Strengthening in Metal/Graphene Composites: Capturing the Transition from Interface to Precipitate Hardening
Not provided.
SEARCH · Search NASA
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.
Not provided.
UNKNOWN
Interfacing gene delivery vehicles with biomaterials has the potential to play a key role in diversifying gene transfer capabilities, including localized, patterned, and controlled delivery. However, strategies for modifying biomaterials to interact with delivery vectors must be redesigned whenever new delivery vehicles and applications are explored. We have developed a vector-independent biomaterial platform capable of interacting with various adeno-associated viral (AAV) serotypes. A water-soluble, cysteine-tagged, recombinant protein version of the recently discovered multi-AAV serotype receptor (AAVR), referred to as cys-AAVR, was conjugated to maleimide-displaying polycaprolactone (PCL) materials using click chemistry. The resulting cys-AAVR-PCL system bound to a broad range of therapeutically relevant AAV serotypes, thereby providing a platform capable of modulating the delivery of all AAV serotypes. Intramuscular injection of cys-AAVR-PCL microspheres with bound AAV vectors resulted in localized and sustained gene delivery as well as reduced spread to off-target organs compared to a vector solution. This cys-AAVR-PCL system is thus an effective approach for biomaterial-based AAV gene delivery for a broad range of therapeutic applications.
A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.
The formation and preservation of the active phase of the catalysts at the triple-phase interface during CO 2 capture and reduction is essential for improving the conversion efficiency of CO 2 electroreduction toward value-added chemicals and fuels under operational conditions. Designing such ideal catalysts that can mitigate parasitic hydrogen generation and prevent active phase degradation during the CO 2 reduction reaction (CO 2 RR), however, remains a significant challenge. Herein, we developed an interfacial engineering strategy to build a new SnO x catalyst by invoking multiscale approaches. This catalyst features a hierarchically nanoporous structure coated with an organic F-monolayer that modifies the triple-phase interface in aqueous electrolytes, substantially reducing competing hydrogen generation (less than 5%) and enhancing CO 2 RR selectivity (~90%). This rationally designed triple-phase interface overcomes the issue of limited CO 2 solubility in aqueous electrolytes via proactive CO 2 capture and reduction. Concurrently, we utilized pulsed square-wave potentials to dynamically recover the active phase for the CO 2 RR to regulate the production of C1 products such as formate and carbon monoxide (CO). This protocol ensures profoundly enhanced CO 2 RR selectivity (~90%) compared with constant potential (~70%) applied at -0.8 V (V vs RHE). We further achieved a mechanistic understanding of the CO 2 capture and reduction processes under pulsed square-wave potentials via in situ Raman spectroscopy, thereby observing the potential-dependent intensity of Raman vibrational modes of the active phase and CO 2 RR intermediates. Finally, this work will inspire material design strategies by leveraging triple-phase interface engineering for emerging electrochemical processes, as technology moves toward electrification and decarbonization.
High-throughput single-cell mass spectrometry is a rapidly evolving field that requires innovative sampling and ionization techniques to balance speed, sensitivity, and reliability for metabolomic and lipidomic analyses. This study provides a comparative analysis of two cutting-edge ionization platforms for single-cell analysis: Liquid Vortex Capture (LVC) and Rapid Droplet Sampling Interface (RDSI). The performance was benchmarked by testing pharmaceuticals, EquiSPLASH, and single-cell experiments. RDSI demonstrated up to 100-fold improvements in sensitivity for drugs and lipids such as propranolol, amiodarone, atorvastatin, and phosphocholines in water and phosphate-buffered solutions. This was attributed to its low-flow rate operation (3 μL/min) and reduced dilution. Conversely, LVC excelled in handling higher liquid volumes with greater reproducibility due to its higher solvent flow rate (200 μL/min), enabling increased dilution, solubility, and cleaning. Single-cell uptake of atorvastatin incubated for 10 min, or amiodarone incubated for 24 h in HepG2 cells, similarly revealed up to 85-fold enhancement in sensitivity by RDSI for drugs and lipids. These findings highlight the potential of RDSI for enhancing sensitivity in single-cell drug monitoring and lipidomics.
In this paper a numerical procedure for simulating two-fluid flows is presented. This procedure is based on the Volume of Fluid (VOF) method proposed by Hirt and Nichols and the continuum surface force (CSF) model developed by Brackbill, et al. In the VOF method fluids of different properties are identified through the use of a continuous field variable (color function). The color function assigns a unique constant (color) to each fluid. The interfaces between different fluids are distinct due to sharp gradients of the color function. The evolution of the interfaces is captured by solving the convective equation of the color function. The CSF model is used as a means to treat surface tension effect at the interfaces. Here a modified version of the CSF model, proposed by Jacqmin, is used to calculate the tension force. In the modified version, the force term is obtained by calculating the divergence of a stress tensor defined by the gradient of the color function. In its analytical form, this stress formulation is equivalent to the original CSF model. Numerically, however, the use of the stress formulation has some advantages over the original CSF model, as it bypasses the difficulty in approximating the curvatures of the interfaces. The least-squares finite element method (LSFEM) is used to discretize the governing equation systems. The LSFEM has proven to be effective in solving incompressible Navier-Stokes equations and pure convection equations, making it an ideal candidate for the present applications. The LSFEM handles all the equations in a unified manner without any additional special treatment such as upwinding or artificial dissipation. Various bench mark tests have been carried out for both two dimensional planar and axisymmetric flows, including a dam breaking, oscillating and stationary bubbles and a conical liquid sheet in a pressure swirl atomizer.
This review offers a succinct overview of the development of a vacuum-compatible microfluidic reactor system for analysis at the liquid vacuum interface (SALVI), and its diverse applications in in situ, in vivo, and in operando imaging of liquid surfaces as well as the air-liquid (a-l), liquid-liquid (l-l), and solid-liquid (s-l) interfaces in the past decade. SALVI is one of the first microfluidics-based reactors that has enabled direct analysis of volatile liquids in vacuum surface tools such as scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Its integration into ambient and vacuum spectroscopy and microscopy is illustrated. Several applications are highlighted including (1) imaging nanoparticles in liquid using in situ SEM; (2) mapping the evolving l-l interface using in situ x-ray absorption spectroscopy and ToF-SIMS; (3) following complex a-l interfacial oxidation reaction products using in situ ToF-SIMS; (4) capturing biological interfaces of cells and microbes via in vivo multimodal and correlative imaging; and (5) monitoring the dynamic solid electrode and liquid electrolyte interface using in operando molecular imaging. Finally, outlook and recommendations are presented. Besides showing the holistic information volume obtained by real-time multiplexed imaging, this review intends to convey the importance of tool development in revolutionizing surface and interface analysis using vacuum platforms previously limited to solid surfaces. Microfluidics is manifested to be not limited to ambient conditions in many examples in this review. Moreover, fundamental interfacial phenomena underpinning mass and charge transfer can now be pursued in real time via innovated chemical imaging and spectroscopy.
This paper presents the development of a two-phase CFD model that is used to study sloshing of a cryogenic fluid (LN2) undergoing phase change generated by lateral acceleration and its effects on the ensuing heat and mass transfer and pressure drop in a small-scale tank. In this model the interface is captured using the volume-of-fluid (VOF) method [1]. Kinetic-theory based Schrage relation [2] is used for calculating phase change mass transfer at the liquid-vapor interface. Computational results are compared to the data provided by a non-isothermal sloshing experiment with phase change conducted by the University of Tokyo and JAXA in 2018 using liquid Nitrogen in a transparent tank. The effect of different thicknesses of a liquid temperature stratification layer created at the interface on the pressure drop during sloshing was studied experimentally. CFD results for interface movement and tank pressure are presented and compared in this paper to experimental data for the cases with the smallest and largest liquid temperature stratification thickness.
As power becomes an important resource to provision for modern HPC systems it becomes crucial to obtain deeper insights into application's power and thermal characteristics and to correlate application context with important system-level measurements. Existing profiling tools to monitor power and thermal measurements either operate at a granularity that is not fine enough to correlate with application-level events that describe application context or are not equipped to sample application-level events transparently. We introduce LibPowerMon, a lightweight, vendor-neutral user-level profiling framework to simultaneously sample user-specified application events and system-level metrics at up to 1 kHz sampling intervals. At the application level, LibPowerMon provides a source-level phase markup interface to capture application context. At runtime level, LibPowerMon records MPI and OpenMP events and can be extended to include other runtimes. At the system level, LibPowerMon samples power, thermal and relevant metrics, and provides an extensible interface to system's control capabilities. In this release, LibPowerMon supports monitoring and control functionalities on the Intel, IBM Power9 and Nvidia Volta architectures.
Thermal stratification and self-pressurization of partially filled liquid hydrogen (LH2) storage tanks under microgravity condition is studied theoretically. A spherical tank is subjected to a uniform and constant wall heat flux. It is assumed that a vapor bubble is located in the tank center such that the liquid-vapor interface and tank wall form two concentric spheres. This vapor bubble represents an idealized configuration of a wetting fluid in microgravity conditions. Dimensionless mass and energy conservation equations for both vapor and liquid regions are numerically solved. Coordinate transformation is used to capture the interface location which changes due to liquid thermal expansion, vapor compression, and mass transfer at liquid-vapor interface. The effects of tank size, liquid fill level, and wall heat flux on the pressure rise and thermal stratification are studied. Liquid thermal expansion tends to cause vapor condensation and wall heat flux tends to cause liquid evaporation at the interface. The combined effects determine the direction of mass transfer at the interface. Liquid superheat increases with increasing wall heat flux and liquid fill level and approaches an asymptotic value.
Thermal stratification and self-pressurization of partially filled liquid hydrogen (LH2) storage tanks under microgravity condition is studied theoretically. A spherical tank is subjected to a uniform and constant wall heat flux. It is assumed that a vapor bubble is located in the tank center such that the liquid-vapor interface and tank wall form two concentric spheres. This vapor bubble represents an idealized configuration of a wetting fluid in microgravity conditions. Dimensionless mass and energy conservation equations for both vapor and liquid regions are numerically solved. Coordinate transformation is used to capture the interface location which changes due to liquid thermal expansion, vapor compression, and mass transfer at liquid-vapor interface. The effects of tank size, liquid fill level, and wall heat flux on the pressure rise and thermal stratification are studied. Liquid thermal expansion tends to cause vapor condensation and wall heat flux tends to cause liquid evaporation at the interface. The combined effects determine the direction of mass transfer at the interface. Liquid superheat increases with increasing wall heat flux and liquid fill level and approaches an asymptotic value.
Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to assist loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to counteract the effect reduced gravity has on the body. Astronauts will exercise on a flywheel based device on the second Orion Exploration Mission (EM2). The effect that the flywheel load profile has on biomechanics is unknown when compared to free weights or a simulated free weight profile. The purpose of this evaluation is to compare the differences in lower body kinematics and kinetics between the flywheel and free weight profile. Test subjects were instrumented with reflective markers for motion capture data collection while exercising on the Device for Aerobic and Resistive Training (DART) prototype developed by TDA Research, Inc. of Wheat Ridge, CO. Exercises performed were either the squat while wearing a harness or deadlift while grasping a T-bar, both of which interfaced with the DART through a rope cable. The DART, a motorized device controlled via software, is capable of producing different load profiles. Profiles include simulated free weights with adjustable eccentric overload capability and flywheel. Test variables included the applied device load and the load profile, both set through the DART software interface. Motion capture data was collected with a 12 camera system (Smart-DX, BTS Bioengineering, Brooklyn, NY). Bilateral ground reaction force data were collected with force plates (P6000, BTS Bioengineering). DART cable force was recorded through an internal load cell. Data was collected from a total of four test subjects. The three repetition maximum was determined for each test subject for both squat and deadlift. High, medium and low loads were determined based on this maximum. The test subject performed five repetitions of each exercise at each load and each test trial was repeated twice. Cadence was controlled during exercising. Biomechanical data were used to drive the models utilizing the OpenSim software platform. Subject specific models were scaled to match the anthropometrics of the test subjects and used to estimate the peak joint angle, joint range of motion, the peak joint moment and the joint moment impulse of the lower extremity joints and the lumbar joint.These are preliminary results because the data analysis is ongoing. There was a lot of inter-subject variability, however, in general, there was a greater peak lumbar flexion angle for the flywheel squat exercise. There was a trend towards a greater range of motion at the hip for the squat exercise with free weight. There was also a greater hip extension, lumbar extension and ankle plantar flexion impulse moment during the squat exercise with free weight. During the deadlift exercise, the peak knee flexion angle and peak knee extension moment were greater when using the free weight profile. For deadlift, the hip extension, lumbar extension and ankle plantar flexion impulse moment tended to be greater with the free weight profile. Overall, the kinematic and kinetic outcomes for the flywheel profile were either statistically the same as free weight profile in many cases, significantly reduced in a few cases, and rarely higher.
Metallic catalyst modification by organic ligands is an emerging catalyst design in enhancing the activity and selectivity of electrocatalytic carbon dioxide (CO 2 ) reactive capture and reduction to value-added fuels. However, a lack of fundamental science on how these ligand–metal interfaces interact with CO 2 and key intermediates under working conditions has resulted in a trial-and-error approach for experimental designs. With the aid of density functional theory calculations, we provided a comprehensive mechanism study of CO 2 reduction to multicarbon products over aminothiolate-coated copper (Cu) catalysts. Our results indicate that the CO 2 reduction performance was closely related to the alkyl chain length, ligand coverage, ligand configuration, and Cu facet. The aminothiolate ligand–Cu interface significantly promoted initial CO 2 activation and lowered the activation barrier of carbon–carbon coupling through the organic (nitrogen (N)) and inorganic (Cu) interfacial active sites. Experimentally, the selectivity and partial current density of the multicarbon products over aminothiolate-coated Cu increased by 1.5-fold and 2-fold, respectively, as compared to the pristine Cu at –1.16 V RHE , consistent with our theoretical findings. Furthermore, this work highlights the promising strategy of designing the ligand–metal interface for CO 2 reactive capture and conversion to multicarbon products.
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in dynamical systems, particularly those involving sharp moving boundaries with complex initial morphologies, remains a challenge. Here, this study introduces an approach combining residual-based adaptive refinement (RBAR) with causality-informed training to enhance the performance of PINNs in solving spatio-temporal PDEs. Our method employs a three-step iterative process: initial causality-based training, RBAR-guided domain refinement, and subsequent causality training on the refined mesh. Applied to the Allen-Cahn equation, a widely-used model in phase field simulations, our approach demonstrates significant improvements in solution accuracy and computational efficiency over traditional PINNs. Notably, we observe an ‘overshoot and relocate’ phenomenon in dynamic cases with complex morphologies, showcasing the method’s adaptive error correction capabilities. This synergistic interaction between RBAR and causality training enables accurate capture of interface evolution, even in challenging scenarios where traditional PINNs fail. Our framework not only resolves the limitations of uniform refinement strategies but also provides a generalizable methodology for solving a broad range of spatio-temporal PDEs. The enhanced performance of the RBAR–causality combined framework demonstrates its strong potential for advancing PINN-based modeling of physical systems characterized by complex, evolving interfaces.
Potential-induced degradation (PID) remains a significant reliability concern for photovoltaic (PV) modules, arising when a voltage difference between the module frame and the solar cells drives unintended leakage current through the glass-encapsulant stack. Although PID ultimately manifests as PID-s, PID-p, or PID-c, the underlying behavior of the leakage current-its magnitude and time dependence-requires clearer electrochemical interpretation. Traditional explanations attribute the initial transient current to bulk capacitive elements of the glass, encapsulant, and antireflection coatings, and the steady-state current according to their effective ohmic resistance. More recent studies, however, indicate that electrochemical charge-transfer processes at the encapsulant-metallization interface can play a dominant role in defining the leakage-current path. This paper develops a unified electrochemical framework for modeling PID leakage current. First, an RC-equivalent circuit is formulated by combining conventional RC elements with a Randles-type interface to capture transient leakage current through double-layer capacitance and faradaic processes at ionic-electronic boundaries. Second, the steady-state current-voltage behavior is explained using a linearized Butler-Volmer relationship, showing that the measured ohmic response corresponds to the low-overpotential limit of charge-transfer kinetics. Analytical results demonstrate that, for typical module materials-3.2-mm soda-lime glass and 0.45-mm encapsulant-the dominant modulators to PID leakage current are the glass surface resistance (under dry-surface conditions), the glass bulk capacitance, and the encapsulant resistance (under wet-surface conditions), with soda lime glass surface and EVA/POE encapsulant resistances primarily governing steady-state current. The proposed electrochemical model is validated against measured leakage-current data, showing good agreement in both the magnitude and the time-dependent evolution of PID leakage current.
The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.