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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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Transfer Learning Facilitates the Prediction of Polymer–Surface Adhesion Strength
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Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials
Within the framework of Kohn–Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric–isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.
Self-Assembled Oligomers Facilitate Amino Acid-Driven CO 2 Capture at the Air–Aqueous Interface
Direct air capture of CO 2 using amino acid absorbents, such as glycine or sarcosine, is constrained by the relatively slow mass transfer of CO 2 through the air–aqueous interface. Our recent study showed a marked improvement in CO 2 capture by introducing CO 2 -permeable oligo-dimethylsiloxane (ODMS-MIM + ) oligomers with cationic (imidazolium, MIM + ) headgroups. Here, in this work, we have employed all-atom molecular dynamics simulations in combination with subensemble analysis using network theory to provide a detailed molecular picture of the behavior of CO 2 and the glycinate anions (Gly – ) at the ODMS-MIM + decorated air–aqueous interfaces. We show that the cationic head groups of the surfactants enhance the concentration and lifetime of Gly – in the interfacial region, while ODMS tails promote the physisorption of CO 2 in the interfacial region. Together, these two factors increase the effective region of contact and the probability of interactions between CO 2 and Gly – compared to that of the pure air–aqueous interface. The fundamental insights gained in this work establish essential foundations for developing hybrid systems with oligomer-decorated interfaces to maximize the overall CO 2 capture rates.
Tunability and Long-Range Enhancement of Resonance Energy Transfer Facilitated by Plasmonic Nanorods
Resonance energy transfer (RET) between molecules or quantum dots is an important process in many energy-related applications. Different environmental structures have been studied and demonstrated to be able to enhance the RET rates between a nearby donor–acceptor pair. In particular, cylindrical silver nanorods and nanowires have shown an extraordinary ability to transfer energy along their longitudinal axes over large distances. However, the detailed mechanism of such transfer and the effects on the molecular RET process are as yet elusive. In this study, we use the recently developed computational tool based on the plasmon-coupled RET method to systematically study the effects of nanorods with different dimensions on the RET rates. We find that highly frequency-dependent coupling factor (CF) spectra, whose amplitudes determine RET rates, can be obtained due to the localized surface plasmon polariton modes of the rods with nanoscale dimensions. Simple phenomenological models can be derived for the wavelengths of CF peaks in relation to the length and width of the nanorods, providing easy tunability for enhancing the RET rate in specific wavelength ranges. When coupled to longer rods with mesoscale lengths, exponential decay of the CF over long donor–acceptor distances with a small decay constant is observed, leading to the possibility of long-range RET processes. Furthermore, drumhead resonance modes emerge on the flat ends of the rod when the rod’s diameter reaches 300 nm, resulting in extra enhancement to RET rate compared to certain thinner rods. Furthermore, these findings shed new light on the mechanism of plasmonic enhancement with silver nanorods and establish design principles for how to optimally utilize these structures to manipulate RET processes for various applications.
Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model
Metal organic framework (MOF)-based mixedmatrix membranes (MMMs), which embed MOF particles in polymer matrices, combine the advantages of polymeric and inorganic membranes. Multiple previous studies have used the Maxwell model together with molecular simulations and machine learning (ML) to predict the performance of MOF/polymer MMMs. However, the assumption of rigid MOF frameworks in molecular simulations limited the accuracy of the data used in the predictions, particularly in predicting molecular diffusivities. We developed a novel workflow integrating ML models with consideration of MOF flexibility to predict the permeability and selectivity of 131,722 MMMs for CO 2 /CH 4 , O 2 /N 2 and He/H 2 separations. The full range of achievable MMM performance within the Maxwell model was analyzed, and several promising MOFs were identified using this workflow. This approach offers an efficient tool for screening any polymer and MOF combination in gas separation applications.
Counterintuitive Catalytic Reactivity of the Aluminum Oxide “Passivation” Shell of Aluminum Nanoparticles Facilitating the Thermal Decomposition of exo -Tetrahydrodicyclopentadiene (JP-10)
High energy density aluminum nanoparticles (AlNP) have been at the center of attention as additives to hydrocarbon jet fuels like exo-tetrahydrodicyclopentadiene (JP-10, C 10 H 16 ) aiming at a superior performance of volume-limited air-breathing propulsion systems. However, a fundamental understanding of the ignition and combustion chemistry of JP-10 in presence of AlNPs has been elusive. Exploiting an isomer-selective comprehensive identification of the decomposition products in a newly designed high temperature chemical microreactor coupled to vacuum ultraviolet photoionization, we reveal an active low temperature heterogeneous surface chemistry commencing at 650 K involving the alumina (Al 2 O 3 ) shell. Contrary to textbook knowledge of an “inactive alumina surface”, this unconventional reactivity, where oxygen is transferred from alumina to JP-10, leads to generating cyclic, oxygenated organics like phenol (C 6 H 5 OH) and 2,4-cyclopentadiene-1-one (C 5 H 4 O) - key tracers of an alumina-mediated interfacial chemistry. Furthermore, this counterintuitive reactivity transforms our knowledge on the (catalytic) processes of alumina-coated AlNPs on the molecular level.
Evidence of Facilitated Transport in Crowded Nanopores
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Conversion of Co Nanoparticles to CoS in Metal-Organic Framework-Derived Porous Carbon during Cycling Facilitates Na2S Reactivity in a Na-S Battery
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Dopamine-Mediated Polymer Coating Facilitates Area-Selective Atomic Layer Deposition
Area-selective atomic layer deposition (ALD) has the potential to significantly improve current fabrication approaches by introducing a bottom-up process in which robust and conformal thin films are selectively deposited onto patterned substrates. This bottom-up approach requires selective areas of the substrates to be masked to inhibit deposition. Spontaneous self-assembly and organization of a mask, incorporating adhesion and other functions, are particularly attractive for this role as they do not require a separate patterning step. In this work, we make use of the pH/light tunability of catechol adhesion to develop a catechol-functionalized polymer that exhibits tunable adhesion strengths on different materials based on their specific chemistry. Tunable selective deposition was shown between metal/metal oxide substrates by controlling the local pH. Moreover, by controlling the adhesion strength through UV light, the deposition of hafnium oxide (HfO 2 ) during ALD was successfully inhibited.
Site-Independent Hydrogenation Reactions on Oxide-Supported Au Nanoparticles Facilitated by Intraparticle Hydrogen Atom Diffusion
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Hexa-Fe(III) Carboxylate Complexes Facilitate Aerobic Hydrocarbon Oxidative Functionalization: Rh Catalyzed Oxidative Coupling of Benzene and Ethylene to Form Styrene
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