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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Meeting sustainable aviation fuel policy targets through first generation corn biorefineries

This paper investigates options to produce low carbon and renewable aviation fuel through existing commercial first generation biorefineries, which could be strategic given the rapid growth of the electric vehicle market for personal mobility. We evaluate the environmental impact of retrofitting a first generation biorefinery to produce aviation fuel through chemical process simulation. The analysis investigates the potential for reducing greenhouse gas (GHG) emissions and displacing fossil fuel (petroleum-based jet kerosene) in the aviation fuel industry through using corn feedstocks in dry grind biorefineries to convert sugars to 1,4-dimethylcyclooctane (DMCO), which qualifies as renewable jet-A fuel blend and is infrastructure compatible. Further, the average life cycle GHG emissions for a baseline case and a scenario with carbon capture and storage (CCS) of fermentative CO 2 are 36 and 5g CO 2 e/MJ DMCO, respectively. Investment in CCS at the biorefinery and adoption of crop best management practices on farms are essential for mitigating the risks of induced land use change GHG emissions for existing corn biorefineries. DMCO as a low-carbon aviation fuel could be a strategic fuel product for retrofitting existing corn dry grind facilities. For aviation fuel supply, corn dry grind facilities could displace about 12% of jet fuel demand in the near term, which would meet sustainable aviation fuel policy targets. We conclude that corn-to-DMCO shows promise for near-term low-carbon fuel markets in the aviation industry and as a bridge to developing biomass-based jet fuel in the future.

09 BIOMASS FUELS↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2021

The development of a Sustained High Power Density (SHPD) facility

A Sustained High Power Density (SHPD) facility is being planned within the U.S. as a non DT device which can be a bridge to a Compact Fusion Pilot Plant (CFPP) DT operated facility with the future capacity to produce a levelised cost of electricity at a rate competitive with current power generating systems. To accomplish this a review of ITER cost data and recent next step physics/engineering studies that promoted technology development, physics enhancements and innovated configuration improvements that increase operating availability have been injected into a proposed PPPL SHPD design. Much of the physics and engineering design basis for this effort centers on a series of ST studies carried out in recent years attempting to maximize the low aspect ratio (AR) ST performance within realistic engineering constraints of this compact fusion device. One issue found in the low AR design is the lack of volt-seconds generated by a small central solenoid that resides in the available space left after sizing the TF structure to support the high current density/HTS TF coil. Although not required in a larger pilot plant device, a solenoid wrapped around the plasma side of the SC TF coil was considered for the small low aspect ratio SHPD device. Adding a solenoid to the plasma side of the TF imposes a major change to the device configuration – a change which has been found to have cost and assembly issues when compared with an OH solenoid located at the machine center. Finally, the inclusion of a liquid metal first wall divertor system, the integration of local pairs of outboard DCLL blanket segments and a summary of physics performance conditions will be presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Mechanisms of dissolution from gibbsite step edges elucidated by ab initio molecular dynamics with enhanced sampling

Surface reactions underpin our collective understanding of mineral dissolution. This impacts a range of predictive geochemical models (for weathering, the fate and transport of metals) as well as industrial utilization of minerals. Yet atomistic details are rarely known due to the complex mineral/fluid interfacial environment. There is significant need to understand the mechanistic details of dissolution reactions and how they depend on surface morphology and solution conditions. This work utilizes surface pit models in conjunction with changes to solution composition that mimic pH to explore surface detachment of aluminate from gibbsite, which is a primary source of Al in soils and within the industrial processing of aluminum. Ab initio molecular dynamics simulations with enhanced sampling has been used to explore the detailed process of the detachment, the results of which indicate two potential pathways that are differentiated based upon the extent of water hydration. Here, the heights of the energy barriers depend upon the local morphology which influence the number of bridges (quasi-)simultaneously broken (1 or 2) or the Al-O coordination of the neighboring aluminum atoms (5 or 6) at the armchair edge. pH effects are significant, with a nearly 50% reduction in barrier height under alkaline conditions that are relevant to geothermal fluids and Al extraction from minerals.

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

Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships

Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional- and global-scale soil–environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg−1 averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.

Deep learning↗
DOE OSTI2026

Self-supervised and multi-fidelity learning for extended predictive soil spectroscopy

Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigation of methane mass transfer and sorption in Marcellus shale under variable net-stress

Natural gas in shale exists as free and adsorbed gas, subject to prevailing pore pressures and stress conditions. Accordingly, to accurately estimate/predict the shale gas recovery potential, a central requirement is to represent gas transport and sorption behavior under varying stress conditions. The objective of this work is to facilitate the interpretation of laboratory-scale experiments, at relevant conditions, in an attempt to bridge the gap in scales between laboratory- and field-scale observations. We have conducted a series of high-pressure experiments on a full-diameter core sample from the Marcellus shale. These include gas loading (pressure-decay) and depletion (production) experiments with pure methane (CH 4 ) at variable stress conditions to characterize transport and sorption behavior under reservoir-relevant conditions. Here we have formulated and applied a novel integral model for mass transfer and storage in multi-porosity shale systems that allows us to effectively investigate transport and sorption phenomena: We delineate gas transport by interpreting helium (He) pressure-decay experiments and demonstrate how to use the information gained to calculate the relevant transport coefficients of CH 4 and other gases. A separate measurement of the CH 4 sorption isotherm on a smaller sample (a shale cube) was interpreted and combined with the transport description to predict the production behavior of CH 4 from the experiments with the full-diameter core. Our experiments demonstrate that the representation of sorption hysteresis is crucial for predicting and guiding shale gas production: At the end of both gas production experiments, approximately 20% of the initial gas in place remained in the core. Without accounting for sorption hysteresis, our modeling demonstrates that the CH 4 production could be overestimated by 10%. We demonstrate that our integral, triple-porosity model provides an effective approach for the interpretation and prediction of gas transport and sorption behavior during loading and production experiments on shale cores under variable net-stress conditions. In summary, our work combines measurements and modeling of mass transfer and sorption in shales at different scales to validate a characterization approach that facilitates an improved understanding of shale gas production. Furthermore, the triple-porosity model utilized in our work defines a potential pathway for the translation of laboratory-scale experimentation to larger-scale applications.

58 GEOSCIENCES↗
DOE OSTI2024

Time-dependent thermal degradation of lost circulation materials in geothermal systems

Treatment of lost circulation can represent anywhere from 5 to 25 % of the cost in drilling geothermal wells. The cost of the materials used for lost circulation treatment is less important than their effectiveness at reducing fluid losses. In geothermal systems, the high temperatures (>90 °C) are expected to degrade many commonly used lost circulation materials over time. This degradation could compromise different materials ability to mitigate fluid loss, creating more non-productive time as multiple treatments are needed, but may result in recovering desired permeability zones within the reservoir section over time. This research aimed to study how thermal degradation of eight different lost circulation materials affected their properties relevant to sealing loss zones in geothermal wells. Here, mass loss experiments were conducted with each material at temperatures of 90–250 °C for 1–42 days to measure the breakdown of the material at geothermal conditions, collecting gases during several experiments to determine the waste produced during degradation. Compaction experiments were conducted with the degraded materials to show how temperatures reduced the rigidity and increased packing of the materials. Viscosity tests were conducted to show the impact of different materials on drilling fluid rheology. Microscope observations were conducted to characterize the alterations to each material due to thermal degradation. Organic materials tend to degrade more than inorganic materials, with organics like microcellulose, cotton seed hulls and sawdust losing 30–50 % of their mass after 1 day of heating at 200 °C, while inorganics like magma fiber only lose ~5–10 % of its mass after one day of heating at 200 °C. Granular materials are the strongest when compacted despite any mass loss, while fibrous and flaky materials are fairly weak and breakdown easily under stress. The materials do not generally affect fluid rheology unless they have a viscosifying agent as part of the mixture. Microscopic analysis showed that more rigid materials like microcellulose and cedar fiber degrade in brittle manners with splitting and fracturing, while others like cotton seed hulls degrade in more ductile manners forming meshes or clumps of material. The thermal breakdown of lost circulation materials tested suggests that each material should also be classified by its degree of thermal degradability, as at certain temperatures the materials can lose the capability to bridge loss zones around the wellbore.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we nd that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman ltering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗
DOE OSTI2021

A physics-based model for frost buildup under turbulent flow using direct numerical simulations

We present a new model for frost buildup under turbulent (and laminar) flow using direct numerical simulations. The physical model consists of two layers, the air and the frost. The air layer is fully resolved and consists of solving for the velocity, temperature, and vapor mass fraction fields. The frost layer thickness is resolved using conservation of mass and energy. Both phases are dynamically coupled using the immersed boundary method. Three-dimensional simulations are conducted in an open-channel configuration. A number of challenges need to be overcome to make these simulations feasible. First, to enforce far-field conditions of zero gradient and prescribed mean temperature and humidity, a source term is added to the energy and transport equations in the flow solver. Second, the mean frost thickness is subtracted after each time step to ensure a constant mean flow thickness and level of turbulence in the numerical domain. Third, a slow-time acceleration approach, which accelerates the frost buildup by a predetermined factor, is employed to bridge the gap between the fast turbulent and slow frost buildup time scales. Finally, a frost densification scheme is used to overcome the difficulties of vertically varying frost properties. The model is validated by comparing the frost thickness and frost thickness buildup rate over a period of one hour from a cooled flat plate experiment. As a result, both quantities compare favorably with experiments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Numerical simulation of falling film sensible heat transfer over round horizontal tubes

Numerical simulations are performed to explore the heat transfer characteristics of falling films over horizontal round tubes with uniform heat flux imposed for a range of Reynolds numbers spanning the droplet, jet, and sheet regimes. Simulations results agree well with available experimental measurements. The study analyzes the local as well as average heat transfer behavior under the different flow modes. The numerical results show that the local Nusselt number (Nu) distribution depends on the flow features in each mode and varies substantially in all directions for the respective mode. In the droplet mode, the Nu value varies significantly as the droplet impinges and the remnant liquid-bridge retracts (peak instantaneous Nu near 6), followed by wave propagation over the tube surface with peak Nu around 0.25. For the jet modes, the local maximum in the heat transfer occurs off-center to the impingement location with magnitudes of peak Nu = 3.1 for the inline jet mode and Nu = 2.7 for the staggered jet mode, while on the rest of the tube surface, it has an inverse relation with the liquid film thickness. Substantial variations in the heat transfer value are also recorded in the middle of the two impinging jets with Nu = 0.95 in the inline jet mode where the neighboring jets do not interact, and Nu = 0.60 in the crest region of the staggered jet mode where the neighboring jets interact with each other. In the sheet mode, the Nu was seen to depend on the thickness of the liquid waves traversing over the tube surface. Lower Nu values were recorded beneath the crest location of the liquid waves, which increases (1.4–11.6% depending on circumferential location) abruptly in magnitude at the advancing fronts of the waves. The temperature distribution in the liquid film in each of the modes was examined to evaluate the mechanism of heat transfer process. This study also compares the local heat transfer coefficient distribution with the analytical heat transfer models derived to predict heat transfer performance over horizontal tube surfaces.

42 ENGINEERING↗
DOE OSTI2023

Differential multi-probe thermal transport measurements of multi-walled carbon nanotubes grown by chemical vapor deposition

Carbon nanotubes (CNTs) are quasi-one dimensional nanostructures that display both high thermal conductivity for potential thermal management applications and intriguing low-dimensional phonon transport phenomena. In comparison to the advances made in the theoretical calculation of the lattice thermal conductivity of CNTs, thermal transport measurements of CNTs have been limited by either the poor temperature sensitivity of Raman thermometry technique or the presence of contact thermal resistance errors in sensitive two-probe resistance thermometry measurements. Here we report advances in a multi-probe measurement of the intrinsic thermal conductivity of individual multi-walled CNT samples that are transferred from the growth substrate onto the measurement device. The sample-thermometer thermal interface resistance is directly measured by this multi-probe method and used to model the temperature distribution along the contacted sample segment. The detailed temperature profile helps to eliminate the contact thermal resistance error in the obtained thermal conductivity of the suspended sample segment. A differential electro-thermal bridge measurement method is established to enhance the signal-to-noise ratio and reduce the measurement uncertainty by over 40%. The obtained thermal resistances of multiple suspended segments of the same MWCNT samples increase nearly linearly with increasing length, revealing diffusive phonon transport as a result of phonon-defect scattering in these MWCNT samples. The measured thermal conductivity increases with temperature and reaches up to 390 ± 20 W m –1 K –1 at room temperature for a 9-walled MWCNT. In conclusion, theoretical analysis of the measurement results suggests submicron phonon mean free paths due to extrinsic phonon scattering by extended defects such as grain boundaries. The obtained thermal conductivity is decreased by a factor of 3 upon electron beam damage and surface contamination of the CNT sample.

42 ENGINEERING↗
DOE OSTI2025

Integrated CO 2 capture and hydrogenation in presence of Ru–Na 2 ZrO 3 : An in-situ study

Integrated CO 2 capture and conversion (ICCC) by hydrogenation is a promising strategy to utilize carbon dioxide and this work add to the effort to elucidate the catalytic hydrogenation mechanism using Ru based dual functional materials (DFM). Ru-Na 2 ZrO 3 DFMs, obtained through different wet methods, were evaluated for the first time and the relationship between Ru and support systematically investigated. The thermally stable and cyclable Ru-Na 2 ZrO 3 -a (obtained without filtration step) exhibited CO 2 conversion of 80% and a higher yield of CO at 400°C compared to previously tested DFM, while the Na depleted/Zr rich Ru-Na 2 ZrO 3 -b resulted in 90% selectivity to CH 4 with yield of 1.11 mmol/g at the same temperature. The in-situ experiments have provided conclusive evidence showing that CO 2 hydrogenation on the two Ru DFMs is fundamentally different. In Ru-Na 2 ZrO 3 -a, the monoclinic Na 2 ZrO 3 support acted as the active centre (not as promoter) for CO 2 bridging binding and hydrogenation to CH 4 at the metal-support interface through associative formate pathway with limited further reduction to methane due to lack of H 2 spillover from the small and well dispersed Ru NPs, which results in CO desorption. Conversely, abundant clusters of larger Ru NPs in Ru-Na 2 ZrO 3 -b, led to CH 4 production due to co-existent Ru on-top direct dissociation of CO 2 (preferential) and monodentate formate adsorption and further methanation. Alkali zirconates doped metals, and their synthesis method could thus play a crucial role in designing tuneable heterogeneous catalysis in C 1 chemistry, which could significantly benefit the environment by lowering CO 2 levels, encouraging cleaner industrial practices, supporting a circular economy, and converting waste CO 2 into valuable products.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗
DOE OSTI2020

Size- and concentration-dependent Eu 2+ /Eu 3+ mixed luminescent characteristics of rare-earth-doped CaF 2 nanoparticles and their monolithic epoxy nanocomposites

CaF 2 is a promising luminescent host material for a wide range of applications, due to its high transparency, non-hygroscopy, low refractive index, low density, low backscattering efficiency, and abundance in nature. Here we report the photoluminescence (PL) and cathodoluminescence (CL) characteristics of rare-earth-doped CaF 2 nanoparticles and their composite monoliths with epoxy with visible emissions. The CaF 2 nanoparticles of ca. 36, 108 and 305 nm were prepared by a facile solution-mixing process and subsequent hydrothermal treatment. As the particle size decreases, the luminescence intensity increases for single-doped particles, while oppositely decreases for codoped particles because of energy transfer. Further, Eu 2+ - and Tb 3+ -codoped CaF 2 nanoparticles exhibit red emission, providing an efficient path for red emission through an energy transfer by Tb 3+ bridging without direct Eu 3+ doping. The effects of size and concentration are found to be pronounced in CL than PL, as the charge transfer between Eu 2+ and Eu 3+ reduces the concentration quenching in CL. The luminescence emission characteristics of epoxy/CaF 2 monoliths are different from those of particles due to the interactions with the epoxy molecules, and thus the energy transfer is hindered in PL while not in CL, enabling them for high-energy irradiation applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Metal forming and working of stabilized nanocrystalline Cu-Ta for electrical contacts

The commercialization of nanocrystalline metals and alloys is currently entering a renaissance period. Many of the processing and consolidation challenges that have haunted them are now more fully understood, opening the doors for stabilized nanocrystalline metals to be produced on a bulk scale. While challenges remain, the increased volume at which these materials are being supplied is for the first time allowing for investigations into more traditional methods of metal working, such as extruding, rolling, forming, and forging. Recently, the manufacturing science has been developed to allow nanocrystalline Cu-Ta alloys to progress to this point. This article therefore builds upon the last decade of evolutionary progression within the family of stabilized nanocrystalline Cu-Ta alloys by presenting some of the first findings related to scaled powder synthesis, production of billets, thin sheets, and foils. Here, the mechanical performance and physical properties relevant to forming electrical contacts and pins including, tensile, J-integral fracture toughness, Charpy Impact, bi-axial tension, and conductivity are reported. This introductory investigation into forming such a novel material, provides evidence to these alloys potential at bridging the gap between being a scientific curiosity to that of a real engineering material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nitrogen: A promising doping strategy for high-performance ovonic threshold switching selectors

The Ovonic Threshold Switching (OTS) selector serves as an essential component in the development of three-dimensional high-density memory integration technology. Nevertheless, the state-of-the-art high-performance OTS materials usually contain toxic elements such as arsenic (As), posing significant risks to both environmental and human health. Nitrogen (N), which belongs to the same group as arsenic (As), has emerged as a highly promising alternative for As doping. However, the underlying mechanisms that govern N-based OTS materials have not yet been extensively investigated. In this study, we delve into the effects of N doping on the structural, bonding, and electronic properties of amorphous GeSe (a-GeNSe) by ab initio molecular dynamics simulations to bridge the knowledge gap. Our findings indicate that upon N doping in a-GeSe, the formation of robust Ge-N bonds, along with N-centered tetrahedral and triangular structures, resulting in the sluggish atomic movement that enhances the thermal stability and endurance of a-GeNSe. The OTS characteristics are significantly influenced by the material’s electronic band structure, and thus the relatively slow performance drift can be attributed to the stabilization of mid-gap states, a result of N doping which effectively slows down the aging process of chalcogenide glass. Moreover, the increased mobility gap in a-GeNSe raises the threshold voltage (V th ), making it more compatible with commercially available phase-change memory materials. Furthermore, our findings reveal the extensive impact of the N element on a typical OTS material and offer valuable perspectives for alternative doping strategies that could potentially supplant As practices.

36 MATERIALS SCIENCE↗