Bayesian Optimization of Low-Temperature Nonthermal Plasma Jet Sintering of Nanoinks
Not provided.
Materials Science↗
SEARCH · Search NASA
Results for “Temperature optimization”
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.
A Liquid–Vapor Two-Phase Model of Direct Methanol Fuel Cells With Platinum Group Metal-Free Cathode Catalyst
Abstract This study has developed a two-dimensional, two-phase transport model to investigate the transport characteristics in direct methanol fuel cells (DMFCs) using platinum group metal (PGM)-free cathode catalysts. The model considered anisotropic properties of the gas diffusion layer (GDL) caused by current collector’s mechanical compression, the interfacial mass transfer of water and methanol between liquid and vapor, and unique properties of the cathode PGM-free catalyst layer. Results showed that the liquid methanol solution from the anode could provide sufficient water to hydrate the proton exchange membrane (PEM), and the relative humidity of the cathode air did not impact the membrane hydration. Fully hydrating the cathode air may deteriorate the fuel cell performance, especially when the operating temperature is close to 100 °C because the exponential increase of the saturated water pressure with temperature decreased the partial pressure of oxygen. The optimized operating temperature increased with the increase of air pressure and was about 80 °C at 1.5 atm cathode pressure. To achieve the US Department of Energy’s performance target of 300 mW/cm2 peak power density, catalytic activities of both the anode and cathode catalysts need to be improved by one order of magnitude compared with the state-of-the-art commercial catalysts.
Electrochemistry↗
Formulation of Conductive Inks for DIW Printed Battery Electrodes
Ink formulations for DIW printing of Li ion battery electrodes were optimized for high conductivity, surface area and printability. Our focus is particularly on optimizing Li-ion battery electrodes for low temperature applications by 3-D printing the cathodes into high surface area shapes. The sluggish kinetics of ion transport at the solid-electrolyte phase boundary can be a major problem in batteries at low temperatures, so optimizing for ion transport can help combat these issues. Two families of DIW ink were investigated; aqueous conductive polymer suspension with graphene nano platelets (GNP) ink, and “oil in water” graphene oxide (GO) micro emulsion ink which carbonizes to an open cell graphene aerogel. By iterative formulation changes, the conductivity of PEDOT:PSS based ink was increased over tenfold, and stiffness of GO emulsion ink was increased to provide better printability without sacrificing porosity.
36 MATERIALS SCIENCE↗
Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning
Accurate prediction of the optimal catalytic temperature ( T opt ) of enzymes is vital in biotechnology, as enzymes with high T opt values are desired for enhanced reaction rates. Recently, a machine learning method (temperature optima for microorganisms and enzymes, TOME) for predicting T opt was developed. TOME was trained on a normally distributed data set with a median T opt of 37 °C and less than 5% of T opt values above 85 °C, limiting the method’s predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on T opt values greater than 85 °C is nearly an order of magnitude higher than the error on values between 30 and 50 °C. Here, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high T opt values (>85 °C) by 60% and increase the overall R 2 value from 0.527 to 0.632. The revised method, temperature optima for enzymes with resampling (TOMER), and the resampling strategies applied in this work are freely available to other researchers as Python packages on GitHub.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Smart Refractory Sensors Development for Corrosion and Erosion Monitoring in High Temperature Systems
To optimize the operation and functioning of high temperature systems such as slagging gasifiers, coal boilers and glass/steel melters, it is important to monitor corrosion and erosion of refractory used in such systems. Corrosion test strategies are generally based on continuous gravimetric and chemical reactivity monitoring at operational temperatures (750°-1500°C). Both thermocouples and failure sensors and arrays would be useful to monitor the health of any refractory or coatings in these systems. Many of such type of sensors are installed into the systems through open access ports within the refractory; however, there are some disadvantages of this approach where corrosive/erosive gas and molten materials can penetrate and compromise the system. The current work presents the development and performance demonstration of smart refractory with embedded high temperature sensors such as thermocouples, thermistors, and various spallation/crack monitoring sensors, which may be used within a variety of refractory brick in different high temperature processes and applications. The main feature of this technology is that electroceramic based sensors are embedded into smart refractory without significantly impact to the intrinsic properties of the refractory. This technology circumvents the need to insert an isolated monolithic, stand-alone sensor into the refractory via an access port. This technological approach guarantees the integrity and the chemical stability of the materials used in the sensor fabrication within the harsh environment and does not introduce molten material (such as slag) penetration pathways within the refractory. One interesting and important aspect of this innovation is that these embedded sensors can be used to in situ monitoring processes such as chemical reactions and at the same time give information and a deeper understanding of the corrosion and erosion process of the refractory within the system. As stated above, the objective of our work is to develop high-temperature sensors composed of electroceramic materials that are chemically stable at high temperatures (750°-1500°C) and high pressures (up to 1000 psi) that can be used in monitoring corrosion and erosion process in refractory used in high energy systems. The high-temperature sensors investigated in this work were composed of various oxide composites directly embedded into the refractory oxides. The composites used for this work were synthesized by a mixed-oxide route. Metal oxides were inserted within a matrix material composed of refractory oxides (Al2O3, ZrO2, etc.). The physical and electrical properties were specifically manipulated by altering the level of percolation of the conductive species (metal oxides) within the refractory constituent (refractory oxide). Prior to the development of the high-temperature sensors, the oxides composites developed in this study were sintered up to 1600°C under oxidizing atmosphere in order to investigate densification, microstructural evolution, phase development, and their thermoelectrical performance as a function of the composition. The 4-point DC conductivity measurements were performed between 100°-1500°C. The sensors were fabricated from the composite materials by 3D-printing or screen-printing methods into the refractory brick during the consolidation process. An example of one of these embedded sensors consisted of an electroceramic-based thermocouple fabricated with two separate oxide composite compositions which were patterned to produce a couple within the interior of a refractory matrix. The thermocouple successfully displayed thermoelectric voltage trend (as a function of temperature), and the voltage was 220.0 mV around 1400 °C. Corrosion tests on the refractory embedded sensors were performed. To evaluate corrosion in the refractory brick an in-house glass composition was prepared and pressed into pellets and delivered into a pre-cut cavity in the brick. Corrosion experiments results showed the glass penetrated the brick over a 90 h period, and the penetration of the glass through the brick could be monitored by both an amperometric and voltametric based sensor. With this experiment, it was demonstrated that the embedded sensor could dynamically monitor the corrosion process.
20 FOSSIL-FUELED POWER PLANTS↗
Processing and characterization of the homologous Zr x Ta 2 O 2x+5 series
In this study, Zr x Ta 2 O 2x+5 (ZTOx) was systematically studied to determine ideal solid-state synthesis and pressureless sintering conditions. For Zr 6 Ta 2 O 17 (ZTO6) and other compositions with values of x in ZTOx the optimal synthesis temperature of 1100 °C with 1 h dwell time was optimal. XRD phase analysis of the homologous series indicated that for ZTOx, x = 5, 6, 7, and 8 were within the phase stability field while x = 4 and x = 9 were outside the phase stability field. Unit cell lattice parameters changed anisotropically with Zr:Ta ratio with an overall <0.1% change in unit cell volume across the solid solution. Some Raman modes red shifted while others blue shifted, indicating competing tensile and compressive strains. Sintering of ZTOx required a progressively higher temperature as Zr-content increased despite the same starting powder grain sizes and synthesis temperature. Higher Vickers hardness for Zr-rich (12.7–12.8 GPa) than Ta-rich Zr x Ta 2 O 2x+5 (11.6–11.7 GPa) disappeared when accounting for different sintering temperatures. Indentation toughness was similar for all compositions (1.2–1.4 MPa m 1/2 ). Properties of Zr x Ta 2 O 2x+5 series indicate tunability of Zr:Ta composition ratio while maintaining the structure and room temperature mechanical properties comparable to Hf 6 Ta 2 O 17 and 8 mol% yttria stabilized zirconia ceramics.
36 MATERIALS SCIENCE↗
Synthesis of metastable Ruddlesden–Popper titanates, ( A TiO 3 ) n A O, with n ≥ 20 by molecular-beam epitaxy
We outline a method to synthesize ( ATiO 3 ) n AO Ruddlesden–Popper phases with high- n, where the A-site is a mixture of barium and strontium, by molecular-beam epitaxy. The precision and consistency of the method described is demonstrated by the growth of an unprecedented (SrTiO 3 ) 50 SrO epitaxial film. We proceed to investigate barium incorporation into the Ruddlesden–Popper structure, which is limited to a few percent in bulk, and we find that the amount of barium that can be incorporated depends on both the substrate temperature and the strain state of the film. At the optimal growth temperature, we demonstrate that as much as 33% barium can homogeneously populate the A-site when films are grown on SrTiO 3 (001) substrates, whereas up to 60% barium can be accommodated in films grown on TbScO 3 (110) substrates, which we attribute to the difference in strain. This detailed synthetic study of high n, metastable Ruddlesden–Popper phases is pertinent to a variety of fields from quantum materials to tunable dielectrics.
36 MATERIALS SCIENCE↗
Engineering the formation of spin-defects from first principles
The full realization of spin qubits for quantum technologies relies on the ability to control and design the formation processes of spin defects in semiconductors and insulators. We present a computational protocol to investigate the synthesis of point-defects at the atomistic level, and we apply it to the study of a promising spin-qubit in silicon carbide, the divacancy (VV). Our strategy combines electronic structure calculations based on density functional theory and enhanced sampling techniques coupled with first principles molecular dynamics. We predict the optimal annealing temperatures for the formation of VVs at high temperature and show how to engineer the Fermi level of the material to optimize the defect’s yield for several polytypes of silicon carbide. Our results are in excellent agreement with available experimental data and provide novel atomistic insights into point defect formation and annihilation processes as a function of temperature.
36 MATERIALS SCIENCE↗
Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.
Computer simulations↗
High polarization InAlGaAs/AlGaAs photocathodes grown using MBE
Strained superlattices of GaAs/GaAsP grown using molecular beam epitaxy (MBE) have been used for more than 20 years to generate high polarization electron beams for nuclear physics. GaAs/GaAsP superlattices have several manufacturing challenges, including the thick graded layer required for the virtual GaAsP substrate, the significant difference in optimal growth temperatures for GaAs and GaAsP, and the scarcity of MBE systems using phosphorus. InAlGaAs/AlGaAs strained superlattice photocathode are an alternative structure that eliminates many of hurdles to growing GaAs/GaAsP in MBE systems. Measurements of quantum efficiency and polarization from InAlGaAs/AlGaAs will be presented. These include studies on a variety of growth parameters to optimize performance, including digital alloys, growth temperature variations, and variation in structure.
Stutzman, Marcy L↗
Optimizing Phase Change Composite Thermal Energy Storage Using the Thermal Ragone Framework
The thermal Ragone framework can be applied to the design of thermal energy storage heat exchangers to identifying relationships between their power requirements and energy capabilities. This presentation focuses on the design of planar thermal energy storage heat exchangers with phase change materials and thermal conductivity additives. Finite-difference models were used to optimize thermal storage heat exchanger designs focusing on key design parameters of conductivity additive volume fraction, spacing between tubes, and the phase transition temperatures. Design trends were developed for optimal designs based on varying thermal power requirements. To aid in future device design, models of reduced complexity were developed with the ability to predict optimal designs with computational speeds up to 5,000 times faster than finite-difference models.
ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
Formation of 6H-Ba 3 Ce 0.75 Mn 2.25 O 9 during Thermochemical Reduction of 12R-Ba 4 CeMn 3 O 12 : Identification of a Polytype in the Ba(Ce,Mn)O 3 Family
The resurgence of interest in a hydrogen economy and the development of hydrogen-related technologies has initiated numerous research and development efforts aimed at making the generation, storage, and transportation of hydrogen more efficient and affordable. Solar thermochemical hydrogen production (STCH) is a process that potentially exhibits numerous benefits such as high reaction efficiencies, tunable thermodynamics, and continued performance over extended cycling. Although CeO 2 has been the de facto standard STCH material for many years, more recently 12R-Ba 4 CeMn 3 O 12 (BCM) has demonstrated enhanced hydrogen production at intermediate H 2 /H 2 O conditions compared to CeO 2 , making it a contender for large-scale hydrogen production. However, the thermo-reduction stability of 12R-BCM dictates the oxygen partial pressure (pO 2 ) and temperature conditions optimal for cycling. In this study, we identify the formation of a 6H-BCM polytype at high temperature and reducing conditions, experimentally and computationally, as a mechanism and pathway for 12R-BCM decomposition. 12R-BCM was synthesized with high purity and then controllably reduced using thermogravimetric analysis (TGA). Synchrotron X-ray diffraction (XRD) data is used to identify the formation of a 6H-Ba 3 Ce 0.75 Mn 2.25 O 9 (6H-BCM) polytype that is formed at 1350 degrees C under strongly reducing pO 2 . Density functional theory (DFT) total energy and defect calculations show a window of thermodynamic stability for the 6H-polytype consistent with the XRD results. These data provide the first evidence of the 6H-BCM polytype and could provide a mechanistic explanation for the superior water-splitting behaviors of 12R-BCM.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Atomic layer deposition of sodium fluoride thin films
The need for advanced energy conversion and storage devices remains a critical challenge amid the growing worldwide demand for renewable energy. Metal fluoride thin films are of great interest for applications in lithium-ion and emerging rechargeable battery technologies, particularly for enhancing the stability of the electrode-electrolyte interface and thereby extending battery cyclability and lifetime. Reported within, sodium fluoride (NaF) thin films were synthesized via atomic layer deposition. NaF growth experiments were carried out at reactor temperatures between 175 and 250 °C using sodium tert-butoxide and HF-pyridine solution. Here, the optimal deposition temperature range was 175–200 °C, and the resulting NaF films exhibited low roughness (R q ≈ 1.6 nm for films of ~8.5 nm), nearly stoichiometric composition (Na:F = 1:1.05) and a growth per cycle value of 0.85 Å/cycle on SiO 2 substrates. These results are encouraging for future applications of NaF thin films in the development of improved energy capture and storage technologies.
25 ENERGY STORAGE↗
Advanced Moderation Module for High-Temperature Micro-Reactor Applications
Development and deployment of micro-reactors that provide competitive efficiency, prevailing compactness, and inherent safety is an immediate focus of the U.S. nuclear industry. For thermal neutron micro-reactors operating at elevated temperature for optimized efficiency, such as micro molten-salt reactors (MSRs), heat-pipe reactors, and very-high temperature reactors (VHTRs), high-performance moderator based on metal hydride can enhance the neutron economy and therefore achieve reduced weight and enhanced portability. As unclad metal hydride inevitably decomposes at elevated temperature, an enclosure is required for hydride moderator to deliver desired performance at elevated temperatures. Conventional moderator enclosure solutions based on high-temperature alloys introduce extraneous neutron penalty into the micro-reactor, affecting the neutronic benefits provided by the hydride moderator. Additionally, the compatibility between the high-temperature alloy enclosure and the micro-reactor matrix is also a potential issue. In this report, we disclose an Advanced Moderator Module (AMM) concept enabled by an innovative enclosure solution combining the advantages of refractory metals, ceramic matrix composites (CMCs), and advanced coating technology to serve as hydrogen permeation barrier up to very-high temperatures. A schematic description of the AMM structure is illustrated in Figure A1. The AMM contains a moderating material core made of metal hydride with high thermal stability, such as YH 2-x . The hydride core is enclosed by an H 2 barrier layer coated on a ductile refractory metal liner to minimize hydrogen loss during high-temperature operation. A ceramic matrix composite (CMC) cladding is adopted to provide further structural strength, especially during power transients. Between the CMC cladding and metal liner, an extra diffusion barrier coating is inserted to suppress the chemical interaction at elevated temperatures. Hence, based on a series of innovative material solutions, the AMM is capable of containing the metal hydride core at elevated temperature (>900oC) inside coated and lined CMC envelop with negligible hydrogen loss throughout the microreactor lifetime. The benefit of the AMM technology was assessed based on a comprehensive multi-stage reactor-physics analysis. Advanced moderation based on hydride metals (such as YH2-x) enables reaching optimum moderation with higher fuel content than traditional VHTR technology, which is required to design compact micro-reactor cores targeting long-life operation. The AMM enclosure technology provides lower thermal neutron absorption rates than traditional solutions (based on stainless steel or Mo-based alloy such as TZM), which reduces the fissile enrichment requirements by 6-8% on a TRISO-fueled design based on the EMPIRE core. Finally, combining the hydride moderator with neutron transparent enclosure solutions provides significant potential to boost neutronics performance of micro-reactors in terms of increased core lifetime or reduced size and weight by 30-50% on a micro-reactor based on the Holos Quad technology. The progress and plans for on-going development and demonstration efforts are also discussed in this report. The current demonstration of AMM is focused on the hydrogen diffusion barrier demonstration under thermal cycling, while radiation tolerance demonstration is planned. The next step of the demonstration plan will be the assembly of miniature AMM for high-temperature testing, which can be used as a prototype for future pilot scale demonstration.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
An experimental and numerical investigation of HD diesel engine DOC efficiency in oxidizing NO to NO 2
Reducing pollutant emissions from heavy-duty (HD) diesel engines is critical due to their significant environmental impact, particularly concerning NOx emissions. Understanding and optimizing modern diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) performance is essential for improving exhaust aftertreatment (EAT) system efficiency to meet stringent emissions regulations. The oxidation of nitric oxide (NO) to nitrogen dioxide (NO 2 ) in DOC plays a key role in improving SCR efficiency in reducing NO x . This study investigates the DOC performance in oxidizing NO to NO2 and its impact on the SCR efficiency of a 2021 MY Navistar E39 HD diesel engine. The influence of engine speed, load, exhaust gas temperature, and composition on DOC efficiency is experimentally investigated. The relationships between DOC inlet temperature, oxygen availability and NO 2 /NO x ratio at the DOC inlet are examined to better understand their effects on the overall DOC efficiency. The results indicate that DOC NO oxidation efficiency is highly dependent on exhaust temperature, with optimal oxidation occurring within a specific temperature range (275-350°C). Below this threshold, the chemical reactions are kinetically limited, while at higher temperatures, thermodynamic constraints reduce the efficiency of DOC in oxidizing NO to NO 2 . The experimental data further reveal that the NO 2 /NO x ratio peaks at medium loads before declining at higher loads due to reduced residence time and mass transfer effects. Additionally, the SCR NO x conversion efficiency is significantly influenced by the NO 2 /NO x ratio, achieving peak performance when the NO 2 /NO x ratio approaches 0.5. A DOC chemistry model was developed and validated against the experimental data to predict DOC oxidation behavior under various operating conditions. The findings of this study provide insights into the interdependencies between DOC and SCR performance, contributing to the optimization of SCR systems for optimized NOx reduction.
33 ADVANCED PROPULSION SYSTEMS↗
Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.
15 GEOTHERMAL ENERGY↗
RNA language models predict mutations that improve RNA function
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.
59 BASIC BIOLOGICAL SCIENCES↗
Quantifying bias due to non-Gaussian foregrounds in an optimal reconstruction of CMB lensing and temperature power spectra
We estimate the magnitude of the bias due to non-Gaussian extragalactic foregrounds on the optimal reconstruction of the cosmic microwave background (CMB) lensing potential and temperature power spectra. The reconstruction is performed using a Bayesian inference method known as the marginal unbiased score expansion (MUSE). We apply MUSE to a minimum variance combination of multifrequency maps drawn from the Agora publicly available simulations of the lensed CMB and correlated extragalactic foreground emission. Taking noise levels appropriate to the SPT-3G D1 release, we find non-Gaussian foregrounds may bias the MUSE reconstruction of the lensing potential amplitude at the level of (0.7 ± 0.3)σ when using modes up to ℓ max = 3500. We do not detect a statistically significant bias, finding a value of (-0.4 ± 0.3)σ, when restricted to lower angular multipoles, ℓ max = 3000. This work is a first step toward understanding the impact of extragalactic foregrounds on optimal reconstructions of CMB temperature and lensing potential power spectra.
Statistical sampling techniques↗