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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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-Input files for randomly selected subset data (30 instances) from primary dipole-dipole forward modeling data.-Inversion results in surfer grid format as well as in .DAT format.-Scatterplots for MLP-NN vs Gauss-Newton present in the excel sheet.
Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.
Data-driven security assessment provides key indicators on power system stability using simulations on scheduling models, as opposed to dynamic simulations that are more time-consuming. This paper investigates data-driven security assessment of power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm due to its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool, including transient stability, frequency stability and small signal stability. The estimation values from machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the three stability metrics.
Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.
Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.
A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.
Radical-carrying organic molecules have received significant attention to bypass the issue related to harvesting triplet excitons in current light-emitting materials. While the computational efforts conducted so far have treated these radical emitters as isolated entities, in actual devices, they are embedded in a host matrix and subject to emitter–host interactions. In this study, by combining molecular dynamics simulations and density functional theory calculations, the impact of the host matrix on the optoelectronic performance of radical emitters is evaluated, taking as a representative example the (4-ncarbazolyl-2,6-dichlorophenyl)bis(2,4,6-trichlorophenyl)-methyl (TTM-3NCz) radical emitter dispersed in a 4,4-bis(carbazol-9-yl)biphenyl (CBP) host. A morphological analysis shows that steric effects around the radical centers, carried by the TTM electron-poor moieties of the emitters, disfavor π–π interactions with the host molecules, which leads to random intermolecular orientations around the TTM moieties. The 3NCz electron-rich moieties of the emitters, however, have much lesser spatial hindrance for intermolecular π–π stacking, which modulates the structural and electronic properties of the emitters in the host matrix. The influence of dynamic and static disorders on the radiative and nonradiative recombination processes is also investigated and it is found that the rates of nonradiative recombination are small, which opens the way to 100% internal quantum efficiency for the doublet-based emission process.
Abstract Understanding how various redox activities evolve and distribute in disordered rocksalt oxides (DRX) can advance insights into manipulating materials properties for achieving stable, high‐energy batteries. Herein, the authors present how the reaction kinetics and spatial distribution of redox activities are governed by the particle size of DRX materials. The size‐dependent electrochemical performance is attributed to the distinct cationic and anionic reaction kinetics at different sizes, which can be tailored to achieve optimal capacity and stability. Overall, the local charged domains in DRX particles display random heterogeneity caused by the isotropic delithiation pathways. Owing to the kinetic limitation, the micron‐sized particles exhibit a holistic “core‐shell” charge distribution, whereas sub‐micron particles show more uniform redox reactions throughout the particles and ensembles. Sub‐micron DRX particles exhibit increasing anionic redox activities yet inferior cycling stability. In summary, engineering particle size can effectively modulate how cationic and anionic redox activities evolve and distribute in DRX materials.
Abstract Here, a comprehensive study on the synthesis, characterization, and reactivity of grain‐boundary (GB)‐rich noble metal nanoparticle (NP) assemblies is presented. A facile and scalable synthesis of Pt, Pd, Au, Ag, and Rh NP assemblies is developed, in which NPs are predominantly connected via Σ3 (111) twin GBs, forming a network. Driven by water electrolysis, the random collisions and oriented attachment of colloidal NPs in solution lead to the formation of Σ3 (111) twin boundaries and some highly mismatched GBs. This synthetic method also provides convenient control over the GB density without altering the crystallite size or GB type by varying the NP collision frequency. The structural characterization reveals the presence of localized tensile strain at the GB sites. The ultrahigh activity of GB‐rich Pt NP assembly toward catalytic hydrogen oxidation in air is demonstrated, enabling room‐temperature catalytic hydrogen sensing for the first time. Finally, density functional theory calculations reveal that the strained Σ3(111) twin boundary facilitates oxygen dissociation, drastically enhancing the hydrogen oxidation rate via the dissociative pathway. This reported large‐scale synthesis of the Σ3 (111) twin GB‐rich structures enables the development of a broad range of high‐performance GB‐rich catalysts.
Current–induced spin–orbit torques (SOTs) have emerged as a powerful tool to control magnetic elements and non–uniform magnetic textures such as domain walls and skyrmions. SOT–induced switching of perpendicular magnetization generally requires an external field to break the rotational symmetry of the spin–orbit effective fields responsible for the deterministic reversal. The proposed mechanisms to eliminate this requirement often rely on complex multilayer structures that necessitate laborious optimization in the material and spin transport properties, making them less attractive for applications. Herein, current–induced, external field–free switching of an epitaxial MgO/Pt/Co trilayer with an extremely large perpendicular anisotropy in excess of 3 Tesla is reported. It is found that switching occurs due to the interplay of strong SOTs, local anisotropy fluctuations, and the Dzyaloshinkii–Moriya interaction inherent to this epitaxial system. Finally, given that these layers constitute the base stack of a magnetic tunnel junction, this switching mechanism offers the most technologically viable path toward devices such as field–free SOT–based magnetic random–access memories.
Abstract The work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO 3 and the (001)‐F surface of Ag 2 F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 10 5 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.
Prey wrapping spider silk's unique mechanical properties are investigated confirming the silk's high degree of extensibility and superior toughness compared to other types of spider silk. For the first time, the pre-spinning dope phase is studied in isotope-enriched intact aciniform (AC) silk glands using solution NMR that reveals a combination of α-helical domains linked by disordered random coil chains consistent with previously proposed “beads-on-a-string” models. The model is further refined through the AlphaFold2 protein structure prediction tool. Finally, extensive magic angle spinning (MAS) solid-state (SS) NMR data for isotopically-enriched fibers is used to refine the structural model for AC silk from two species, A. aurantia and A. argentata. The SSNMR data shows that the AC silk fibers are highly α-helical, coiled-coil in structure but, also exhibit significant β-sheet components that can be traced back to the Gly-rich disordered linker regions in the pre-spinning dope phase that are converted to β-sheet structures during fiber formation. This combination of mechanical and structural characterization enhances the understanding of AC silk's liquid-to-solid transition and structure-mechanics relationship. In conclusion, these prey wrap silk results and models will provide the basis for the design of biomimetic materials inspired by the AC spider silk system.
It is crucial to minimize cobalt content in Ni-rich layered single-crystal cathodes due to their high price and limited availability, yet it will inevitably lead to cation disordering, capacity degradation, and thermal issues. Herein, to overcome the intrinsic trade-off between performance and composition of Ni-rich Co-less single-crystal cathodes, a precursor engineering strategy with an epitaxially grown cobalt enrichment on the surface is innovatively proposed. In contrast to traditional coating modifications with random orientation and rigid surface-bulk boundary, the epitaxially enriched surface cobalt layer on the precursor undergoes rapid interdiffusion with the internal Ni 3+ during the optimized sintering process. This interdiffusion eliminates the surface-bulk boundary, promoting the uniform distribution of cobalt and synergistically addressing the Li/Ni intermixing. Moreover, an enhanced surface Li + diffusion is obtained, thereby suppressing the Li + concentration gradient and intragranular cracks generation. Consequently, the modified LiNi 0.7 Co 0.07 Mn 0.23 O 2 exhibits impressive cycling stability with increased capacity retention in both coin-type half-cells and pouch-type full-cells (91% after 1000 cycles), even under the harsh condition of high-temperature, surpassing the majority of previously reported Ni-rich cathodes. Finally, this work opens new avenues toward the low cost, high energy density, thermal stability, and long cyclic life for Ni-rich Co-less cathodes and sheds light on large-scale commercial production.
Flow cell electrodes are typically composed of porous carbon materials, such as papers, felts, and cloths. However, their random architecture hinders the fundamental characterization of electrode structure-performance relationships during in situ operation of porous electrochemical flow systems. This work describes a “print-and-plate” method that combines direct ink writing of micro-periodic lattices with a two-step metal plating process that converts them into highly conductive (sheet resistance 40 mΩ sq -1 ) electrodes. Their operando performance is assessed in an anthraquinone disulfonic acid half-cell using widefield electrochemical fluorescence microscopy, where output current and fluorescence intensity are in excellent agreement. The pressure drop associated with flow through three electrode designs is determined via simulations from which the most efficient design is identified and manufactured via print-and-plate. Confocal fluorescence microscopy is then used to create a 3D map of the state of charge (SOC) inside this print-and-plate electrode. The experimental state of the charge map is in good agreement with computational predictions. The rapid design, simulation, and fabrication of print-and-plate electrodes enable fundamental investigations of how architected porosity affects electrochemical performance under flow.
Magnetic high entropy alloys (HEAs) consisting of 3 d transition metals offer an exciting platform to explore novel magnetic phases as they often house competing exchange interactions in combination with random site disorders. In this work, a sensitive and tunable magnetic order is demonstrated in sputtered single-layer FeCoNiMnAl x films, as a function of non-magnetic Al addition, along with an unexpected exchange bias effect. Thin films of 50 nm FeCoNiMn exhibit a face-centered-cubic ( fcc ) phase, reentrant spin glass (SG) transition near 100 K, and a large exchange bias of over 500 Oe after field-cooling to 5 K. The exchange bias is increased to 930 Oe through a small addition of 5 at.% Al. Further Al addition to 12 at.% results in a body-centered-cubic ( bcc ) phase, coinciding with a large increase in the saturation magnetization, decrease of exchange bias to 50 Oe, and suppression of SG behavior. The change in magnetic order across the Al-induced structural transformation is mediated by the switching of Mn ground state from AF to FM, which is supported by first-principles calculations and experimentally confirmed via X-ray magnetic circular dichroism. These results open up new HEA strategies for explorations of novel magnetic phases.
Abstract Quasi‐2D Ruddlesden–Popper halide perovskites with a large exciton binding energy, self‐assembled quantum wells, and high quantum yield draw attention for optoelectronic device applications. Thin films of these quasi‐2D perovskites consist of a mixture of domains having different dimensionality, allowing energy funneling from lower‐dimensional nanosheets (high‐bandgap domains) to 3D nanocrystals (low‐bandgap domains). High‐quality quasi‐2D perovskite (PEA) 2 (FA) 3 Pb 4 Br 13 films are fabricated by solution engineering. Grazing‐incidence wide‐angle X‐ray scattering measurements are conducted to study the crystal orientation, and transient absorption spectroscopy measurements are conducted to study the charge‐carrier dynamics. These data show that highly oriented 2D crystal films have a faster energy transfer from the high‐bandgap domains to the low‐bandgap domains (<0.5 ps) compared to the randomly oriented films. High‐performance light‐emitting diodes can be realized with these highly oriented 2D films. Finally, amplified spontaneous emission with a low threshold 4.16 µJ cm −2 is achieved and distributed feedback lasers are also demonstrated. These results show that it is important to control the morphology of the quasi‐2D films to achieve efficient energy transfer, which is a critical requirement for light‐emitting devices.
2D nanosheets have been widely explored as electrode materials owing to their extraordinarily high electrochemical activity and fast solid-state diffusion. However, the scalable electrode fabrication based on this type of material usually suffers from severe performance losses due to restricted ion-transport kinetics in a large thickness. Here, a novel strategy based on evaporation-induced assembly to enable directional ion transport via forming vertically aligned nanosheets is reported. The orientational ordering is achieved by a rapid evaporation of mixed solvents during the electrode fabrication process. Compared with conventional drop-cast electrodes, which exhibit a random arrangement of the nanosheets and obvious decrease of rate performance with increasing thickness, the electrode based on the vertically aligned nanosheets is able to retain the original high rate capability even at high mass loadings and electrode thickness. Furthermore, combined electrochemical and structural characterization reveals the electrode composed of orientation-controlled nanosheets to possess lower charge-transfer resistances, leading to more complete phase transformation in the active material.
Topological solitary fields, such as magnetic and polar skyrmions, are envisioned to revolutionize microelectronics. These configurations have been stabilized in solid-state materials with a global inversion symmetry breaking, which translates in magnetic materials into a vector spin exchange known as the Dzyaloshinskii-Moriya interaction (DMI), as well as spin chirality selection and isotropic solitons. This work reports experimental evidence of 3D chiral spin textures, such as helical spins and skyrmions with different chirality and topological charge, stabilized in amorphous Fe-Ge thick films. Overall, these results demonstrate that structurally and chemically disordered materials with a random DMI can resemble inversion symmetry broken systems with similar magnetic properties, moments, and states. Disordered systems are distinguished from systems with global inversion symmetry breaking by their degenerate spin chirality that allows for forming isotropic and anisotropic topological spin textures at remanence, while offering greater flexibility in materials synthesis, voltage, and strain manipulation.