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At least 1,297 records · Page 72

Hybrid Nanostructured Ni(OH) 2 /NiO for High Capacity Lithium–ion Battery Anodes

A straightforward hydrothermal process followed by a controlled calcination technique is introduced for the synthesis of a Ni(OH) 2 modified NiO nanohybrid structure. Conversion materials as Li-ion battery anodes, NiO in this case, suffer from capacity fade and structural/morphological instability during lithiation and delithiation. The novelty of this work is in utilizing this hybrid configuration to increase the specific capacity and enable reversible electrochemistry. Here, we study the lithiation/delithiation process of NiO using a suite of spectroscopy and microscopy techniques from the atomic to electrode scale. We propose a mechanism for a reversible redox couple behavior of the NiO electrode by means of a hybrid Ni(OH)2/NiO structure. The ultimate objective of this work is to guide the development of anode with rationally-designed heterogeneity to create high capacity Li-ion batteries with excellent cycling and rate performance.

25 ENERGY STORAGE↗

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗

Direct Numerical Simulation of Partial Fuel Stratification Assisted Lean Premixed Combustion for Assessment of Hybrid G-Equation/Well-Stirred Reactor Model

Partial fuel stratification (PFS) is a promising fuel injection strategy to stabilize lean premixed combustion in spark-ignition (SI) engines. PFS creates a locally stratified mixture by injecting a fraction of the fuel, just before spark timing, into the engine cylinder containing homogeneous lean fuel/air mixture. Further, this locally stratified mixture, when ignited, results in complex flame structure and propagation modes similar to partially premixed flames and allows for faster and more stable flame propagation than a homogeneous lean mixture. This study focuses on understanding the detailed flame structures associated with PFS-assisted lean premixed combustion. First, a two-dimensional direct numerical simulation (DNS) is performed using detailed fuel chemistry, experimental pressure trace, and realistic initial conditions mapped from a prior engine large-eddy simulation (LES), replicating practical lean SI operating conditions. DNS results suggest that the conventional triple flame structure is prevalent during the initial stage of flame kernel growth. Both premixed and nonpremixed combustion modes are present with the premixed mode contributing dominantly to the total heat release. Detailed analysis further reveals the effects of flame stretch and fuel pyrolysis on flame displacement speed. Based on the DNS findings, the accuracy of a hybrid G-equation/well-stirred reactor (WSR) combustion model is assessed for the PFS-assisted lean operation in the LES context. It is found that the G-equation model qualitatively captures the premixed branches of the triple flame, while the WSR model predicts the nonpremixed branch of the triple flame. Finally, potential needs for improvements to the hybrid G-equation/WSR modeling approach are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

Identifying Bounds of Inorganic Content in Solventless Processing of Hybrid Solid Electrolytes

Solid-state lithium batteries require safe, robust electrolytes to enable higher energy densities and improved safety over conventional cells. Hybrid polymer–ceramic electrolytes are a promising solution, combining the processability of polymers with the high ionic conductivity and mechanical strength of inorganic fillers. In this work, we demonstrate a solventless, UV-curing method to produce hybrid solid electrolytes using a poly(ethylene glycol) dimethyl ether (PEGDME)-based photocurable matrix incorporating Li 1.5 Al 0.5 Ge 1.5 (PO 4 ) 3 (LAGP) or Li 7 La 3 Zr 2 O 12 (LLZO) ceramic electrolyte. Inorganic filler loadings up to ∼55 wt.% could be successfully incorporated via this process which was the highest inorganic content at which the slurry remains processable and cured into a uniform film. The resulting UV-cured composite electrolytes remain flexible and exhibit room-temperature ionic conductivities on the order of 10 −4 S·cm −1 , along with notably improved lithium-ion transference numbers compared to conventional polymer electrolytes. Similar performance and processing limits were observed for both LAGP and LLZO, indicating that ceramic filler chemistry does not significantly affect the UV-curing process or the electrolyte's ion transport properties in this regime. Eliminating solvents from fabrication not only simplifies processing and mitigates environmental concerns but also enables higher solid contents that enhance mechanical strength and help suppress lithium dendrite formation. In conclusion, this scalable approach thus paves the way for manufacturing robust composite solid electrolytes for next-generation solid-state batteries (SSBs).

Batteries↗

Hybrid molecular beam epitaxy growth of BaTiO 3 films

The ability to reproducibly synthesize thin films with precise composition and controlled structure is essential for fundamental study and mass production. Here, we demonstrate the hybrid molecular beam epitaxy (MBE) growth of epitaxial, single crystalline BaTiO3 films with different thicknesses on Nb-doped SrTiO3 substrates with atomically smooth surfaces. By combining scanning transmission electron microscopy, temperature-dependent high-resolution x-ray diffraction, reflection high-energy electron diffraction, and atomic force microscopy, we study the effect of growth conditions and the interplay between stoichiometry and epitaxial strain on the resulting structure. Furthermore, we demonstrate a close to bulk-like ferroelectric phase transition in thicker films and highlight the effect of strain on the phase transition temperature. This work establishes the hybrid MBE approach for the growth of heteroepitaxial BaTiO3 films on conducting substrates with scalable thickness and controlled stoichiometry.

Materials Science↗

Freestanding epitaxial SrTiO 3 nanomembranes via remote epitaxy using hybrid molecular beam epitaxy

The epitaxial growth of functional oxides using a substrate with a graphene layer is a highly desirable method for improving structural quality and obtaining freestanding epitaxial nanomembranes for scientific study, applications, and economical reuse of substrates. However, the aggressive oxidizing conditions typically used in growing epitaxial oxides can damage graphene. Here, we demonstrate the successful use of hybrid molecular beam epitaxy for SrTiO 3 growth that does not require an independent oxygen source, thus avoiding graphene damage. This approach produces epitaxial films with self-regulating cation stoichiometry. Furthermore, the film (46-nm-thick SrTiO 3 ) can be exfoliated and transferred to foreign substrates. These results open the door to future studies of previously unattainable freestanding oxide nanomembranes grown in an adsorption-controlled manner by hybrid molecular beam epitaxy. This approach has potentially important implications for the commercial application of perovskite oxides in flexible electronics and as a dielectric in van der Waals thin-film electronics.

36 MATERIALS SCIENCE↗

Solid-state polymer-particle hybrid electrolytes: Structure and electrochemical properties

Solid-state electrolytes (SSEs) are challenged by complex interfacial chemistry and poor ion transport through the interfaces they form with battery electrodes. Here, we investigate a class of SSE composed of micrometer-sized lithium oxide (Li 2 O) particles dispersed in a polymerizable 1,3-dioxolane (DOL) liquid. Ring-opening polymerization (ROP) of the DOL by Lewis acid salts inside a battery cell produces polymer-inorganic hybrid electrolytes with gradient properties on both the particle and battery cell length scales. These electrolytes sustain stable charge-discharge behavior in Li||NCM811 and anode-free Cu||NCM811 electrochemical cells. On the particle length scale, Li 2 O retards ROP, facilitating efficient ion transport in a fluid-like region near the particle surface. On battery cell length scales, gravity-assisted settling creates physical and electrochemical gradients in the hybrid electrolytes. By means of electrochemical and spectroscopic analyses, we find that Li 2 O particles participate in a reversible redox reaction that increases the effective CE in anode-free cells to values approaching 100%, enhancing battery cycle life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hybridization capture sequencing for Vibrio spp. and associated virulence factors

ABSTRACT Proliferation ofVibriospp. in aquatic ecosystems is associated with climate change and, concomitantly, increased incidence of vibriosis. They are autochthonous to aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing (HCS) was employed to profile low-abundanceVibriospp. in environmental samples. The HCS panel targeted a family of molecular chaperones (CPN60) specific to 69Vibriospp. and 162Vibrio-specific virulence factors. This approach was evaluated in parallel with traditional whole-community shotgun sequencing in a metagenomic analysis of water and oyster samples collected from the Chesapeake Bay. In addition,Vibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples were subjected to whole-genome sequencing to determine the genetic characteristics of pathogenicVibriospp. circulating in an aquatic environment. HCS, employed to determine the incidence and characterization of specificVibriospp., yielded significantly greater metagenomic insight, notably a variety of otherVibriospp., including detection ofVibrio cholerae,Vibrio fluvialis, andVibrio aestuarianus, in addition toVibrio parahaemolyticusandVibrio vulnificus, and also important virulence factors not detectable using traditional molecular methods. Thus, pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood. It is concluded that environmental surveillance should include HCS, a valuable tool for the detection and characterization of pathogenic agents in aquatic ecosystems, notably vibrios. IMPORTANCE The increasing prevalence of pathogenicVibriospp. in aquatic ecosystems, driven by climate change, is closely linked to a rise in cholera and vibriosis cases, emphasizing the need for improved environmental surveillance. Vibrios are naturally occurring in aquatic environments globally, but traditional metagenomic methods for detecting and typing pathogenicVibriospp. are challenged by their presence in relatively low abundance and ability to persist in a viable but nonculturable state. In the study reported here, hybridization capture sequencing was employed to profile low-abundanceVibriospp. in metagenomic samples, namely water and oysters collected from the Chesapeake Bay. This approach was evaluated in parallel with traditional whole-community shotgun sequencing and whole-genome sequencing ofVibrio parahaemolyticusandVibrio vulnificusstrains isolated from the samples. Results suggest pathogenicVibriospp. in aquatic ecosystems may be far more common than currently understood, when multiple methods are considered for environmental surveillance.

Microbiology↗

Hybrid PDES Simulation of HPC Networks Using Zombie Packets

Although high-fidelity network simulations have proven to be reliable and cost-effective tools to peer into architectural questions for high-performance computing (HPC) networks, they incur a high resource cost. The time spent in simulating a single millisecond of network traffic in the highest detail can take hours, even for static, well-behaved traffic patterns such as uniform random. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should only be used when appropriate. Thus, there is a need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. Here, we present a surrogate model for HPC networks in which: packets bypass the network, while the network state is left untouched, i.e., suspended. To bypass the network, we use historical data to estimate the arrival time at which every packet should be scheduled at; to suspend the network, all in-flight packets are scheduled to arrive at their destinations, and are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. This light-weight surrogate obtained up to 76× speedup. Keeping the zombies in the network showed an increase in the accuracy of the high-fidelity simulation on restart when compared to restarting the network from an empty state.

HPC networks↗

Integrating and Characterizing HPC Task Runtime Systems for hybrid AI-HPC workloads

Scientific workflows increasingly involve both HPC and machine-learning tasks, combining MPI-based simulations, training, and inference in a single execution. Launchers such as Slurm’s srun constrain concurrency and throughput, making them unsuitable for dynamic and heterogeneous workloads. We present a performance study of RADICAL-Pilot (RP) integrated with Flux and Dragon, two complementary runtime systems that enable hierarchical resource management and high-throughput function execution. Using synthetic and production-scale workloads on Frontier, we characterize the task execution properties of RP across runtime configurations. RP+Flux sustains up to 930 tasks/s, and RP+Flux+Dragon exceeds 1,500 tasks/s with over 99.6% utilization. In contrast, srun peaks at 152 tasks/s and degrades with scale, with utilization below 50%. For IMPECCABLE.v2 drug discovery campaign, RP+Flux reduces makespan by 30–60% relative to srun/Slurm and increases throughput more than four times on up to 1,024. These results demonstrate hybrid runtime integration in RP as a scalable approach for hybrid AI-HPC workloads.

HPC-AI↗

Solid-Binding Proteins: Bridging Synthesis, Assembly, and Function in Hybrid and Hierarchical Materials Fabrication

There is considerable interest in the development of hybrid organic–inorganic materials because of the potential for harvesting the unique capabilities that each system has to offer. Proteins are an especially attractive organic component owing to the high amount of chemical information encoded in their amino acid sequence, their amenability to molecular and computational (re)design, and the many structures and functions they specify. Genetic installation of solid-binding peptides (SBPs) within protein frameworks affords control over the position and orientation of adhesive and morphogenetic segments, and a path toward predictive synthesis and assembly of functional materials and devices, all while harnessing the built-in properties of the host scaffold. Furthermore, we review the current understanding of the mechanisms through which SBPs bind to technologically relevant interfaces, with an emphasis on the variables that influence the process, and highlight the last decade of progress in the use of solid-binding proteins for hybrid and hierarchical materials synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗

Data on temporal complementarity of hybrid renewable energy systems [SWR-23-09]

These datasets describe multiple facets of the temporal complementarity of co-located hybrid renewable energy systems throughout the United States. Several metrics characterizing the complementarity of generation profiles are provided on an annual and monthly basis (for both hourly and daily aggregations). These generation profiles are underpinned by hourly resource data (e.g., the WIND Toolkit and National Solar Radiation Database (NSRDB)) spanning the multi-year period 2007-2013. The data include complementarity results for greater than 1.76 million individual locations within the continental United States (CONUS). The data are intended to accompany two publications on the topic of temporal complementarity: 1) Harrison-Atlas, Dylan, Caitlin Murphy, Anna Schleifer, and Nicholas Grue. "Temporal complementarity and value of wind-PV hybrid systems across the United States." Renewable Energy 201 (2022): 111-123, doi:10.1016/j.renene.2022.10.060; and 2) Murphy, Caitlin, Harrison-Atlas, Dylan, Nicholas Grue, Vahan Gevorgian, Juan Gallego-Calderon, Shiloh Elliot and Thomas Mosier. “A Resource Assessment for FlexPower”. NREL Technical Report.

Harrison-Atlas, Dylan↗

Forecasting for ESCAPE: A Multi-Institution Hybrid Forecasting and Nowcasting Operation for Sea-Breeze Convection Supporting a Ground-Based and Airborne Field Campaign

The Experiment of Sea-Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field project deployed two aircraft and ground-based assets in the vicinity of Houston, Texas, between 27 May and 2 July 2022, examining how meteorological conditions, dynamics, and aerosols control the initiation, early growth stage, and evolution of coastal convective clouds. To ensure that airborne- and ground-based assets were deployed appropriately, a forecasting and nowcasting team was formed. Daily forecasts guided real-time decision-making by assessing synoptic weather conditions, environmental aerosol, and a variety of atmospheric modeling data to assign a probability for meeting specific ESCAPE campaign objectives. During the research flights, a small team of forecasters provided “nowcasting” support by analyzing radar, satellite, and new model data in real time. The nowcasting team proved invaluable to the campaign operation, as sometimes changing environmental conditions affected, for example, the timing of convective initiation. In addition to the success of the forecasting and nowcasting teams, the ESCAPE campaign offered a unique “testbed” opportunity where in-person and virtual support both contributed to campaign objectives. The forecasting and nowcasting teams were each composed of new and experienced forecasters alike, where new forecasters were given invaluable experience that would otherwise be difficult to attain. Both teams received training on forecast models, map analysis, Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), and thermodynamic sounding analysis before the beginning of the campaign. In this article, the ESCAPE forecasting and nowcasting teams reflect on these experiences, providing potentially useful advice for future field campaigns requiring forecasting and nowcasting support in a hybrid virtual/in-person framework.

54 ENVIRONMENTAL SCIENCES↗

Improved fabrication of scalable hybrid silicon nitride–thin-film lithium niobate electro-optic modulators

Strategies for improved fabrication of integrated thin-film lithium niobate electro-optic (EO) Mach–Zehnder modulators (MZMs) are reported using scalable processes and designs. The MZM devices utilize direct bonding of unetched and unpatterned thin-film lithium niobate to patterned and planarized silicon photonic microchips. The latter contains silicon nitride waveguide structures of various widths that are used to form hybrid modes that are suitable for high-bandwidth low-voltage EO modulators based on Pockels effect. We report that the incorporation of appropriately designed outgassing channels and certain modifications to key processing steps helped achieve a greater than 99% reduction in void density during bonding. Void reduction is critically important for these traveling-wave hybrid MZM devices in which the optical mode is controllably distributed between multiple thin layers and propagates over millimeter-scale lengths.

Mere, Viphretuo↗

Long-term hybrid stabilization of the carrier-envelope phase

Controlling the carrier envelope phase (CEP) in mode-locked lasers over practically long timescales is crucial for real-world applications in ultrafast optics and precision metrology. We present a hybrid solution that combines a feed-forward technique to stabilize the phase offset in fast timescales and a feedback technique that addresses slowly varying sources of interference and locking bandwidth limitations associated with gain media with long upper-state lifetimes. We experimentally realize the hybrid stabilization system in an Er:Yb:glass mode-locked laser and demonstrate 75 hours of stabilization with integrated phase noise of 14 mrad (1 Hz to 3 MHz), corresponding to around 11 as of carrier to envelope jitter. Additionally, we examine the impact of environmental factors, such as humidity and pressure, on the long-term stability and performance of the system.

47 OTHER INSTRUMENTATION↗

Hybrid interferometric diagnostic for the refractive index measurement across nearly discrete shock waves

The measurement of high temperature gas properties is key for characterizing high speed flows. Nearly discrete changes in density across shock waves, in particular, are difficult to resolve through traditional fringe-counting interferometric methods. Existing techniques for estimating large fringe jumps are either resolution limited or require specialized window configurations. In this Letter, we describe a unique hybrid interferometric technique that combines narrowband fringes for high resolution and broadband fringes as an absolute reference to measure changes in refractive index with a resolution of up to 7 × 10 −8 across nearly discrete index changes of up to 1.5 × 10 −4 . By capturing fringes with an ultrahigh-speed camera, the refractive index changes across discrete shock fronts can be estimated inside a shock tube with high accuracy and time resolution. First, a novel hybrid calibration method for tracking finite fringes is discussed. Next, this technique is used to measure the post-initial-shock refractive indices for Mach 2.7 to 4.2 flows (pressures from 90.4 to 228.4 kPa). Results are then compared with theoretical values showing agreement within 2%.

Wang, Gwendolyn T. (ORCID:000000016271893X)↗

Hybrid integrated photonic platforms: opinion

While photonic integration has made remarkable progress in recent years, there is no one integrated photonic platform that offers all desired functionalities and manufacturability on the same platform. GaAs and InP-based optoelectronic integrated circuits (OEICs) were very popular in the past decades; however, silicon photonics has recently emerged as a preferred platform due to its high-density and high-yield manufacturability leveraging the CMOS ecosystem, although it lacks optical gain, the Pockels effect, and other characteristics. On the other hand, hybrid photonic integration adds new and diverse functionalities to the host materials like silicon. This opinion paper investigates hybrid integrated photonic platforms, and discusses the new functionalities added to the silicon CMOS photonic platform.

42 ENGINEERING↗