction mechanisms of the cyclic ethers are not. As a result, chemical kinetics mechanisms commonly adopt simplified chemistry to describe the consumption of 2,3-dimethyloxirane by convoluting several elementary reactions into a single step, which may introduce mechanism truncation error—uncertainty derived from missing or incomplete chemistry. The present research examines the isomerdependence of 2,3-dimethyloxirane reaction mechanisms in support of ongoing efforts to minimize mechanism truncation error. Reaction mechanisms are inferred via the detection of products from Cl-initiated oxidation of both cis-2,3-dimethyloxirane and trans-2,3-dimethyloxirane using multiplexed photoionization mass spectrometry (MPIMS). The experiments were conducted at 10 Torr and temperatures of 650 K and 800 K. To complement the experiments, the enthalpies of stationary points on the ˙R + O 2 surfaces were computed at the ccCA-PS3 level of theory. In total, 28 barrier heights were computed on the 2,3-dimethyloxiranylperoxy surfaces. Two notable aspects are low-lying pathways that form resonance-stabilized ketohydroperoxide-type radicals caused by ˙QOOH ring-opening when the unpaired electron is localized adjacent to the ether group, and cis-trans isomerization of ˙R and ˙QOOH radicals, via inversion, which enable reaction pathways otherwise restricted by stereochemistry. Several species were identified in the MPIMS experiments from ring opening of 2,3-dimethyloxiranyl radicals. Neither of the two conjugate alkene isomers prototypical of ˙R + O 2 reactions were detected. Products were also identified from decomposition of ketohydroperoxide-type radicals. The present work provides the first analysis of 2,3-dimethyloxirane oxidation chemistry and reveals that consumption pathways are complex and require the expansion of submechanisms in chemical kinetics mechanisms.

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

Engineering the hyperthermophilic archaeon Pyrococcus furiosus for 1-propanol production

ABSTRACT Society relies heavily on chemicals traditionally produced through the refinement of fossil fuels. The conversion of renewable biomass to value-added chemicals by microbes, particularly hyperthermophiles (T opt ≥80°C), offers a renewable alternative to this traditional approach. Herein, we describe the engineering of the hyperthermophilic archaeon Pyrococcus furiosus , which grows optimally (T opt ) at 100°C, for the conversion of sugar to 1-propanol. This was accomplished by constructing a hybrid metabolic pathway consisting of two native and seven heterologously produced enzymes to convert acetyl-CoA from carbohydrate metabolism to 1-propanol. A total of eleven foreign genes from two other organisms were utilized, one from the thermophilic bacterium Thermoanaerobacter sp. strain X514 and 10 from the thermoacidophilic archaeon Metallosphaera sedula, both of which grow optimally near 70°C. The recombinant P. furiosus strain produced 1-propanol at similar concentrations (up to ~1 mM) when incubated at 75°C to activate the gene products of Thermoanaerobacter sp. strain X514 and M. sedula and by initially incubating at 95°C for P. furiosus growth and then subsequently returning to 75°C to promote 1-propanol formation. Note that 1-propanol was not produced if the culture was grown only at 95°C. This work has the potential for future optimization through harnessing the genome-scale metabolic model of P. furiosus that was used herein to identify engineering targets to increase 1-propanol titer. IMPORTANCE As petroleum reserves become increasingly strained, the development of renewable alternatives to traditional chemical synthesis becomes more important. In this work, a high-temperature biological system for sugar to 1-propanol conversion was demonstrated by metabolic engineering of the hyperthermophilic archaeon Pyrococcus furiosus (T opt 100°C). The engineered strain produced 1-propanol by temperature shifting from 75°C to 95°C and then back to 75°C to accommodate the temperature ranges for native and foreign proteins associated with 1-propanol biosynthesis. Genome-scale metabolic modeling informed the carbon and reductant flux in the system, identified potential factors limiting 1-propanol production, and revealed potential optimization targets.

O'Quinn, Hailey C. (ORCID:0009000274865755)↗
DOE OSTI2020

Advances in Polymerizations Modulated by External Stimuli

Synthetic polymer chemistry endeavors to imitate the spatial and temporal control exhibited within biological systems to obtain well-defined polymeric materials with unique structures, properties, and applications. This is often approached through the development of dynamic catalyst (or initiator) systems that use external stimuli to elicit discrete, site specific transformations that impact the polymerization. In this work, we highlight developments in polymerizations that are modulated by external stimuli, with particular focus on those systems that enable notable changes in kinetics, monomer selectivity, polymer architecture, or tacticity. Examples of external stimuli include chemical oxidants or reductants, light, applied voltage, and mechanical force.

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

Investigating magnetic van der Waals materials using data-driven approaches

In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. Finally, this study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

An Overview of the Risk Assessment Information System

This technical memorandum (TM) presents an overview of the Risk Assessment Information System (RAIS), a collection of web-based tools designed to assist with the environmental risk assessment process. The objective of the RAIS is to be a single resource for the risk assessment process, providing guidance when planning and performing the steps: data assessment, exposure assessment, toxicity assessment, and risk characterization. The RAIS evolved as a result of the initial remediation efforts at various United States (U.S.) Department of Energy (DOE) facilities. The goal was to increase the efficiency and transparency of the human health and ecological assessments being performed by DOE’s Office of Environmental Management, Oak Ridge Operations (ORO) office by providing a repository for toxicity information, physicochemical data, risk assessment procedures, standardized risk calculation methods, and web-based tools. Since the initial launch in 1996, the RAIS has expanded its user base outside of the federal government and now has users from over 100 countries, universities, states, and local governments. What sets the RAIS apart from other risk assessment sites are the publicly available, searchable toxicity and physicochemical databases and the wide range of chemical and radionuclide risk calculation tools. The purpose of this TM is to present the RAIS tools in order of the website menus and explain how they fit in the risk assessment process. In addition, this TM describes differences between the chemical and radionuclide tools of the RAIS. Screening level equations, chronic daily intake equations, and default exposure factors used in the chemical and radionuclide calculators are included in the appendices of this TM. This TM is not intended to be a detailed guide to risk assessment or the RAIS tools. The tools on the RAIS can be used to comply with procedures from multiple agencies, including but not limited to DOE, U.S. Environmental Protection Agency (EPA), U.S. Department of Defense (DoD), and many state governments. Further information on the RAIS tools can be found in the user guides and tutorials available on the webpage.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

FY25 Organic Evaporation Testing in Support of Hanford Sample-and-Send

The Hanford Waste Treatment and Immobilization Plant (WTP) is being built to treat and immobilize the approximately 56 million gallons of radioactive mixed waste stored in 156 underground storage tanks. The baseline method for immobilization of Low-Activity Waste (LAW) is vitrification, but additional immobilization capacity is needed to supplement the initial LAW melters. Additionally, efforts are being undertaken to accelerate the disposition of Pre-Treated Waste (PTW) in Hanford’s West Area. An alternative cementitious waste form is being investigated as an alternative immobilization method. However, one impediment to a cementitious waste form is the presence of Land Disposal Restricted (LDR) organic chemicals in tank waste, which are regulated on a concentration-based standard in the final waste form. Hence, if the quantity of organics in PTW and LAW is high enough, treatment may be needed to destroy or remove said organics to make a waste form compatible with disposal in a mixed low level waste landfill. This work evaluates evaporation as a potential avenue for treatment of LDR organics to eliminate the impediment and permit possible use of a cementitious waste form.

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

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

SURFACE FINISHING AND ELECTROLESS NICKEL PLATING OF ADDITIVELY MANUFACTURED (AM) METAL COMPONENTS

This study investigates the application of electroless nickel deposition on additively manufactured stainless steel samples. Current additive manufacturing (AM) technologies produce metal components with a rough surface. Rough surfaces generally exhibit fatigue characteristics, increasing the probability of initiating a crack or fracture to the printed part. For this reason, the direct use of as-produced parts in a finished product cannot be actualized, which presents a challenge. Post-processing of the AM parts is therefore required to smoothen the surface. This study analyzes chempolish (CP) and electropolish (EP) surface finishing techniques for post-processing AM stainless steel components CP has a great advantage in creating uniform, smooth surfaces regardless of size or part geometry EP creates an extremely smooth surface, which reduces the surface roughness to the sub-micrometer level. In this study, we also investigate nickel deposition on EP, CP, and as-built AM components using electroless nickel solutions. Electroless nickel plating is a method of alloy treatment designed to increase manufactured component's hardness and surface resistance to the unrelenting environment. The electroless nickel plating process is more straightforward than its counterpart electroplating.. We use low-phosphorus (2-5% P), medium-phosphorus (6-9% P), and high-phosphorus (10-13% P). These Ni deposition experiments were optimized using the L9 Taguchi design of experiments (TDOE), which compromises the prosperous content in the solution, surface finish, plane of the geometry, and bath temperature. The pre-and post-processed surface of the AM parts was characterized by KEYENCE Digital MicroscopeVHX-7000 and Phenom XL Desktop SEM. The experimental results show that electroless nickel deposition produces uniform Ni coating on the additively manufactured components up to 20 μm per hour. Mechanical properties of as-built and Ni coated AM samples were analyzed by applying a standard 10 N scratch test. Nickel coated AM samples were up to two times scratch resistant compared to the as-built samples. This study suggests electroless nickel plating is a robust viable option for surface hardening and finishing AM components for various applications and operating conditions.

Keywords: additive manufacturing, fatigue, chempol↗
DOE OSTI2025

Dual aging pathways of Cu-SSZ-13 SCR catalysts: Hydrothermal vs. sulfur-induced deactivation

Hydrothermal aging (HTA) and chemical poisoning are two primary factors contributing to the real-world degradation of Cu-SSZ-13 SCR catalysts. Investigating field-returned samples offers valuable insights into performance degradation caused by these mechanisms. However, the simultaneous presence of both deactivation pathways complicates the isolation of their individual effects in post-mortem analyses. In this study, we separately prepared model Cu-SSZ-13 SCR catalysts subjected to hydrothermal-aging and sulfur-induced chemical poisoning. Using various characterization techniques, we elucidated the specific role of each aging process in catalyst deactivation and compared the results to real-world field-aged catalysts. Our findings show that hydrothermal aging at 650 °C for 100 h caused dealumination of the zeolite framework but no significant CuO x cluster formation. In contrast, sulfur aging (via sulfur exposure, calcination at 550 °C, and desulfation up to 750 °C) led to CuO x formation without any observable dealumination. On model catalysts, sulfur poisoning was found to reduce Cu mobility and the amount of active Cu sites, thus degrading catalyst activity. Although some activity was recovered upon desulfation, a portion of the initial catalyst activity remained irreversibly lost due to CuO x formation. We demonstrate that this occurs because sulfated species impede the ability of multi-nuclear Cu species (e.g., Cu dimers) to split back into their isolated form, leading to CuSO 4 -clusters that oxidatively desulfate to CuO x species. This degradation pathway explains the significant reduction in activity of field-aged samples, where substantial CuSO 4 -cluster accumulation leads to reduced active Cu and subsequent conversion to CuO x . Furthermore, the conclusions from model catalysts were extended directly to field-aged commercial samples, elucidating the decline in activity and chemical properties during field deployment.

Catalyst Deactivation↗
DOE OSTI2020

Autonomous Navigation and Control of UGVs' in Nuclear Power Plants - 20381

The purpose of the husky A200 ground robot is to autonomously navigate through the places where it is very hazardous for human beings to reach and operate, like nuclear power plants, chemical industries. The aim is to navigate the ground robot autonomously with an Arm mounted on the robot along with the different sensors as camera, and Lidar. The autonomous motion of the robot is controlled by the controller which uses path planner for trajectory generation of the robot. The mission planner uses the current position of the husky A200, given the way points of the initial and the destination it would extract a best possible route based on the current events provided using GMapping. The global reference frame is used for planning the way points. Creating the appropriate path and the actions required to follow the path are given by the motion planner. The motion planner depends on the active sensor data such as obstacles, lanes, based on the sensor data feasible path is generated. Feasibility of the path is determined by the dynamics of the husky and a series of points generated with certain velocity and acceleration profile. The controller adjusts the lateral, longitudinal and yaw motion of the husky to command the behaviors. The kinematic model is developed for kinematic motion of the husky and the dynamic model is developed for transient and steady state characteristics. The images and other type of data captured by the camera are processed through the computational framework used to build machine learning models. TensorFlow will be used for deep learning and to identify and classify different objects around the husky. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Novel solid-infused durable nonwetting surfaces for sustained condensation heat transfer enhancement

Here, in this article, we report novel nonwetting solid-infused surfaces that are easily fabricated using industrially widely adopted methods such as chemical etching or electrodeposition and exhibit sustained dropwise condensation performance durably than superhydrophobic and lubricant-infused surfaces. Solid-infused surfaces demonstrate fourfold larger condensation heat transfer coefficient and 25% better condensation effectiveness when compared to superhydrophobic surface and conventional filmwise condensation. Although the condensation heat transfer coefficient of lubricant-infused surface initially matches that of solid-infused surface, it is shown through durability studies that the wettability characteristic that underpins the performance of lubricant-infused surfaces degrades quickly with time as the lubricant depletes and lubricant-infused surface transitions to superhydrophobic surface. Solid-infused surface, on the other hand, retains its steadfast superior dropwise condensation effectiveness in the long run, overcoming the challenges of droplet pinning, surface inundation, cloaking, and lubricant depletion that eventually deteriorate condensation on conventional nonwetting superhydrophobic and lubricant-infused surfaces.

42 ENGINEERING↗
DOE OSTI2020

Characterization of a minor actinides bearing metallic fuel pin irradiated in EBR-II

The X501 transmutation experiment irradiated in Experimental Breeder Reactor II (EBR-II) was intended to evaluate the safety and performance of the addition of minor actinides (Am, Np) into the metallic nuclear fuel system. The irradiation of this experiment is also of paramount importance for comparisons between true fast spectrum irradiations (e.g. EBR-II) and shrouded irradiation capsules performed at the thermal neutron spectrum Idaho National Laboratory Advanced Test Reactor. The X501 experimental fuel pins were irradiated to a peak measured burnup of 6.2% fission per initial heavy metal atom. This paper reports and discusses Postirradiation Examination results for one of two minor actinide bearing pins from the EBR-II X-501 experiment (X501-G591) with a focus on fuel microstructure evolution. Additionally, fuel swelling, fission product distribution, cladding strain, fission gas release, fuel microstructure and fuel cladding chemical interaction were all evaluated. The fission gas release was 79.9% and the microstructure along the fuel pin presented several characteristics very similar to other EBR-II U-Pu-Zr ternary metallic fuel experience. Minor actinides seem to not dramatically affect the overall performance of this candidate transmutation fuel. Scanning Electron Microscopy was also performed and an in-depth characterization of the different regions and phases is presented. Performance data from these irradiations can be used to inform the feasibility of minor actinide transmutation in future reactor systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Photocatalytic Methanol Dehydrogenation with Switchable Selectivity

Switchable selectivity achieved by altering reaction conditions within the same photocatalytic system offers great advantages for sustainable chemical transformations and renewable energy conversion. In this study, we investigate an efficient photocatalytic methanol dehydrogenation with controlled selectivity by varying the concentration of nickel cocatalyst, using zinc indium sulfide nanocrystals as a semiconductor photocatalyst, which enables the production of either formaldehyde or ethylene glycol with high selectivity. Control experiments revealed that formaldehyde is initially generated and can either serve as a terminal product or intermediate in producing ethylene glycol, depending on the nickel concentration in the solution. Mechanistic studies suggest a unique role of ionic nickel as an additional photoelectron competitor that can significantly influence selectivity, alongside its well-established function as a hydrogen evolution reaction cocatalyst under photocatalytic conditions. The demonstrated switchable selectivity provides a new tool for producing diverse products from methanol, while advancing the understanding of cocatalyst behavior for versatile catalytic performance.

Luo, Jie↗
DOE OSTI2025

Directed self-assembly of chiral liquid crystals into biomimetic bouligand structures in thin film

The Bouligand structure, renowned for its helicoidal arrangement and enhanced mechanical properties, has attracted significant research interest for its ability to impart enhanced strength to intrinsically soft materials. Biomimetic approaches have centered on fibrous structures in bulk materials, but translating this architecture into thin-film regime for miniaturized-wearable devices with programmable functions remains challenging. Here, we direct the self-assembly of cholesteric liquid crystals (CLCs) into hierarchical helical structures using chemically patterned surfaces. Alternating surface anchoring regions align uniform lying-down helices at the nanoscale, guiding a secondary microscale helical structure exhibiting both left- and right-handed twists. This mimetic Bouligand structure in CLCs enables optical modulation under applied field and strain with enhanced mechanical response. Simulations reveal the structural evolution from initial Bouligand configuration in LC layers to alternating twist helices. This research provides a basis for designing and manufacturing miniaturized or wearable devices with nanometer-scale precision in regulating electro-optical and mechanical properties. Bouligand structures, which offer strength in natural materials, are of interest but difficult to obtain. Here, the authors report the development of such structures by directed self-assembly of cholesteric single crystals into hierarchical helical structures, with the secondary structure having right and left-handed twists.

Bouligand structure↗