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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Molecular Imaging of Microbially Induced Corrosion of Synthetic Archeological Glasses by a Rhizosphere Bacterium

Microbially induced corrosion (MIC) focuses on the degradation of solid materials, such as glass or metal. Soil microbes are often associated with the corrosion of foreign objects in the rhizosphere. Paenibacillus polymyxa SCE2, a facultative anaerobic bacterium in soil, is of the same genus as bacteria found near nuclear waste disposal sites. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used for imaging surface changes induced by P. polymyxa SCE2 cultured on two synthetic glass coupons to represent natural analogs of materials that were studied in relation to the vitrification of nuclear waste. Multimodal imaging was used to verify bacterial coverage across the glass surface after long-term growth. ToF-SIMS spectral analysis showed detection of glass component ions, such as silicon oxide (m/z – 59.96 SiO 2 – ) and aluminum oxide (m/z – 101.95 Al 2 O 3 – ), and biofilm’s extracellular polymeric substance (EPS) components, such as pentadecanoic acid (m/z – 241.22 C 15 H 29 O 2 – ) and sterol lipids (m/z – 311.16 C 20 H 23 O 3 – ). ToF-SIMS spectral, imaging, and depth profiling analyses showed that the glass rich in silica and other light elements (“granite glass”) had more “corrosion related” peaks than the glass that was less silica-rich and contained more iron (“dike glass”). Furthermore, these surface and interface compositional and spatial differences observed in the mass spectra and imaging were attributed to bacterial metabolism and an electron transfer mechanism influenced by morphological and compositional differences between the two types of glasses. ToF-SIMS is effective in studying microbial effects, bringing new molecular insights into MIC in a broader context of materials degradation.

Amorphous materials↗
DOE OSTI2026

Local Structural Coherence and Interfacial Charge Transfer in Cu 2 ⁢S/Mo⁢S 2 Heterostructure

Precise control over electronic coupling at nanoscale interfaces is critical for designing materials with tunable charge-transfer behavior and catalytic function. Heterostructures with locally coherent interfaces provide a platform for interrogating interfacial charge redistribution in coupled material systems. Here, we report Cu 2 ⁢S/Mo⁢S 2 heterostructures exhibiting nanoscale crystallographic alignment, which are synthesized through a rapid thermal transformation pathway. We employed electrochemical reduction reactions to probe interfacial charge transfer, revealing shifts in product distribution attributable to modified interfacial energetics, even in the absence of optimized catalytic performance. The observed formate Faradaic efficiency suggests that interfacial electronic modulation in the heterostructure shifts product selectivity towards formate, highlighting how interface-driven electronic modulation can direct reaction pathways and influence product selectivity. Optimizing catalyst loading, architecture, and reactor configuration will be critical for future improvement. Structural and compositional integrity were confirmed through powder x-ray diffraction, x-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. Electron transfer between Cu 2 ⁢S and Mo⁢S 2 domains was further evaluated by electrochemical impedance spectroscopy, while selected-area electron diffraction revealed local crystallographic alignment consistent with a local epitaxial relationship at the Cu 2 S/Mo⁢S 2 heterointerface. To illustrate the broader applicability of this approach, a Zn⁢S/Mo⁢S 2 heterostructure was also synthesized using the same microwave strategy, confirming the generalizability of interfacial engineering principles across metal sulfide-Mo⁢S 2 systems. Collectively, these findings demonstrate that controlled local epitaxial alignment serves as an effective design principle for tuning interfacial energetics and catalytic reactivity in complex heterostructure materials.

carbon capture & utilization↗
DOE OSTI2020

Workshop on Improving Holdup Monitoring in the US

The characterization and quantification of nuclear deposits, or residual nuclear material retained in process equipment, generally referred to as holdup, continues to challenge nuclear processing facilities in DOE program areas such as nuclear criticality safety, material control and accountability, environmental management. The efficiency and effectiveness of nuclear operations in these facilities depends heavily on the results from nondestructive techniques designed to measure nuclear materials in situ. The traditional methods used to measure holdup were established more than 30 years ago and assume unrealistic conditions for most measurement cases. These blind, in situ measurements present unique challenges due to the following attributes: unique, non-ideal geometries; unknown deposit thicknesses; lack of representative calibration standards; large number of holdup deposit locations; and poor accessibility for many measurement locations. These difficulties and the poor assumptions made to handle them have resulted in measurement uncertainties that are often too large for many programs to effectively utilize. In an effort to address and identify solutions to the current holdup challenges facing the US nuclear industry, Oak Ridge National Laboratory hosted the Technical Workshop on Improving Holdup Monitoring in the US. The two-day workshop held August 21–22, 2019, was attended by 35 holdup measurement practitioners, program managers, stakeholders from US Department of Energy sites, the Nuclear Regulatory Commission, and representatives from commercial industry. The primary objectives were to identify critical elements of a successful and defensible holdup measurement program; identify technical challenges, needs, and potential improvements associated with measurement of holdup; and share best practices and lessons learned from recent accumulation events. Fourteen presentations were delivered on holdup topics including necessary elements for effective program management, measurement needs, emerging technologies and other measurement solutions, and lessons learned from recent events. These presentations highlighted best practices related to accumulation monitoring, considerations and proactive steps to facilitate holdup in new facility designs, imaging solutions to improve holdup assumptions, and new software developed in support of holdup measurements. Breakout sessions were also employed to determine additional needs and identify potential solutions to common holdup challenges. The primary discussions focused on causes and prevention of material accumulations, needs for uranium and plutonium measurements, and reduction of measurement uncertainty, all of which were explicitly identified as critical needs during the 2018 Workshop on Technical and Programmatic Needs for a Sustainable NDA Program for the US DOE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Defects of perovskite semiconductor CsPbBr3 investigated via photoluminescence and thermally stimulated current spectroscopies

Halide perovskites are essential materials for hard radiation detectors at ambient temperature. To improve detector performance, charge transport must be investigated and optimized. Using photoluminescence (PL) and thermally stimulated current (TSC) spectroscopies, we investigate photogenerated charge carriers in Bridgman-grown CsPbBr3 single crystals to understand the nature of charge transport. PL spectroscopy of these halide perovskites revealed the presence of strong emission bands at the band edge, which were attributed to free or bound excitons. It is shown that a wide broadening of the excitonic linewidth in these halide perovskites arises from strong exciton–phonon coupling, which is substantially dominated by longitudinal optical phonons via Fröhlich interaction. An additional contribution due to the presence of ionized impurities was also observed. Crystals with a detectable sensitivity to high-energy gamma radiation are characterized by a higher intensity and a narrower linewidth of the principal PL peak at 2.326 eV. Defect states beyond 2.214 eV have a negative impact on detector sensitivity to high-energy gamma radiation. TSC spectroscopy reveals an array of trap levels spanning 0.15–0.70 eV, attributed to intrinsic point defects and multiple extrinsic defects involving dopants or impurities. Defects identified included Cs and Br vacancies, as well as Pb interstitials with concentrations in the 1011–1016 cm−3 range. Understanding how the synthesis process impacts the types and concentrations of the defects present is currently under investigation. Elimination or suppression of the defect/trap states should result in halide perovskite materials with longer carrier diffusion lengths and improved detector characteristics.

Physics↗
DOE OSTI2020

Nickel-rich Nickel Manganese Cobalt (NMC622) Cathode Lithiation Mechanism and Extended Cycling Effects Using Operando X-ray Absorption Spectroscopy

Ni-rich NMC materials are a particularly promising class of Li-ion cathodes for various applications. LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) offers a unique balance of thermal stability and energy density, thus attracting attention for electric vehicle implementation. However, upon extended cycling, capacity fade is prevalent due to structural degradation, which is a major drawback for layered oxide cathodes. Therefore, exploring the underlying phenomena that drive detrimental structural response can lead to future improvements. For the first time, operando X-ray absorption spectroscopy (XAS) was performed on NMC622 pouch cells at three different stages. An extensive description of the first cycle (de)lithiation mechanisms was achieved through X-ray absorption near-edge structure analyses and extended X-ray absorption fine structure modeling. Transition metal specific electrochemical participation and structural variation revealed that much of the delivered capacity and distortion is a result of Ni redox behavior, while the local structure of Co and Mn are impacted due to their interdependencies. Key mechanistic components were identified, as the local structural variation from redox processes and Ni 3+ Jahn–Teller distortion were decoupled. Further, operando XAS was used to investigate the structural response of NMC622 to extended cycling and was supported by X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy analyses. In this work, reduced capacity was found in the cell after 100 cycles and is attributed to structural degradation and cathode–electrolyte interphase buildup from repeated Li (de)insertion processes, which limit the electrochemical reversibility of Ni and Co through increased polarization. These results expand the understanding of a Ni-rich NMC material under extended cycling, which is vital to the future design of electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

While the fabrication of graded materials by directed energy deposition (DED) has led to accelerated materials discovery, the ability to rapidly explore sufficiently large material composition spaces is limited due to the time-intensive nature of conventional materials characterization techniques. The present study investigates the viability of small punch test (SPT) protocols for rapidly evaluating DED-fabricated alloy mixtures of stainless steel 316L (316L) and Inconel 625 (IN625). The SPT protocols evaluated in this study include both the recently established two-step Bayesian estimation framework as well as the empirical relationships established in prior literature. It is shown that these protocols are capable of reliably and quantitatively tracking the changes in the mechanical properties of the alloy mixtures studied. Enhancement of mechanical properties was observed with the addition of IN625 to 316L, which is attributed to the austenite stabilization in the matrix and the formation of fine δ - Ni3Nb precipitates. It is shown that CALPHAD-based Scheil model simulations predicted the formation of different precipitate phases for each composition. The novel protocols presented in this paper open new avenues for high throughput material explorations for additive manufacturing.

2-step Bayesian estimation↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Schema Elements for Granta Annual Report: FY2024

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling the evolution of slip localization: Realization of link to material strength

Slip localization formation is the chief mechanism underlying the deformation of almost all metals, from pure elements to high-performance superalloys. The intensity of individual slip localizations is often related to the ultimate strain level for failure but not to the strength of the metal. Here we show that across 15 distinct metals, the intensity of slip in individual slip localizations and slip localization spacings are strongly related to material yield strength. Using a three-dimensional crystal plasticity-based micromechanical model that explicitly simulates the growth of discrete slip localizations, we reveal that the stronger the metal, the faster and earlier slip localizations intensify. The relationship is attributed to the formation of a zone that surrounds the slip localization where the driving force for slip is absent. We find that the zone size is controlled by the strength of the neighboring crystal. Consequently, as strength increases, slip becomes increasingly preferred within the slip localization itself and formation of other slip localizations becomes more likely further away.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries

Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb 2 O 5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li + . This electrode can reversibly cycle three lithiums per Nb 2 O 5 , corresponding to a capacity of 269 mAh g –1 at 20 mA g –1 , and retains a capacity of 191 mAh g –1 at a high rate of 1 A g –1 . Further, it exhibits superb cycling stability with a capacity of 225 mAh g –1 at 200 mA g –1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Hierarchical Polyelemental Nanoparticles as Bifunctional Catalysts for Oxygen Evolution and Reduction Reactions

Efficient electrocatalysts are critical in various clean energy conversion and storage systems. Polyelemental nanomaterials are attractive as multi-functional catalysts due to their wide compositions and synergistic properties. However, controlled synthesis of polyelemental nanomaterials is difficult due to their complex composition. Herein, we present a one-step synthetic strategy to fabricate a hierarchical polyelemental nanomaterial, which contains ultrasmall precious metal nanoparticles (IrPt, ~5 nm) anchored on spinel-structure transition metal oxide nanoparticles. The polyelemental nanoparticles serve as excellent bifunctional catalysts for the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The mass catalytic activity of the polyelemental nanoparticles is 7-times higher than that of Pt in ORR and 28-times that of Ir in OER at the same overpotentials, demonstrating the high activity of the bifunctional electrocatalyst. We attribute this outstanding performance to the controlled multiple elemental composition, mixed chemical states, and large electroactive surface area. The hierarchical nanostructure and polyelemental design of these nanoparticles offers a general and powerful alternative material for catalysis, solar cells, and more.

25 ENERGY STORAGE↗
DOE OSTI2023

The mechanism of twin thickening and the elastic strain state of TWIP steel nanotwins

A Twinning Induced Plasticity (TWIP) steel with a nominal composition of Fe-16.4Mn-0.9C-0.5Si-0.05Nb-0.05V was deformed to an engineering strain of 6%. The strain around the deformation twins were mapped using the 4D-STEM technique. Strain mapping showed a large average elastic strain of approximately 6% in the directions parallel and perpendicular to the twinning direction. However, the large average strain comprised of several hot spots of even larger strains of up to 12%. These hot spots could be attributed to a high density of sessile Frank dislocations on the twin boundary and correspond to shear stresses of 1–1.5 GPa. The strain and therefore stress fields are significantly larger than other materials known to twin and are speculated to be responsible for the early thickness saturation of TWIP steel nanotwins. The ability to keep twins extremely thin helps improve grain fragmentation, i.e. the dynamic Hall–Petch effect, and underpins the large elongations and strain hardening rates in TWIP steels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-coercivity ThMn 12 -type monocrystalline Sm–Zr–Fe–Co–Ti particles by high-temperature reduction diffusion

The ThMn 12 -type (Sm,Zr)1(Fe,Co,Ti) 12 compounds have the potential of powerful permanent magnets. Magnetically hard and anisotropic powders of such compound have been prepared by subjecting elemental oxides and Co mixed with Ca and CaO dispersant to a succession of high-energy ball-milling, reduction diffusion at 990–1220 °C and repeated washing. The size of the resulting ThMn 12 -type crystallites, their coercivity and fraction of monocrystalline particles were all found to increase with the reduction-diffusion temperature. Particles synthesized at 1220 °C were highly monocrystalline with a mean size of 0.54 μm and, after a magnetic-field alignment, exhibited a coercivity of 1.26 T and a full-density-projected maximum energy product of at least 209 kJ/m 3 (26.3 MGOe). Here, the strong positive effect of the reduction-diffusion temperature on the coercivity has been attributed to separation of the crystallites and to decrease in the incidence of structural defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tuning Photoexcited Charge Transfer in Imine-Linked Two-Dimensional Covalent Organic Frameworks

The generation of a long-lived charge-separated state in versatile π-conjugated two-dimensional covalent organic frameworks (2D COFs), a process essential to extending their great potentials in advanced semiconducting applications, is yet fully elucidated. Herein, we report a systematic investigation of the photophysical properties of three highly crystalline imine-linked 2D COFs using steady-state and transient absorption spectroscopy accompanied by time-dependent density functional theory (TDDFT) calculations. The different electron affinity between 5,5',5''-(1,3,5-benzenetriyl)-tris(2-pyridinecarboxaldehyde) (BTPA) and three tunable electron-donating/accepting triamine monomers dominated the formation of the excited state, charge transfer direction and lifetime. A prominent charge transfer from electron-rich 4,4',4''-triaminotriphenylamine (TAPA) to BTPA in COFTAPA-BTPA led to the long-lived charge-separated state, which was attributed to a greater degree of delocalization compared to the two other COFs. Furthermore, these results provide fundamental insight into the importance of structure-property correlation for designing advanced photoactive 2D COF materials with the efficient charge transfer and long-lived charge-separated state.

14 SOLAR ENERGY↗
DOE OSTI2022

Unconventional non-local relaxation dynamics in a twisted trilayer graphene moiré superlattice

Abstract The electronic and structural properties of atomically thin materials can be controllably tuned by assembling them with an interlayer twist. During this process, constituent layers spontaneously rearrange themselves in search of a lowest energy configuration. Such relaxation phenomena can lead to unexpected and novel material properties. Here, we study twisted double trilayer graphene (TDTG) using nano-optical and tunneling spectroscopy tools. We reveal a surprising optical and electronic contrast, as well as a stacking energy imbalance emerging between the moiré domains. We attribute this contrast to an unconventional form of lattice relaxation in which an entire graphene layer spontaneously shifts position during assembly, resulting in domains of ABABAB and BCBACA stacking. We analyze the energetics of this transition and demonstrate that it is the result of a non-local relaxation process, in which an energy gain in one domain of the moiré lattice is paid for by a relaxation that occurs in the other.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Positive spin Hall magnetoresistance in single-crystalline Pt/CoO(001) bilayers

Here, the spin Hall magnetoresistance (SMR) effect in single-crystalline Pt/CoO(001) bilayers has been systematically investigated. X-ray magnetic linear dichroism measurements prove that CoO antiferromagnetic (AFM) spins can be switched into the direction orthogonal to the applied field. We find the SMR signal is comprised of two components related to either the switching of CoO AFM Néel order or the applied strong field effect. Both SMR components show a “positive” angular dependence with $R_{∥} > R_{⊥}$, while $R_{∥} (R_{⊥})$ is defined as the resistance with the applied in-plane field parallel (perpendicular) to the current. The observed positive SMR is mainly attributed to the uncompensated spins at the Pt/CoO interface, instead of the CoO AFM spins. Our study may attract a great deal of interest to understand the complicated SMR effect in AFM spintronics materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Enhanced thermoelectric performance of PbSe-graphene nanocomposite manufactured with acoustic cavitation induced defects

An order of magnitude rise in the thermoelectric (TE) performance of the PbSe, a scalable and easy-to-manufacture TE material, has been achieved by incorporating reduced graphene oxide (Gr) nanoplatelets in a PbSe/PbSeO 3 heterostructure formed by acoustic cavitation-assisted oxidation. The fabricated Gr/PbSe/PbSeO 3 nanocomposites exhibit high TE performance with an exceptionally high Seebeck coefficient coupled with low thermal conductivity. The variation in the Seebeck coefficient has been attributed to a reduction in charge carrier mobility due to the ferroelectric polarization effect. Furthermore, the increase in electrical resistivity is minimized by adding graphene. At an optimal weight fraction (0.2 wt%), graphene nano-inclusions lead to superior Seebeck coefficient values as high as ~2000 μV/K at ~500 K, providing high overall TE performance. This study shows substantial changes in the TE properties of PbSe through the incorporation of graphene and PbSeO 3 . The understanding and methodology developed in this study can be exploited for the scalable manufacturing of high-performance TE materials.

Structure-property correlations↗
DOE OSTI2026

Graphene oxide precursor effects on 3D-printed carbon scaffolds

Manganese oxide (MnO 2 ), an earth-abundant material, is a promising component for energy storage devices, with uses in both pseudocapacitors and batteries. However, high MnO 2 loading often leads to reduced performance due to poor ion diffusion. 3D printing, particularly using the direct ink writing (DIW) technique, offers a solution by enabling the fabrication of electrodes with hierarchical porous structures and open channels that enhance mass transport and ion diffusion. Previous work demonstrated that 3D-printed graphene aerogels with MnO 2 coatings exhibited excellent electrochemical performance, even with thick electrodes, due to their optimized structure. Building on this work, the current study investigates the performance differences between aerogels developed using graphene oxide (GO) and reduced graphene oxide (rGO) as carbon precursors. Both materials were incorporated into thixotropic inks, 3D-printed into lattice structures, and carbonized. Despite expected similarities between the final graphene aerogel, rGO-based aerogels exhibited superior areal capacitance, compared to GO-based aerogels. These differences are attributed to the lower oxygen content and defect density of rGO, which influence its interaction with cellulose viscosifiers in the ink formulation. Brunauer–Emmett–Teller (BET) surface area analysis revealed that rGO aerogels exhibit a larger surface area and mesoporous structure, further enhancing their performance. When coated with MnO 2 , rGO-based aerogels maintained their superior capacitive behavior over GO-based aerogels. This study highlights the effect of carbon precursor on the end performance of graphene aerogels.

Materials science↗