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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS2021

Breakdown in Tantalum Capacitors

Failures in tantalum capacitors can be considered as a time-dependent-breakdown and reliability of all types of capacitors, including wet, MnO2 and polymer cathode parts, depends on the efficiency of self-healing that can mend parts after breakdown. A mechanism of self-healing in MnO2 capacitors is associated with oxygen reduction in the cathode layer and isolation of the breakdown site with high-resistive Mn2O3/Mn3O4 compositions. Although this mechanism is commonly accepted, details of self-healing have not been analyzed sufficiently yet, and the effects of scintillations on behavior of the parts has not been fully understood. There is a lack of data on the self-healing efficiency in different lots of capacitors. Even less is known about self-healing in polymer tantalum capacitors. In this work, different types of polymer and MnO2 capacitors have been tested for scintillation breakdown using a constant current stress (CCS) technique modified to allow detection of the amplitudes and durations of current spikes during breakdown. Characteristics of the parts and in particular leakage currents were measured to assess the efficiency of self-healing and improve screening processes for tantalum capacitors. Damaged sites were localized using infrared camera and their appearance analyzed after deprocessing and crosssectioning. Thermal effects during scintillations have been modeled, self-healing processes in polymer and MnO2 cathode capacitors discussed, and mechanisms of breakdown based on the growth of conductive filaments in the dielectric suggested.

tantalum polymer capacitors↗
NASA NTRSDate not supplied

Uranus Probe Entry and Descent Mission Concept

Introduction: Uranus was identified as the third highest priority flagship mission in the 2012-2022 Planetary Science Decadal Survey. This latest concept study was requested by the Decadal Survey panel to determine NASA’s planetary science priorities from 2022-2032. This study focused on the probe’s entry and descent aspects and associated trades for viable trajectory options. Uranus Mission and Descent Probe: The proposed Uranus Orbiter and Probe (UOP) Flagship mission will investigate Uranus and its surrounding moons using an orbiting spacecraft with a Uranus descent probe. Unlike previous studies the probe release will occur after orbit insertion allowing sufficient separation of critical events during the orbit insertion burn. The probe will be released at an altitude that allows one hour of in situ atmospheric readings that will be relayed to the orbiter. Afterwards the orbiter will transition to the moon tour phase of the mission. The configuration chosen for the entry aeroshell was a 45° sphere-cone. This shape has been used in the past in the Pioneer Venus Galileo missions. However, the nose radius considered in the present study differed from the values used in either of the previous configurations, primarily to reduce the heat flux at the stagnation point. A two-step approach was used in the development of flight trajectories for the chosen configuration. In the first step, the trajectory code POST2 was used to screen the thousands of entry states provided by interplanetary trajectory simulations, which were terminated at an altitude of 2000 km from the reference surface (1 bar) of Uranus. The screening criteria were: (i) optimization of the communication geometry between the entry probe and orbiter to ensure at least 1 hour of science measurements, (ii) peak stagnation point pressures to be less than six bar, (iii) peak heat fluxes to be less than 5 kW/cm2; the latter two constraints being the limits of ground-test capabilities of the arc jets at NASA Ames Research Center. The entry team investigated two trajectories that met the criteria above, a shallow entry (high heat load ~44 kJ/cm2) and a steeper entry (high heat rate ~1950 W/cm2). In the second step, the two bounding candidate entry states from the POST2 screening process were used in developing flight trajectories using TRAJ coupled with FIAT (a materials thermal response and sizing code) and a margins policy to determine a margined uniform thickness (hence mass) of the forward heatshield material based on the aerothermal environments at the stagnation point. Since the combination of TRAJ and FIAT size the TPS based on stagnation point environments only, flow field computations using DPLR were necessary to determine turbulent aerothermal environments on the conical flank, and the augmentation of these environments due to surface roughness. The environments at select locations on the forward heatshield were then used to size the thermal protection material, with the largest thickness value then used to estimate the mass. The newly developed woven thermal protection material –HEEET (Heatshield for Extreme Entry Environments Technology) –was considered for the forward heatshield and PICA (Phenolic-Impregnated Carbon Ablator) was considered for the backshell. These NASA-developed materials are at TRL 6 and TRL 9, respectively. Furthermore, two options were considered for the HEEET material: (i) a dual-layer option with a denser recession layer on top and an insulative layer underneath it, and (ii) a single layer option consisting of the insulative layer alone. Results: It is clear that probe entry states are feasible and the selected TPS options are able to perform in the predicted aerothermal environments thus enabling the mission to meet of the descent probe portion of this flagship mission

Entry Descent and Landing↗
NASA NTRSDate not supplied

Analysis of the Artemis I Orion Spacecraft Power System Performance

NASA successfully completed an uncrewed test flight of the Orion spacecraft during the 26-day Artemis I mission in November and December 2022. The Artemis I mission profile included several potentially stressing features for Orion electrical power system (EPS) performance, including eclipse duration, multiple propulsive or navigational maneuvers which constrained positioning of the solar array wings (SAWs), and the proximity and phasing of various events together. All of these features vary significantly with Earth-sun-moon geometry, providing a unique challenge for predicting EPS performance before an exact launch date is known. This presentation will provide a brief mission overview, discuss the different computer models with varying levels of fidelity used to analyze Orion EPS performance, as well as the screening process developed to incorporate EPS performance as a consideration for launch epoch selection. Final preflight model predictions of EPS performance will be compared to in-flight telemetry measurements, and several EPS anomalies observed will be briefly discussed.

Orion↗
DOE OSTI2020

Quantifying Human Behavior and Decision Errors in Security Screening Operations

The potential for human errors in conducting security related screening operations can lead to inadvertent and adverse decision outcomes. This paper overviews an initial mathematical framework designed to model and quantify the various human factors and decision outcomes that may occur in conducting security screening operations, such as U.S. port of entry radiological and nuclear (rad/nuc) security screening. This framework is based on the Human Error Assessment and Reduction (HEART) technique. As applied here, the framework incorporates a set of rules for human engagement, including a prescribed concept of operations (CONOPS) and deviations that may occur from this established CONOPS due to inadvertent personnel decision errors. We also review some of the various factors that may adversely influence such decisions by security screening personnel. Some of these factors include current workload, environmental conditions, training, and various other intangible factors. Using the HEART methodology, we translate each of these factors into error producing conditions, their effects on error, an assessed proportion of effects, and finally the overall probability of human error at each stage of the screening process. We then include a small scale example to demonstrate the methodology and results based on an assumed set of input conditions at a notional port of entry.

Brigantic, Robert T.↗
NASA NTRS1986

Configuration evaluation and criteria plan. Volume 2: Evaluation critera plan (preliminary). Space Transportation Main Engine (STME) configuration study

The unbiased selection of the Space Transportation Main Engine (STME) configuration requires that the candidate engines be evaluated against a predetermined set of criteria which must be properly weighted to emphasize critical requirements defined prior to the actual evaluation. The evaluation and selection process involves the following functions: (1) determining if a configuration can satisfy basic STME requirements (yes/no); (2) defining the evaluation criteria; (3) selecting the criteria relative importance or weighting; (4) determining the weighting sensitivities; and (5) establishing a baseline for engine evaluation. The criteria weighting and sensitivities are cost related and are based on mission models and vehicle requirements. The evaluation process is used as a coarse screen to determine the candidate engines for the parametric studies and as a fine screen to determine concept(s) for conceptual design. The criteria used for the coarse and fine screen evaluation process is shown. The coarse screen process involves verifying that the candidate engines can meet the yes/no screening requirements and a semi-subjective quantitative evaluation. The fine screen engines have to meet all of the yes/no screening gates and are then subjected to a detailed evaluation or assessment using the quantitative cost evaluation processes. The option exists for re-cycling a concept through the quantitative portion of the screening and allows for some degree of optimization. The basic vehicle is a two stage LOX/HC, LOX/LH2 parallel burn vehicle capable of placing 150,000 lbs in low Earth orbit (LEO).

Bair, E. K.↗
NASA NTRS1979

Improving NASA's technology transfer process through increased screening and evaluation in the information dissemination program

The current status of NASA's technology transfer system can be improved if the technology transfer process is better understood. This understanding will only be gained if a detailed knowledge about factors generally influencing technology transfer is developed, and particularly those factors affecting technology transfer from government R and D agencies to industry. Secondary utilization of aerospace technology is made more difficult because it depends on a transfer process which crosses established organizational lines of authority and which is outside well understood patterns of technical applications. In the absence of a sound theory about technology transfer and because of the limited capability of government agencies to explore industry's needs, a team approach to screening and evaluation of NASA generated technologies is proposed which calls for NASA, and other organizations of the private and public sectors which influence the transfer of NASA generated technology, to participate in a screening and evaluation process to determine the commercial feasibility of a wide range of technical applications.

Laepple, H.↗
DOE OSTI2025

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

Artificial intelligence↗
DOE OSTI2022

Novel Process for Screen-Printed Selective Area Front Polysilicon Contacts for TOPCon Cells Using Laser Oxidation

The efficiency potential of double-side tunnel oxide passivated contact (DS-TOPCon) solar cells is limited by parasitic absorption in the front poly-Si layer, despite excellent passivation and high V OC . The use of patterned poly-Si only under the front metal grid lines can significantly reduce the parasitic absorption loss without sacrificing voltage. In this work, we demonstrate a simple, manufacturing-friendly method of patterning the front poly-Si using a nanosecond UV (355 nm) laser. We found that with laser powers ≥3 W at a 400 mm/s scan speed, an estimated 1–4 nm thick stoichiometric SiO 2 layer was grown on TOPCon. This served as a mask for KOH-etching of 200 nm poly-Si, allowing for patterning of poly-Si fingers required for selective TOPCon. While laser powers above 3 W caused substantial deterioration in passivation quality, the resulting damage in J 0 was largely recovered by subsequent PECVD SiN x deposition. At 3 W, the full area J 0 was found to be 36.8 fA·cm –2 . Furthermore, this translates to 1.68 fA·cm –2 for 4.48% coverage from the wing area of the polyfinger lines (100 lines–100 μm wide and 30 μm metal) contributing to a total front J 0 of ~10 fA·cm –2 , well suited for 25% efficient solar cells

42 ENGINEERING↗
NASA NTRS2011

Cryogenic Quenching Process for Electronic Part Screening

The use of electronic parts at cryogenic temperatures (less than 100 C) for extreme environments is not well controlled or developed from a product quality and reliability point of view. This is in contrast to the very rigorous and well-documented procedures to qualify electronic parts for mission use in the 55 to 125 C temperature range. A similarly rigorous methodology for screening and evaluating electronic parts needs to be developed so that mission planners can expect the same level of high reliability performance for parts operated at cryogenic temperatures. A formal methodology for screening and qualifying electronic parts at cryogenic temperatures has been proposed. The methodology focuses on the base physics of failure of the devices at cryogenic temperatures. All electronic part reliability is based on the bathtub curve, high amounts of initial failures (infant mortals), a long period of normal use (random failures), and then an increasing number of failures (end of life). Unique to this is the development of custom screening procedures to eliminate early failures at cold temperatures. The ability to screen out defects will specifically impact reliability at cold temperatures. Cryogenic reliability is limited by electron trap creation in the oxide and defect sites at conductor interfaces. Non-uniform conduction processes due to process marginalities will be magnified at cryogenic temperatures. Carrier mobilities change by orders of magnitude at cryogenic temperatures, significantly enhancing the effects of electric field. Marginal contacts, impurities in oxides, and defects in conductor/conductor interfaces can all be magnified at low temperatures. The novelty is the use of an ultra-low temperature, short-duration quenching process for defect screening. The quenching process is designed to identify those defects that will precisely (and negatively) affect long-term, cryogenic part operation. This quenching process occurs at a temperature that is at least 25 C colder than the coldest expected operating temperature. This quenching process is the opposite of the standard burn-in procedure. Normal burn-in raises the temperature (and voltage) to activate quickly any possible manufacturing defects remaining in the device that were not already rejected at a functional test step. The proposed inverse burn-in or quenching process is custom-tailored to the electronic device being used. The doping profiles, materials, minimum dimensions, interfaces, and thermal expansion coefficients are all taken into account in determining the ramp rate, dwell time, and temperature.

Sheldon, Douglas J.↗
DOE OSTI2022

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dynamics of superconducting qubit relaxation times

Superconducting qubits are a leading candidate for quantum computing but display temporal fluctuations in their energy relaxation times T 1 . This introduces instabilities in multi-qubit device performance. Furthermore, autocorrelation in these time fluctuations introduces challenges for obtaining representative measures of T 1 for process optimization and device screening. These T 1 fluctuations are often attributed to time varying coupling of the qubit to defects, putative two level systems (TLSs). In this work, we develop a technique to probe the spectral and temporal dynamics of T 1 in single junction transmons by repeated T 1 measurements in the frequency vicinity of the bare qubit transition, via the AC-Stark effect. Across 10 qubits, we observe strong correlations between the mean T 1 averaged over approximately nine months and a snapshot of an equally weighted T 1 average over the Stark shifted frequency range. These observations are suggestive of an ergodic-like spectral diffusion of TLSs dominating T 1 , and offer a promising path to more rapid T 1 characterization for device screening and process optimization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening

The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.

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

Multi-scale computational screening and mechanistic insights of cyclic amines as solvents for improved lignocellulosic biomass processing

A computational screening workflow for the efficient deconstruction of cellulose, lignin and hemicellulose fractions of lignocellulosic biomass using cyclic amines as solvents. Lignocellulosic biomass is a promising feedstock for production of affordable fuels and chemicals from renewable resources. Effective solubilization and subsequent deconstruction of its cellulose, hemicellulose, and lignin fractions is essential for the viability of future biorefineries. This study used quantum chemistry-based equilibrium thermodynamics methods to evaluate the potential of 650 cyclic amines to solubilize cellulose, hemicellulose, and lignin. The activity coefficients of solvent - biopolymer interactions were predicted using the COSMO-RS (COnductor-like Screening MOdel for Real Solvents) method and used to identify cyclic amines that can efficiently dissolve and extract selective fractions of biopolymers during biomass pretreatment. Among the 650 cyclic amines, 1-piperazineethanmaine was predicted to be an effective solvent for extracting all three polymers and was experimentally shown to achieve the highest lignin removal (97.1%). Non-covalent interaction, reduced density gradient and quantum chemical calculations were performed to elucidate the dissolution mechanism of lignin, cellulose and hemicellulose and gain further molecular level insights into the interactions between the cyclic amines and biomass polymers that promote efficient solubilization and extraction. These analyses indicated that 1-piperazineethanmaine and 1-methylimidazole make noncovalent van der Waals, electrostatic interactions and hydrogen bonding with lignin, leading to enhanced lignin removal, while the strong intramolecular hydrogen bonding interactions in cellulose and hemicellulose result in weaker solvent-biopolymer interactions. Overall, the computational approach provided an efficient method for identifying cyclic amines tailored for optimal biomass pretreatment and resulted in the identification of a potential new class of solvents for effective biomass pretreatment.

Kumar, Nikhil↗
DOE OSTI2021

Tuning Surface Stoichiometry of SOFC Electrodes at the Molecular and Nano-scale for Enhanced Performance and Durability

This project achieved the following objectives. Different cation segregation behaviors of different common SOFC cathodes, including La 0.6 Sr 0.4 Co0.2Fe 0.8 O 3-δ (LSCF), Sr 0.5 Sm 0.5 O 3-δ (SSC), and PrBa 0.5 Sr 0.5 Co 1.5 Fe 0.5 O5 +δ (PBSCF), were determined. We showed how oxygen partial pressure and gas impurities impact the stability of SOFC cathodes. We developed atomic layer deposition (ALD) coating techniques for electrodes and showed that by introducing different elements and different ALD oxidizers, electrode surface chemistry can be altered, resulting in enhanced oxygen reduction kinetics. In addition, we developed solution infiltration technique to enhance performance and durability of electrodes. Different infiltrates as well as different thermal treatment processes were screened to identify the optimal surface modification process that yields both low impedance and high durability. The modified cathode shows excellent stability and has a low area specific resistance (ASR) of only 0.2 Ωcm 2 at 600 °C after over 2000 hours of operation. Further, we developed ceramic anodes, SrFe(Co,Mo)O 3 (SFCM) and SrFe(Ni,Mo)O 3 (SFNM), and enhanced anode oxidation kinetics by solution infiltration or in situ catalyst exsolution from the ceramic anode surface. The modified anodes showed improved performance in full SOFCs, and the optimized anode modification shows high stability for over 400 hours at 550 °C. Moreover, the modified anode also demonstrates high durability in methane. This work provides fundamental understanding of electrode surface chemistry and demonstrates a simple, facile, cost-effective approach to enhance catalytic activity and durability of SOFC electrodes.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Advances in Modeling Capabilities for Critical Mineral Separation Technologies: A PrOMMiS Overview

This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

critical minerals and materials↗