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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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Dynamic surface and subsurface morphology and chemistry of socketed nanoparticles is monitored in situ for an important emerging class of nanoparticles.
A chemical analysis of the emission and absorption spectra in the combustion chamber of a nitrogen tetroxide/aerozine-50 rocket engine was conducted. Measurements were made under conditions of preignition, ignition, and post combustion operating periods. The cause of severe ignition overpressures sporadically observed during the vacuum startup of the Apollo reaction control system engine was investigated. The extent to which residual propellants or condensed intermediate reaction products remain after the engine has been operated in a pulse mode duty cycle was determined.
The effect of moisture on adhesives used in aerospace applications can be modeled with chemically specific techniques such as molecular dynamics simulation. In the present study, the surface energy and work of adhesion are calculated for epoxy surfaces and interfaces, respectively, by using molecular dynamics simulation. Modifications are made to current theory to calculate the work of adhesion at the epoxy-epoxy interface with and without water. Quantitative agreement with experimental values is obtained for the surface energy and work of adhesion at the interface without water. The work of adhesion agrees qualitatively with the experimental values for the interface with water: the magnitude is reduced 15% with respect to the value for the interface without water. A variation of 26% in the magnitude is observed depending on the water configuration at a concentration of 1.6 wt%. The methods and modifications to the method that are employed to obtain these values are expected to be applicable for other epoxy adhesives to determine the effects of moisture uptake on their work of adhesion.
Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.
We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158, 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. Furthermore, the efficiency and accuracy of our neural network architecture are demonstrated through the example of the methaniminium cation CH 2 NH 2 + .
Graphitic carbon nitride (GCN) has attracted significant attention due to its excellent performance in photocatalytic applications. Non-metal doping of GCN has been widely used to improve the efficiency of the material as a photocatalyst. Using a combination of time-domain density functional theory with nonadiabatic molecular dynamics, we study the charge carrier dynamics in oxygen and boron doped GCN systems. The reported simulations provide a detailed time-domain mechanistic description of the charge separation and recombination processes that are of fundamental importance while evaluating the photovoltaic and photocatalytic performance of the material. The appearance of smaller energy gaps due to the presence of dopant states improves the visible light absorption range of the doped systems. At the same time, the nonradiative lifetimes are shortened in the doped systems as compared to the pristine GCN. In the case of boron doped at a carbon (B–C–GCN), the charge recombination time is very long as compared to the other two doped systems owing to the smaller electron–phonon coupling strength between the valence band maximum and the trap state. The results suggest B–C–GCN as the most suitable candidate among three doped systems studied in this work for applications in photocatalysis. Finally, this work sheds light into the influence of dopants on quantum dynamics processes that govern GCN performance and, thus, guides toward building high-performance devices in photocatalysis.
Many liquid phase studies assume that the potential energy surfaces of reacting molecules are the same as in the gas phase, neglecting complex solvent dynamics that can completely alter the nature of chemical reactivity. Even studies that include solvent effects typically only consider them in an average, equilibrium way as part of a potential of mean force (PMF). In this work, we use mixed quantum/classical simulations to compare how equilibrium and non-equilibrium solvent motions affect the photodissociation of a simple diatomic molecule, NaK + , in liquid tetrahydrofuran. A PMF analysis shows that as the excited-state molecule dissociates with the solvent at equilibrium, the bonding electron remains associated with K + at short bond distances but eventually localizes on Na + at the end of dissociation. When we examine non-equilibrium dynamical photodissociation trajectories, however, we find that they fall into three distinct categories: about a quarter of them have the bonding electron mainly associated with Na + , another quarter stay mainly associated with K + , and about half have the bonding electron shared roughly equally between the two ions. The results show that equilibrium PMFs cannot accurately describe the dynamics of bond-breaking chemical reactions in solution because there is insufficient time for the solvent to reach equilibrium on the time scale over which bond dissociation occurs. Furthermore, our analysis shows that the solvent coupling between the electronic energy surfaces is similar at and away from equilibrium, suggesting that other factors, such as solute velocity-driven solvent memory effects, play a more important role in explaining the failure of the equilibrium PMF to predict the non-equilibrium dynamics.
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Cation size primarily governs dynamic correlations in aqueous solutions. However, drawing a clear line between chaotropic and cosmotrope ions is complicated by the fact that all cations delay dynamic correlation decay.
The NASA Jet Propulsion Laboratory (JPL) successfully landed the Mars 2020 Perseverance Rover at Jezero Crater on the Martian surface. Perseverance’s main mission objective is to cache Martian rock and regolith samples in hermetically sealed tubes to be brought back to Earth by future missions. To achieve this goal the rover is equipped with a 2-meter-long Robotic Arm (RA) which manipulates the Turret to interact with the surface. The Turret is comprised of science instruments and tools that enable surface sampling and science. The backbone of the Turret hardware is the Rotary Percussive Coring Drill. The science instruments, which are directly mounted to the structural housing of this drill, must be able to withstand not only the launch and entry, descent and landing (EDL) loads of the vehicle but also the dynamic percussive environment, induced by the drill throughout surface operations. This paper discusses the technical hardware design, development and testing of a vibration isolation system to protect the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) Instrument from these dynamic environments. The hardware design aspect of this problem was limited by both size and mass constraints. The Turret is tightly configured and the physical space in which hardware can reside is restricted due to multiple axes of surface interaction points. Another driving requirement is the extreme range of thermal non-operational environment from -135C to +90C. This, in addition to SHERLOC being a heavier instrument than previously integrated on a turret, made the use of isolation systems employed on previous rover missions problematic. The final design incorporates custom wire mesh springs preloaded within titanium hexapod struts equipped with flexure ends. Custom versions of commercially available wire mesh springs were designed and tested. These custom springs differed in dimension, density and stiffness from the off the shelf options. The newly designed springs went through various stages of flight acceptance testing. Detailed physics-based modeling to understand what loads the isolation system would experience was used to define the test conditions for the springs and entire isolation subsystem. The testing program developed around this was extensive to fully characterize the isolation performance over cleanliness levels, temperature and life. Another major aspect of this work was developing a method of replicating the drill’s percussive environment for performance and life testing of Turret mounted hardware. Attaching the instrument to an operating drill for testing was not a viable option. A method of using a Highly Accelerated Life Test (HALT) table was developed and applied across the project as the most representative and tunable option to physically simulate this new environment. This method, along with launch environment random vibration testing, was used to build an entire dynamic qualification and flight hardware test program. The results of the design, modeling, testing and analysis of the SHERLOC Isolation System is described throughout the following paper.
Partial melting to generate the crust of a planet can create a buoyant residual layer at the top of the mantle which may have important implications for episodic planetary evolution. However, the rate of mixing of such a chemically buoyant layer with a thermally convecting mantle is an important unresolved question. Except for a few laboratory and numerical studies designed to address questions related to convection in the Earth's mantle, previous studies have generally treated on the mixing of passive tracers. The inhibiting role of chemical buoyancy on mixing is intuitively obvious but not fully understood quantitatively. In this study, we examine the dynamics of an intrinsically buoyant fluid layer at the top of a deeper, thermally convecting, infinite Prandtl number fluid that is heated from below.
Large-scale atomistic models for complex polycyclic aromatic hydrocarbon systems help understand the chemical properties and behaviors of complex feedstocks such as coal or petroleum. However, the development and utilization of large-scale models remain limited due to the difficulty in achieving the varied structural characteristics necessary to capture stochastic nature of these feedstocks. Here we demonstrate a systematic workflow to construct stochastic molecular systems from a broad analytical suite: high-resolution transmission electron microscopy (HRTEM), carbon-13 nuclear magnetic resonance spectroscopy ( 13 C NMR), laser desorption ionization mass spectroscopy (LDI-MS), and elemental analysis. We present a model construction and analysis utility of a new Python-based module. We selected one subbituminous and three high-volatile bituminous coals to construct large-scale models (~40,000 atoms). The constructed models were utilized to examine the affinity for solvent extraction (naphthalene or tetralin) and the effect of structural properties (e.g., aromatic cluster size, functional groups, and cross-linking) in reactive molecular dynamics simulations. Complex chemical reactions were monitored with bond order transitions, intermediates formation, and mass distributions. Reactive molecular dynamics simulations suggest a plausible chemical extraction process and products for the complex fossil feedstocks. The results indicated that radical formations with bond breaking of bridging oxygens and carbons were required at high temperatures to facilitate hydrogeneration and extraction of gas molecules from radical-free molecules. We observed that aliphatic chains of tetralin were easily decomposed and combined with radicals to form small size of molecules with aryl bonding, mainly increasing molecules in the 500–1000 Da, while naphthalene had little impact on chemical extraction process.
We report surface passivation in two-dimensional (2D) materials is one of the best approaches to modulate the structural, dynamical, and mechanical stabilities thereby enhancing chemical and physical properties for optoelectronic applications. Here, we report an ab-initio investigation on structurally, thermally, dynamically, and mechanically stable, and experimentally feasible fluorinated penta-BCN (F-BCN) monolayer. The structural reconstruction after fluorination, increases bond lengths and thickness to reduce the average stiffness and elasticity attaining the mechanical isotropy. Nevertheless, the value of 2D Young’s modulus of F-BCN is comparable to the pristine structure at 6% of compression without mechanical instability. A significant bandgap tailoring is achieved, similar to that of mechanical sensitivity, due to applied strain. Remarkably, following the fluorine passivation, the monolayer’s induced wide direct bandgap semiconducting behavior improves optical absorbance and reflectivity, decreases energy loss, changes optical anisotropy, and makes it a promising candidate material for optoelectronic and nanomechanical applications.
A complete understanding of enzyme mechanisms requires atomistic details of chemical reactions. Quantum-based molecular dynamics simulations (QMD) are a potential source of this information, but trade-offs between accuracy and computational cost have limited their use. We previously developed extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) methods that leverage a negligible compromise in accuracy to substantially decrease the cost of QMD simulations. Here, we develop a reactive QMD approach using the latest XL-BOMD formulation, which enables efficient simulations of highly reactive systems, and use it to investigate mechanisms of intermediate formation in isocyanide hydratase (ICH) catalysis. In QMD simulations, molecular analogs of ICH active site residues reacted with para-nitrophenyl isocyanide, forming a thioimidate. Analysis of simulated atomic configurational and charge dynamics revealed a pathway where protonation of the isocyanide carbon occurs prior to thioimidate formation and suggested a possible role of Asp17 as a proton donor in the early phase of ICH catalysis. To test whether the pathway seen using the reactive QMD approach might be relevant to ICH catalysis, we performed X-ray crystallography and pre-steady-state enzyme kinetics studies of wild-type and D17N mutant ICH. Both the structure and kinetics are sensitive to the D17N mutation in a manner that is consistent with the order of the reaction steps seen in the simulations. Mobile protons play essential roles in many enzymes, yet they are difficult to observe experimentally, making the ordering of proton-dependent steps ambiguous in many enzyme mechanisms. The ability to directly simulate model reactions for the design of experiments that provide information about enzyme mechanisms involving mobile protons demonstrates the significance of our reactive QMD approach and motivates further biological applications.
A new computational fluid dynamic code has been developed for the study of mixing and chemical reactions in the flow fields of ramjets and scramjets. The code employs an implicit finite volume, lower-upper symmetric successive overrelaxation scheme for solving the complete two-dimensional Navier-Stokes equations and species transport equations in a fully-coupled and very efficient manner. The combustion processes are modeled by an 8-species, 14-step finite rate chemistry model whereas turbulence is simulated by a Baldwin-Lomax algebraic model. The validity of the code is demonstrated by comparing the numerical calculations with both experimental data and previous calculations of a cold flow helium injection into a straight channel and premixed hydrogen-air reacting flows in a ramped duct. The code is then used to calculate the mixing and chemical reactions of a hydrogen jet transversely injected into a supersonic airstream. Results are presented describing the flow field, the recirculation regions in front and behind the injector, and the chemical reactions.
A new computational fluid dynamic code has been developed for the study of mixing and chemical reactions in the flow fields of ramjets and scramjets. The code employs an implicit finite volume, lower-upper symmetric successive overrelaxation scheme for solving the complete two-dimensional Navier-Stokes equations and species transport equations in a fully-coupled and very efficient manner. The combustion processes are modeled by an 8-species, 14-step finite rate chemistry model whereas turbulence is simulated by a Baldwin-Lomax algebraic model. The validity of the code is demonstrated by comparing the numerical calculations with both experimental data and previous calculations of a cold flow helium injection into a straight channel and premixed hydrogen-air reacting flows in a ramped duct. The code is then used to calculate the mixing and chemical reactions of a hydrogen jet transversely injected into a supersonic airstream. Results are presented describing the flow field, the recirculation regions in front and behind the injector, and the chemical reactions.
We examine the intrinsic physical-chemical properties of the conjugated ladder-type polymer poly(benzimidazobenzophenanthroline) (BBL) in response to electron transfer. We aim at explaining the origin of the anti-ambipolar behavior behind the observed BBL nonlinear response associated with specific device architectures. To elucidate this point, we use theory and computation based on first principles, including density functional theory optimizations, ab initio molecular dynamics, time-dependent DFT, and Marcus-theory analysis. Our results reveal that this redox response is not simply monotonic but follows an alternating odd/even pattern in which gap narrowing and reopening occur sequentially before near-gapless behavior emerges at high charging. Converging theoretical evidence in this work demonstrates that bell shaped conductivity in BBL originates in its fundamental electronic structure and supramolecular organization.
A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.