Search NASA⌕ Search

SEARCH · Search NASA

Results for “Deep potential”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma: Preprint

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗
DOE OSTI2022

Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1995

Long-wavelength Radar Studies of the Lunar Maria

Radar measurements at 70 cm and 7.5 m wavelengths provide insight into the structure and chemical properties of the upper 5-100 m of the lunar regolith and crust. Past work has identified a number of anomalous regions and changes in echo strength, some attributed to differences in titanium content. There has been little opportunity, however, to compare calibrated long-wavelength backscatter among different units or to theoretical model results. We combine recent high-resolution (3-5 km) 70-cm radar data for the nearside with earlier calibrated full-disk observations to provide a reasonable estimate of the true lunar backscatter coefficient. These data are tested against models for quasi-specular scattering from the surface, echoes from a buried substrate, and Mie scattering from surface and buried rocks. We find that 70 cm echoes likely arise from Mie scattering by distributed rocks within the soil, consistent with earlier hypotheses. Returns from a buried substrate would provide a plausible fit to the observations only if the regolith depth were ~3 m or less and varied little across the maria. Depolarized echoes are due to some combination of single and multiple scattering events, but it appears that single scattering alone could account for the observed echo power, based on comparisons with terrestrial rocky surfaces. Backscatter strength from the regolith is most strongly affected by the loss tangent, whose variation with mineral content is still poorly defined. We compared the backscatter values for the mare deposits to the oxide contents inferred from spectral ratio methods, and found that in general the unit boundaries evident in radar images closely follow those seen in color difference images. The 70-cm data are not well correlated with TiO2 values found using the Charette relationship nor with Fe abundances derived from Clementine observations. The lack of a relationship between radar echo and Fe content is reasonable given the distribution of iron among various mineral phases, but ilmenite content (FeTiO3) has typically been cited as the dominant cause of changes in loss tangent (and thus the radar absorption). The lack of correlation between the radar data and TiO2 estimates may arise from uncertainties in the Charette technique, subtle differences in the upper surface and bulk properties of the regolith, mineralogic effects on the radar not linked to titanium content, or to some combination of these factors. Dark crater haloes in the mare and highlands, and low radar returns from apparent cryptomare regions, are used to illustrate the role radar data can play in identifying changes in regolith composition; low-return haloes around craters such as Petavius may indicate 5-25% contamination of the highlands soil by excavated mare material or a layer of rock-poor ejecta at least several meters deep. The 7.5-m data were shown to correlate to a reasonable degree with estimates of Fe abundance, suggesting that this component of the mare basalts is primarily responsible for attenuation losses at very long wavelengths. The different sensitivities of the two radar wavelengths and multispectral data offers the potential for future deep mapping of the mare lava flows and regolith.

Lunar MariaRadarLunar Studies↗
NASA NTRS1995

Long-wavelength Radar Studies of the Lunar Maria

Radar measurements at 70 cm and 7.5 m wavelengths provide insight into the structure and chemical properties of the upper 5-100 m of the lunar regolith and crust. Past work has identified a number of anomalous regions and changes in echo strength, some attributed to differences in titanium content. There has been little opportunity, however, to compare calibrated long-wavelength backscatter among different units or to theoretical model results. We combine recent high-resolution (3-5 km) 70-cm radar data for the nearside with earlier calibrated full-disk observations to provide a reasonable estimate of the true lunar backscatter coefficient. These data are tested against models for quasi-specular scattering from the surface, echoes from a buried substrate, and Mie scattering from surface and buried rocks. We find that 70 cm echoes likely arise from Mie scattering by distributed rocks within the soil, consistent with earlier hypotheses. Returns from a buried substrate would provide a plausible fit to the observations only if the regolith depth were approx.3 m or less and varied little across the maria. Depolarized echoes are due to some combination of single and multiple scattering events, but it appears that single scattering alone could account for the observed echo power, based on comparisons with terrestrial rocky surfaces. Backscatter strength from the regolith is most strongly affected by the loss tangent, whose variation with mineral content is still poorly defined. We compared the backscatter values for the mare deposits to the oxide contents inferred from spectral ratio methods, and found that in general the unit boundaries evident in radar images closely follow those seen in color difference images. The 70-cm data are not well correlated with TiO2 values found using the Charette relationship nor with Fe abundances derived from Clementine observations. The lack of a relationship between radar echo and Fe content is reasonable given the distribution of iron among various mineral phases, but ilmenite content (FeTiO3) has typically been cited as the dominant cause of changes in loss tangent (and thus the radar absorption). The lack of correlation between the radar data and TiO2 estimates may arise from uncertainties in the Charette technique, subtle differences in the upper surface and bulk properties of the regolith, mineralogic effects on the radar not linked to titanium content, or to some combination of these factors. Dark crater haloes in the mare and highlands, and low radar returns from apparent cryptomare regions, are used to illustrate the role radar data can play in identifying changes in regolith composition; low-return haloes around craters such as Petavius may indicate 5-25% contamination of the highlands soil by excavated mare material or a layer of rock-poor ejecta at least several meters deep. The 7.5-m data were shown to correlate to a reasonable degree with estimates of Fe abundance, suggesting that this component of the mare basalts is primarily responsible for attenuation losses at very long wavelengths. The different sensitivities of the two radar wavelengths and multispectral data offers the potential for future deep mapping of the mare lava flows and regolith.

Lunar Studies↗
DOE OSTI2023

Independent Evidence for the Preservation of Endogenous Bone Biochemistry in a Specimen of Tyrannosaurus rex

Biomolecules preserved in deep time have potential to shed light on major evolutionary questions, driving the search for new and more rigorous methods to detect them. Despite the increasing body of evidence from a wide variety of new, high resolution/high sensitivity analytical techniques, this research is commonly met with skepticism, as the long standing dogma persists that such preservation in very deep time (>1 Ma) is unlikely. The Late Cretaceous dinosaur Tyrannosaurus rex (MOR 1125) has been shown, through multiple biochemical studies, to preserve original bone chemistry. Here, we provide additional, independent support that deep time bimolecular preservation is possible. We use synchrotron X-ray fluorescence imaging (XRF) and X-ray absorption spectroscopy (XAS) to investigate a section from the femur of this dinosaur, and demonstrate preservation of elements (S, Ca, and Zn) associated with bone remodeling and redeposition. We then compare these data to the bone of an extant dinosaur (bird), as well as a second non-avian dinosaur, Tenontosaurus tilletti (OMNH 34784) that did not preserve any sign of original biochemistry. Our data indicate that MOR 1125 bone cortices have similar bone elemental distributions to that of an extant bird, which supports preservation of original endogenous chemistry in this specimen.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Design Evolutuion of Hot Isotatic Press Cans for NTP Cermet Fuel Fabrication

Nuclear Thermal Propulsion (NTP) is under consideration for potential use in deep space exploration missions due to desirable performance properties such as a high specific impulse (> 850 seconds). Tungsten (W)-60vol%UO2 cermet fuel elements are under development, with efforts emphasizing fabrication, performance testing and process optimization to meet NTP service life requirements [1]. Fuel elements incorporate design features that provide redundant protection from crack initiation, crack propagation potentially resulting in hot hydrogen (H2) reduction of UO2 kernels. Fuel erosion and fission product retention barriers include W coated UO2 fuel kernels, W clad internal flow channels and fuel element external W clad resulting in a fully encapsulated fuel element design as shown.

Mireles, O. R.↗
DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Additionally, four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

42 ENGINEERING↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
NASA NTRS2016

The Deep-Space Positioning System Concept: Automating Complex Navigation Operations Beyond the Earth

There is no equivalent of GPS in “deep space,” that is beyond the Earth Moon system (and use of GPS beyond LEO and MEO is already problematic.) NASA has been awarded a patent on the concept of the “Deep-space Positioning System” (DPS) which is a self-contained instrument concept that would use in-situ observations to provide an automated and autonomous means of determining its position in the Solar System, and at least as important, its position relative to a target, whether that target is a planet, asteroid, moon or comet. In addition, the DPS system computes trajectory change parameters, as necessary to keep a spacecraft on a planned trajectory. Implementation options include the use of a radio receiver to receive a frequency beacon from a non-in-situ source for additional navigation information over and above that which can be provided by the cameras that are the principal data source of DPS. This paper discusses the DPS instrument concept, and its potential use in deep space.

Guinn, Joseph R.↗
NASA NTRS2011

Large Aperture "Photon Bucket" Optical Receiver Performance in High Background Environments

The potential development of large aperture groundbased "photon bucket" optical receivers for deep space communications, with acceptable performance even when pointing close to the sun, is receiving considerable attention. Sunlight scattered by the atmosphere becomes significant at micron wavelengths when pointing to a few degrees from the sun, even with the narrowest bandwidth optical filters. In addition, high quality optical apertures in the 10-30 meter range are costly and difficult to build with accurate surfaces to ensure narrow fields-of-view (FOV). One approach currently under consideration is to polish the aluminum reflector panels of large 34-meter microwave antennas to high reflectance, and accept the relatively large FOV generated by state-of-the-art polished aluminum panels with rms surface accuracies on the order of a few microns, corresponding to several-hundred micro-radian FOV, hence generating centimeter-diameter focused spots at the Cassegrain focus of 34-meter antennas. Assuming pulse-position modulation (PPM) and Poisson-distributed photon-counting detection, a "polished panel" photon-bucket receiver with large FOV will collect hundreds of background photons per PPM slot, along with comparable signal photons due to its large aperture. It is demonstrated that communications performance in terms of PPM symbol-error probability in high-background high-signal environments depends more strongly on signal than on background photons, implying that large increases in background energy can be compensated by a disproportionally small increase in signal energy. This surprising result suggests that large optical apertures with relatively poor surface quality may nevertheless provide acceptable performance for deep-space optical communications, potentially enabling the construction of cost-effective hybrid RF/optical receivers in the future.

photon bucket detection↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2024

Understanding the effect of density functional choice and van der Waals treatment on predicting the binding configuration, loading, and stability of amine-grafted metal organic frameworks

Metal organic frameworks (MOFs) are crystalline, three-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M2(4,4′-dioxidobiphenyl-3,3′-dicarboxylate)—henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn—is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew–Burke–Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic potential’s (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.

Chemistry↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces

A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.

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

Speciation and diffusive dynamics in hydrated grain boundaries of complex oxide Gd2Ti2O7

Abstract Grain boundaries in polycrystalline materials significantly affect their properties, such as ionic transport, corrosion, and chemical durability. The pyrochlore compound (Gd 2 Ti 2 O 7 ) is employed as a model for complex oxides and is known for its diverse applications, including nuclear waste immobilization. Density functional theory-based first-principles molecular dynamics simulations were performed at different temperatures on the hydrated grain boundary system. The results show extensive transformations within the grain boundaries among hydrous water species (OH − , H 2 O, and H 3 O + ). The temperature dependence of self-diffusion coefficients follows Arrhenius behavior, with an activation energy of 35.9 kJ/mol for hydrogen and 46.3 kJ/mol for oxygen. The lifetime of OH − is about three to four times longer than that of H 2 O at temperatures from 800 to 2100 K, suggesting the greater stability of OH − over H 2 O, a unique characteristic of the grain boundaries. The estimated lifetime of the hydrous species decreases as the temperature increases, with an activation energy of 9.9 kJ/mol for OH − and 13.4 kJ/mol for H 2 O. While Gd 3 + is more mobile than Ti 4+ , both the Gd 3 + and Ti 4+ cations are orders of magnitude less mobile than the water species. The results suggest that water species are much more mobile within grain boundaries than in the bulk crystal and have the potential to penetrate deep into polycrystalline materials through grain boundaries, leading to grain boundary degradation and dissolution. The different mobilities of cations in complex oxides can lead to leaching of certain cations and incongruent dissolution during the chemical weathering of Earth and industrial materials.

B. Ghosh, Dipta↗