Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “thermal modeling techniques”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Charge carrier extraction and recombination effects in GaInAs/GaAsP multi-quantum well solar cells

The carrier extraction and transport mechanisms as well as the relative contributions of radiative and non-radiative recombination processes are investigated in high-quality strain-balanced GaInAs/GaAsP multi-quantum well solar cells recently implemented in record efficiency multijunction solar cells. A comprehensive suite of complementary characterization techniques including temperature- and suns-dependent photoluminescence and photovoltaic measurements are employed to analyze thermal escape and tunneling rates, which demonstrate the need to move beyond simple drift-diffusion models of p–n junctions. This study examines the processes that best characterize the operation of these devices across varying temperatures using a simple two-diode model, incorporating multiple transport protocols, and provides insights into the performance-limiting processes and pathways for their optimization.

14 SOLAR ENERGY↗
DOE OSTI2021

Engineering Graphene-Ceramic 3D Composite Foams by Freeze Drying

A 3D graphene foam produced by the chemical vapor deposition (CVD) technique is recognized as an effective nanofiller material, as it does not agglomerate in the matrix. Though CVD facilitates pristine graphene foam production, the method poses limitations in developing large-scale 3D graphene composite foams as advanced engineering materials. In this paper, a freeze-drying (FD) process is used to produce a composite 3D foam, which is a mixture of graphene nanoplatelet (GNP) and a low-temperature co-fired ceramic (LTCC). The freeze-dried GNP-LTCC reticulated 3D composite foam has an average pore size ranging from 70 to 100 μm. Pore size is varied by regulating the heat transfer rate during the freezing process using a thermally conductive aluminum (Al) mold and a thermally insulating acrylonitrile butadiene styrene (ABS) mold. Computational thermal modeling is used to visualize the heat transfer and its effect on foam pore size. Subsequently, a freeze-dried GNP-LTCC composite foam is embedded into the LTCC matrix to form a hierarchical assembly by a spark plasma technique without compromising the 3D structure of the FD foam. This study established that a simple, eco-friendly, and scalable processing methodology can produce advanced graphene-based 3D composite foams as the future tailorable nanofillers for designing multimatrix materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗
DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Structural and chemical disorder enhance point defect diffusion and atomic transport in Ni 3 Al-based γ' phase

The ordered L1 2 γ' Ni 3 Al intermetallic inclusions are routinely used as strengthening constituents in Ni-based superalloys and recent high-entropy alloys. As there are different metallic elements relative to the alloy matrix, the composition of γ' phase inclusions usually suffers some variations. In this work, we studied the elemental partition and its influence on atomic transport properties in a model (NiCo) 3 Al γ' phase with chemical and structural disorder. Combining the Monte Carlo and molecular static techniques, we first determined the elemental distribution in such compositionally-complexed γ' phase. The results suggest that Co tends to segregate, which profoundly influences defect energetics and diffusion properties. By modeling thermally activated diffusion by molecular dynamics, we found that chemical disorder induced by Co can enhance vacancy diffusion while significantly suppress diffusion via interstitial atom migration, as compared to the stoichiometric γ' Ni 3 Al. We further reveal that these observations are closely related to the defect energetics in these systems. Furthermore, our results explain the effects of disorder, long- and short-range orders on the diffusion properties in multicomponent intermetallic alloys, which helps to understand the stability and, thus, γ'-phase-assisted strengthening at different conditions including annealing and irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Neural network model of the multi-mode anomalous transport module for accelerated transport simulations

Here, a neural network version of the Multi-Mode Anomalous Transport Module, known as MMMnet, has been developed to calculate plasma turbulent diffusivities in DIII-D with a calculation time suitable for control applications. MMMnet uses a simple artificial neural network structure to predict the ion thermal, electron thermal, and toroidal momentum diffusivities while reproducing Multi-Mode Model (MMM) data with good accuracy and keeping the calculation time as a fraction of that associated with MMM. Model-based control techniques require models with fast calculation times, making many existing physics-oriented predictive codes unsuitable. The control-oriented predictive code COTSIM (Control Oriented Transport SIMulator) calculates the most significant plasma dynamics in response to the different actuators while running at a speed useful for control design. In order to achieve this calculation speed, COTSIM often relies on scaling laws and control-level models. Replacing some of these scaling laws and control-level models with neural network versions of more complex physics-level models has the potential of increasing the range of validity and the level of accuracy of COTSIM without compromising its computational speed. In this work, MMMnet is integrated into COTSIM to improve the turbulent diffusivity predictions, which will in turn improve the prediction accuracy associated with the dynamics of many plasma properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Accurate and Fast Anomaly Detection in Additive Composite-Based Manufacturing using Thermal Cameras

Today, large-scale additive manufacturing with plastics and composite materials requires continuous monitoring by experienced staff to prevent, detect and correct anomalous events affecting the performance of the printed part. We address the complexity of this demanding task by designing a camera-based anomaly detection system utilizing probabilistic principal component analysis (PPCA). This is a machine learning technique is trained with thermal images collected during normal operation of the large-scale printer (Cincinnati BAAM). This technique is advantageous for practical applications as there is no need to artificially introduce anomalous conditions into model training. During deployment, we challenge this model by introducing deliberate variations of the extruder speed. We reduce extrusion speed to a lower level, between 70 and 95% of the nominal value to collected test images. Our results show that images are easily identified as anomalous for extruder speeds at or below 85% of the nominal speed, meaning that an anomalous reduction of the material deposition rate can be detected within seconds of its onset. We show that our results are robust to (a) camera-to-camera variability and (b) print-to-print variability.

Pike, John [ORNL]↗
DOE OSTI2020

Automated Fitting of a Semi-empirical Multiphase Equation of State for Carbon

The equation of state (EOS) of carbon is important in high explosive, geophysical, and inertial confinement fusion applications. Within the semi-empirical Sesame framework, the EOS of each phase is represented by a sum of cold, vibrational, and thermal electronic Helmholtz free energy contributions. Each phase has ~5-10 independent parameters that are adjusted to reproduce single-phase data (e.g., thermal expansion, isothermal compression) as well as experimentally- and computationally- derived phase boundaries. Manual calibration of the full multiphase EOS is arduous. We present our progress in development of automated EOS parameterization based on minimization of an objective function. Here, this function encodes deviation of model EOS results from experimental/computational benchmarks. Optimization is implemented as a combination of global (particle swarm) and local optimization techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Proper orthogonal decomposition based reduced-order modeling of flux-Limited gray thermal radiation

Here, in this work, a proper orthogonal decomposition (POD) based reduced-order model (ROM) is developed to solve gray, flux-limited thermal radiation diffusion. We focus on the variable opacity radiation penetration benchmark posed by Olson, Auer, and Hall. The T -3 relationship for opacity in conjunction with high-temperature radiation penetrating an initially cold material produces a strong thermal radiation shock. This class of problems is particularly challenging for standard POD-based reduced-order modeling due to the nonlinearities presented by 1) the T 4 source term, and 2) flux-limited diffusion operator. To address these challenges and develop a cost competitive ROM, we employ a “hyper-reduction” technique through discrete empirical interpolation (DEIM) and allow for adaptive reduced-order projections through principal interval decomposition (PID). Performance of the proposed methodology is quantified by comparing the cost savings and accuracy relative to a full-order computation. Reference solutions and snapshot data are obtained through a full-order calculation performed by the University of Chicago maintained astrophysics code, FLASH. For consistency and potential extensibility, the developed ROM is also implemented in FLASH. We find that in the initialization regime, where the thermal radiation wave is initially created by the warming of the material, this class of problems is highly reducible and suitable for POD-based ROMs. However, the strong convective nature of the wave propagation regime is less reducible and more challenging to create an efficient ROM.

42 ENGINEERING↗
DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Discovering Blind Geothermal Systems in the Great Basin Region: An Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways: All Phases

Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis. After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley, deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Measuring the thermal conductivity of hydrogels with a bidirectional 3w method

Hydrogels are soft, water-absorbing polymer materials with diverse applications in biomedicine and agriculture. Recently, hydrogels have been proposed to encapsulate water-soluble phase change materials which store energy in their latent heat of solidification. In these applications, the thermal conductivity of these materials affects their performance. Few methods exist for measuring the thermal conductivity of small quantities of hydrogels. Here, we describe an implementation of the bidirectional 3w technique to measure the thermal conductivity of hydrogels with particular attention to their moisture content. Our implementation of the technique can probe sample volumes as little as ~20 mL and yields the thermal conductivity without requiring fitting of additional thermal parameters. We numerically simulate 3w sensor designs with frequency-domain 3-D models to quantify and reduce errors introduced by the choice of substrate and insulation layer thickness. Frequencies in the ~1−20 Hz range yield less error for the materials considered here. We verify our setup with measurements on water and report values for polyacrylamide and poly(2-acrylamido-2-methylpropane sulfonic acid) (PAMPS) hydrogels. Our swollen hydrogels exhibited thermal conductivities nearly equivalent to water, 0.6 W m-1 K-1, and we estimate thermal conductivities of 0.43 and 0.42 W m-1 K-1 for neat polyacrylamide and PAMPS, respectively. Finally, we estimate an error of ±7%, consistent with other 3ω methods, with the largest error coming from the sensor calibration. We find our implementation of the bidirectional 3w method gives reasonable results and can be employed for prototyping soft materials relevant for thermal storage.

3-omega, thermal conductivity, hydrogel, moisture ↗
DOE OSTI2025

Validation of Pronghorn for Natural-Circulation Molten Salt Loops

This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

High Performance Circuit Pastes for Solid Oxide Fuel Cell Applications: Final Technical Report

Using a combination of in-plane electrical conductivity measurements, electrical contact resistance measurements, tensile fracture tests, double shear lap fracture tests, rapid thermal cycling adhesion tests, in situ wetting angle measurements, 3D X-ray tomography, reduction-oxidation (RedOx) cycling, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDAX), Density Functional Theory (DFT) modeling, Molecular Dynamics (MD) modeling, and Phase Field modeling, this project determined that a new brazing technique developed by the PIs (specifically the use of nickel particles to direct the wetting and spreading of molten silver) could be used to produce well-adhered, >97% dense, electrically-conductive circuits, current collectors, and/or electrical contacts on a variety of ceramic and ceramic-coated substrates (specifically lanthanum strontium manganite, yttria stabilized zirconia, sapphire, Cr2O3-passivated stainless steel, and Al2O3-passivated stainless steel). Compared to other silver-based alternatives, Ag:Ni led to less manufacturing defects and was more tolerant of the extreme environments (isothermal high-temperature aging) and occasional abuse (rapid thermal cycling and RedOx cycling) encountered during Solid Oxide Fuel Cell and/or Solid Oxide Electrolysis Cell operation. Further, in some situations (such as when to produce Ag:Ni electrical contacts to Al2O3-protected stainless steel) the Ni particles also improved operation by acting as chemical getters that removed electrically-insulating, surface-segregating substrate impurities (i.e. by lowering the contact resistance with the underlying substrate).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Light-Induced Structural Dynamics and Charge Transport in Layered Halide Perovskite Thin Films

The dynamic nature of the metal halide perovskite lattice upon photoexcitation plays a vital role in their properties. Here we report an observation of light-induced structure dynamics in quasi-2D Ruddlesden–Popper phase perovskite thin films and its impact on the carrier transport properties. By a time-resolved X-ray scattering technique, we observe a rapid lattice expansion upon photoexcitation, followed by a slow relaxation over the course of 100 ns in the dark. Theoretical modeling suggests that the expansion originates from the lattice’s thermal fluctuations caused by photon energy deposition. Power dependent optical spectroscopy and photoconductivity indicate that high laser powers triggered a strong local structural disorder, which increased the charge dissociation activation energy that results in localized transport. Finally, our study investigates the impact of laser energy deposition on the lattices and the subsequent carrier transport properties, that are relevant to device operations.

2D perovskites↗
DOE OSTI2026

Toward durable stacks: glass-ceramic sealants for intermediate-temperature protonic ceramic electrochemical systems

Protonic ceramic electrochemical cells (PCECs) are emerging as promising technologies for efficient energy conversion and hydrogen production because they operate at intermediate temperatures with improved efficiency and durability compared with conventional solid oxide electrochemical cells. However, the long-term reliability and commercialization of PCEC stacks remain strongly limited by the performance of sealants, which are required to maintain gas tightness, electrical insulation, and mechanical integrity under harsh thermal and chemical environments. Among various sealing approaches, glass-ceramic sealants are considered the most practical and scalable due to their excellent wettability, chemical tunability, and strong interfacial adhesion. This review provides a comprehensive overview of recent advances in glass-ceramic sealants for intermediate-temperature protonic ceramic electrochemical systems. The fundamental design principles of sealant compositions are first discussed, followed by recent developments in deposition methods, sintering strategies, surface treatments, and degradation monitoring techniques. Particular attention is given to the unique challenges associated with PCEC operating conditions, including hydrothermal degradation, interfacial reactions with barium-containing electrolytes, and thermal mismatch. Finally, future opportunities involving sustainable materials, multiscale modeling, additive manufacturing, and artificial intelligence-assisted sealant optimization are highlighted.

glass–ceramic sealants↗
DOE OSTI2024

Inexpensive and durable aerogel-based VIP cores

The goal of this project was development of low-cost aerogels to be used as cores of Vacuum Insulated Panels (VIP). The specific aims were: 1. Aerogels to be based on phenolic chemistry to minimize flammability. 2. Aerogels to be in monolithic form and to have mechanical properties sufficient to allow their use as cores of VIPs without collapsing after evacuation. 3. Aerogels to have a cost per kg lower than the cost of fumed silica, one of the most common VIP cores. The achievements are: 1. Phenolic aerogels have been fabricated in monolithic form with thermal conductivity of 0.023 W/m·K, density ~ 0.25 g/cm 3 and a modulus of compression > 10 MPa. The aerogels can withstand the stresses of VIP evacuation without any appreciable deformation. 2. Drying of aerogels was carried in the initial stages of the project via sublimation of frozen solvents, following the procedure described in M. F. Bertino and T. Selden, “Fabrication of Aerogels and Aerogel Composites by Ambient Pressure Sublimation of Frozen Solvents”, PCT/US20/39485. Monoliths as large as 30 x 30 x 2 cm can be fabricated with the proprietary ambient fabrication technique which are free of cracks and warping. 3. Cost analysis was carried out and refined multiple times, cost drivers were identified, and the fabrication procedure was streamlined. Our cost model yields an aerogel cost of $\$$5.78/kg, which compares well with the price of fumed silica ($\$$6.2/kg FOB New York as of March 2023). 4. The monoliths can be cut into custom shapes and used as VIP cores. Cores fabricated with our technique have a thermal conductivity λ = 0.011 W/m·K at a pressure >10 mbar. 5. A variant of the technology yielded fireproof materials, described in M. F. Bertino, T. Moon, G. Waller, J. Ko and D. Clifford, “Fireproof thermal insulation”, Provisional Patent Application 63/415,386. 6. A spin-off company was created (ThermaGel Innovations, Inc.) which licensed technologies developed under this award. PI Bertino is a board member of the newco which is now raising capital to build a pilot plant.

36 MATERIALS SCIENCE↗