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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
DOE OSTI2021

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Engineered disorder in CO 2 photocatalysis

Light harvesting, separation of charge carriers, and surface reactions are three fundamental steps that are essential for an efficient photocatalyst. Here we show that these steps in the TiO 2 can be boosted simultaneously by disorder engineering. A solid-state reduction reaction between sodium and TiO 2 forms a core-shell c-TiO 2 @a-TiO 2-x (OH) y heterostructure, comprised of HO-Ti-[O]-Ti surface frustrated Lewis pairs (SFLPs) embedded in an amorphous shell surrounding a crystalline core, which enables a new genre of chemical reactivity. Specifically, these SFLPs heterolytically dissociate dihydrogen at room temperature to form charge-balancing protonated hydroxyl groups and hydrides at unsaturated titanium surface sites, which display high reactivity towards CO 2 reduction. This crystalline-amorphous heterostructure also boosts light absorption, charge carrier separation and transfer to SFLPs, while prolonged carrier lifetimes and photothermal heat generation further enhance reactivity. The collective results of this study motivate a general approach for catalytically generating sustainable chemicals and fuels through engineered disorder in heterogeneous CO 2 photocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Electric accumulation of millicharged particles

A terrestrial population of millicharged particles that interact significantly with normal matter can arise if they make up a dark matter subcomponent or if they are light enough to be produced in cosmic-ray air showers. Such particles thermalize to terrestrial temperatures through repeated scatters with normal matter in Earth’s environment. We show that a simple electrified shell (for example, a Van de Graaff generator) functions as an efficient accumulator of such room-temperature millicharged particles, parametrically enhancing their local density by as much as 12 orders of magnitude. This can be used to boost the sensitivity of any detector housed in the shell’s interior, such as ion traps and tests of Coulomb’s law. In a companion paper, we apply this specifically to Cavendish tests of Coulomb’s law, and we show that a well-established setup can probe a large region of unexplored parameter space, with sensitivity to the irreducible density of millicharged particles generated from cosmic rays that outperforms future accelerator searches for sub-GeV masses.

Berlin, Asher [Fermilab] (ORCID:0000000211561482)↗
DOE OSTI2023

CMOS-Compatible Ultrathin Superconducting NbN Thin Films Deposited by Reactive Ion Sputtering on 300 mm Si Wafer

We report a milestone in achieving large-scale, ultrathin (~5 nm) superconducting NbN thin films on 300 mm Si wafers using a high-volume manufacturing (HVM) industrial physical vapor deposition (PVD) system. The NbN thin films possess remarkable structural uniformity and consistently high superconducting quality across the entire 300 mm Si wafer, by incorporating an AlN buffer layer. High-resolution X-ray diffraction and transmission electron microscopy analyses unveiled enhanced crystallinity of (111)-oriented δ-phase NbN with the AlN buffer layer. Notably, NbN films deposited on AlN-buffered Si substrates exhibited a significantly elevated superconducting critical temperature (~2 K higher for the 10 nm NbN) and a higher upper critical magnetic field or H c2 (34.06 T boost in H c2 for the 50 nm NbN) in comparison with those without AlN. These findings present a promising pathway for the integration of quantum-grade superconducting NbN films with the existing 300 mm CMOS Si platform for quantum information applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Climate warming is expected to increase global methane (CH 4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH 4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH 4 remain uncertain. Here, we use established drivers to hindcast changes in CH 4 fluxes (FCH 4 ) since the early 1980s. We trained a machine learning (ML) model on CH 4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH 4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH 4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH 4 , with increases in FCH 4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high-CH 4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH 4 (defined according to the 95th percentile of the daily FCH 4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH 4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH 4 emission seasons and boosting extreme emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗
DOE OSTI2020

Unveiling the critical role of interfacial ionic conductivity in all-solid-state lithium batteries

Advancement of all-solid-state lithium-ion (Li + ) batteries (ASSLIBs) has been hindered by the large interfacial resistance mainly originating from interfacial reactions between oxide cathodes and solid-state sulfide electrolytes (SEs). To suppress the interfacial reactions, an interfacial coating layer between cathodes and SEs is indispensable. However, the kinetics of interfacial Li + transport across the coating layer has not been well understood yet. Herein, we tune the interfacial ionic conductivity of the coating layer LiNb 0.5 Ta 0.5 O 3 (LNTO) by manipulating post-annealing temperature. It is found that the interfacial ionic conductivity determines interfacial Li + transport kinetics and enhancing the interfacial ionic conductivity can significantly boost the electrochemical performance of SE-based ASSLIBs. A representative cathode LiNi 0.5 Mn 0.3 Co 0.2 O 2 coated by LNTO with the highest interfacial ionic conductivity exhibits a high initial capacity of 152 mAh.g -1 at 0.1 C and 107.5 mAh.g -1 at 1C. This work highlights the importance of increasing interfacial ionic conductivity for high-performance SE-based ASSLIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

W-band system-on-chip electron cyclotron emission imaging system on DIII-D

Monolithic, millimeter wave “system-on-chip” (SoC) technology has been employed in heterodyne receiver integrated circuit radiometers in a newly developed Electron Cyclotron Emission Imaging (ECEI) system on the DIII-D tokamak for 2D electron temperature profile and fluctuation evolution diagnostics. Here, a prototype module operating in E-band (72-80 GHz) was first employed in a 2 x 10 element array that demonstrated significant improvements over the previous quasi-optical Schottky diode mixer arrays during the 2018 operational campaign of the DIII-D tokamak. For compatibility with International Thermonuclear Experimental Reactor (ITER) relevant scenarios on DIII-D, the SoC ECEI system was upgraded with 20 horn-waveguide receiver modules. Each individual module contains a University of California at Davis designed W-band (75 -110 GHz) receiver die that integrates a broadband low noise amplifier (LNA), a double balanced down-converting mixer, and a x4 multiplier on the local oscillator (LO) chain. A x2 multiplier and two IF amplifiers are packaged and selected to further boost the signal strength as well as downconvert the signal frequency. The upgraded W-band array exhibits > 30 dB additional gain and 20x improvement in noise temperature comparing with the previous Schottky diode radio frequency (RF) mixer input systems; an internal 8 times multiplier chain is used to bring down the LO frequency below 12 GHz, thereby obviating the need for a large aperture for quasi-optical LO coupling and replacing it with coaxial connectors. The horn-waveguide shielding housing avoids out-of-band noise interference on each individual module. The upgraded ECEI system plays an important role for absolute electron temperature evolution and fluctuation measurements for edge and core region transport physics studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models

To support the growth of supercritical carbon dioxide (sCO2) power cycles in the energy industry, this study seeks to train a machine learning model to mirror experimental data to predict new heat transfer data. To do this experimental data was amassed, one preliminary set comprised of 16 test results, and an expanded version comprised of 38 test results. With the goal of predicting experimental apparatus temperatures and pressures, several iterations of models were tested investigating the impact of model hyper-parameters, data inclusion, and data pre-processing on model performance. A total of 15 variations cumulatively of Gaussian Process Regressors, Gradient Boosting Regressors, and Multi-Layer Perceptrons were trained and validated on the preliminary set, and the best algorithm of each class was re-trained on the expanded set. These were compared based on test/train R^2 , test/train mean absolute error (MAE), and validation MAE, to identify the successfulness of these models. It was shown temperatures could be predicted within just a few degrees, showing the potential of this approach. Future research has been identified with approaches to improve pressure and temperature predictions going forward.

Grabowski, Owen↗
DOE OSTI2026

Enhancing mobility and interface state engineering via UV-ozone treatment in BEOL-compatible ultrathin TiO 2 transistors

It has emerged as a potential candidate to improve the performance of monolithic-three-dimensional (M3D) integration of fused logic and memories through low-temperature in situ synthesis of high-performance metal–oxide–semiconductor (MOS) transistors. Here, we report the demonstration of the BEOL-compatible low thermal budget (350 °C) fabrication process of ultrathin-TiO2 transistors by the combination of RTA and UV-ozone treatment (RTA-UVOz). UV–ozone (UVOz) treatment of TiO2 anatase films significantly enhances stability, boosting ION current and field effect mobility (μ FE ) by two times of magnitude in TiO2 TFTs. UV ozone treatment helps to eliminate pre-existing oxygen vacancies and carbon contamination on TiO2 channels even at low temperature (100 °C), resulting in high-quality channel/dielectric interfaces with low interface states (D it ). During UVOz treatment, oxygen species (O x ) passivate the oxygen vacancies ($V$$^{2+}_{o}$), and hence low concentration of $V$$^{2+}_{o}$ would be left for ionization/de-ionization under PBS/NBS, leading to improved bias stress stability. Furthermore, the TiO 2 TFTs with thin ZrO 2 gate dielectric exhibited excellent performance including a lower subthreshold swing (SS) of 98 mV/dec with high drive I ON current ∼ 4.5 μA/μm, a high I ON /I OFF > 10 9 , and mobility μFE of 7 cm 2 /V-s under a battery powered voltage of 1 V. UV–ozone treatment enables high-performance, CMOS-compatible TiO 2 transistors with a low thermal budget, ideal for next-generation flexible, energy-efficient electronics.

BEOL↗
DOE OSTI2021

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Boosting SNR of cascaded FBGs in a sapphire fiber through a rapid heat treatment

This Letter reports the performance of femtosecond (fs) laser-written distributed fiber Bragg gratings (FBGs) under high-temperature conditions up to 1600°C and explores the impact of rapid heat treatment on signal-to-noise ratio (SNR) enhancement. FBGs are essential for reliable optical sensing in extreme temperature environments. Comprehensive tests demonstrate the remarkable performance and resilience of FBGs at temperatures up to 1600°C, confirming their suitability for deployment in such conditions. Here, the study also reveals significant fringe visibility improvements of up to ∼10 dB on a 1-m-long sapphire optical fiber through rapid heat treatment, representing a first-time achievement to the best of our knowledge. These enhancements are vital for improving the SNR and overall performance of optical fiber systems in extreme temperatures. Furthermore, the research attains long-term stability for the cascaded FBGs over a 24-hr period at 1600°C. This research expands our understanding of the FBG behavior in high-temperature environments and opens avenues for developing robust optical fiber systems for energy, aerospace, oil and gas, and high-temperature distributed sensing applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Boosting the H 2 –D 2 Exchange Activity of Dilute Nanoporous Ti–Cu Catalysts through Oxidation–Reduction Cycle–Induced Restructuring

The use of nanoporous metals as catalysts has attracted significant interest in recent years. Their high-curvature, nanoscale ligaments provide not only high surface area but also a high density of undercoordinated step edge and kink sites. However, their long-term stability, especially at higher temperatures, is often limited by thermal coarsening and the associated loss of surface area. Herein, it is demonstrated that the nanoscale morphology of nanoporous Cu can be regenerated by applying oxidation/reduction cycles at 250 °C. Specifically, the morphological evolution and H 2 dissociation activity of hierarchical nanoporous Cu catalysts doped with Ti during structural rearrangement triggered by oxidative and reductive atmospheres at elevated temperatures are studied. In addition to coarsening of the structure at elevated temperatures, oxidation at 400 °C causes an expansion of the ligaments. Further, subsequent reduction at 400 °C leads to the formation of particles and a drop in the H 2 dissociation activity compared the fresh catalyst. However, performing the redox cycle at 250 °C reverses coarsening and boosts the H 2 dissociation activity for the hydrogen–deuterium (H 2 –D 2 ) reaction. Herein, the possibility to reverse coarsening is demonstrated, thereby mitigating the loss of activity frequently observed in nanoporous catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Organizing Chaos: Boosting Thermoelectric Properties by Ordering the Clathrate Framework of Ba 8 Cu 16 As 30

The type I clathrate, Ba 8 Cu 16 As 30 , is reinvestigated and found to have a low-temperature polymorph mP 108-Ba 8 Cu 16 As 30 with ordered Cu and As sites. In situ temperature-dependent powder X-ray diffraction experiments guided synthetic efforts toward the synthesis of the ordered monoclinic ( mP 108) and disordered cubic ( cP 54) polymorphs with high phase purity. While a transition from mP 108-Ba 8 Cu 16 As 30 to cP 54-Ba 8 Cu 16 As 30 is not directly observed, cP 54-Ba 8 Cu 16 As 30 is stabilized through quenching from high temperatures and is confirmed through high-resolution synchrotron powder X-ray diffraction. Further, combined theoretical predictions and experimental observations of the thermoelectric properties of both polymorphs reveal that the ordering of Cu and As atoms in the clathrate framework simultaneously enhances the Seebeck coefficient and electronic conductivity by increasing the hole effective mass and reducing the electronic scattering events. Consequently, the zT of mP 108-Ba 8 Cu 16 As 30 reaches a maximum of 0.2 at 575 K, an order of magnitude higher than that of cP 54-Ba 8 Cu 16 As 30 .

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

Atomistic Simulations of Thermal and Chemical Expansions of PrNi x Co 1‐x O 3‐δ Accelerated by Machine Learning Potentials

The electrodes and solid-state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machine learning potential (MLP) are employed to elucidate the volume expansions of the proton-conducting PrNi x Co 1-x O 3-δ (PNC) lattices, manifested under a combined influence of oxygen vacancies (V$^{\cdot\cdot}_O$ ) and proton uptake (OH$^{\cdot}_O$ ) in the bulk at varying Ni/Co occupancies. It is revealed that the Ni/Co occupancy contributes to thermal and chemical expansions differently, where thermal expansions are related to Co occupancy. In contrast, chemical expansions are more closely associated with the Ni occupancy. Both V$^{\cdot\cdot}_O$ and OH$^{\cdot}_O$ lead to higher thermal expansions when compared to the pristine PNC. The temperature increase will negatively impact the hydration-induced chemical expansions. For combined thermal and chemical expansions, it is predicted that the strategies that boost the PCEC's electrochemical performance may harm the electrode–electrolyte interfacial stability, when the Ni occupancy is high, due to severe chemical expansions. Mitigating chemical expansions of the Ni-abundant PNC will benefit the interfacial stability. Finally, the presented computational methods for phonon calculations, based on emerging machine learning interatomic potential techniques are anticipated to have a lasting impact on future PCEC development.

computational prediction↗
DOE OSTI2020

High-Efficiency Silicon Heterojunction Solar Cells: Materials, Devices and Applications

Photovoltaic (PV) technology offers an economic and sustainable solution to the challenge of increasing energy demand in times of global warming. The world PV market is currently dominated by the homo-junction crystalline silicon (c-Si) PV technology based on high temperature diffused p-n junctions, featuring a low power conversion efficiency (PCE). Recent years have seen the successful development of Si heterojunction technologies, boosting the PCE of c-Si solar cells over 26%. This article reviews the development status of high-efficiency c-Si heterojunction solar cells, from the materials to devices, mainly including hydrogenated amorphous silicon (a-Si:H) based silicon heterojunction technology, polycrystalline silicon (poly-Si) based carrier selective passivating contact technology, metal compounds and organic materials based dopant-free passivating contact technology. The application of silicon heterojunction solar cells for ultra-high efficiency perovskite/c-Si and III-V/c-Si tandem devices is also reviewed. In the last, the perspective, challenge and potential solutions of silicon heterojunction solar cells, as well as the tandem solar cells are discussed.

14 SOLAR ENERGY↗
DOE OSTI2020

Effects of isoalcohol blending with gasoline on autoignition behavior in a rapid compression machine: Isopropanol and isobutanol

Alcohols, and particularly isoalcohols, are potentially advantageous blendstocks towards achieving efficient, low-carbon intensity internal combustion engines. Their use in advanced configurations, such as boosted spark-ignition or spark-assisted compression ignition, requires a comprehensive understanding of their blending effects on the low- and intermediate-temperature autoignition behavior of petroleum-derived gasoline. This work reports an experimental and modeling study of such autoignition characteristics quantified in a twin-piston rapid compression machine. Isopropanol and isobutanol are blended into a research-grade gasoline (FACE-F) at oxygenate blend levels of 0 to 30% vol/vol, with tests conducted at pressures of 20 and 40 bar, temperatures from 700 to 1000 K, and dilute stoichiometric fuel loadings. Changes to overall reactivity, including first-stage and main ignition times, and preliminary exothermicity are established, with comparisons made to previous measurements with ethanol-blended FACE-F gasoline. Furthermore, it is found that at low-temperature/NTC conditions (700–860 K) the isoalcohols suppress first-stage reactivity and associated heat release while main ignition times are extended. At NTC/intermediate-temperature (860–1000 K) conditions changes to fuel reactivity are less significant with isopropanol slightly suppressing reactivity and isobutanol promoting ignition. Detailed chemical kinetic modeling is used to interpret the experimental measurements. Overall trends of suppression or promotion in the blending behavior are reasonably captured by the model. Sensitivity and rate of production analyses indicate that at lower temperatures H-atom abstraction reactions from the surrogate fuel molecules (e.g., cyclopentane, isooctane) and the isoalcohols via OH are important leading to TC 3 H 6 OH and IC 4 H 8 OH–C radicals, for isopropanol and isobutanol respectively, which act as scavengers in the system. At higher temperatures, similar chemistries are dominant, but there is an increasing importance of abstraction by HO 2 . The kinetic modeling also indicates that the promoting effect of isobutanol at higher temperatures is due to the increased abstractions at the γ-sites, while at lower temperatures abstraction at the α-site leads to greater reactivity suppression.

36 MATERIALS SCIENCE↗