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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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Zirconium-Based Metal–Organic Frameworks as Reusable Antibacterial Peroxide Carriers for Protective Textiles
Countries around the world have sought efficient protective coverings, including masks, gowns, and fabrics, for air purification to protect people against infectious diseases. However, the demand for significant quantities of disposable protective textiles poses a global challenge, especially when the production of protective gear is suspended due to COVID-19 outbreaks in factories and along supply lines. Therefore, the development of reusable, self-decontaminating protective masks and coverings loaded with disinfectants, such as antibacterial peroxide species, presents an attractive strategy to fight against bacteria risks. In this work, we incorporated persulfate ions, which serve as stable active peroxide precursors, into two porous zirconium-based metal–organic frameworks (Zr-MOFs), enabling these materials to act as regenerable reservoirs for the slow release of biocidal hydrogen peroxide upon hydrolysis by contact with humid air. Single-crystal X-ray diffraction studies reveal the two different coordination motifs for the persulfate ions, which can either bridge between two adjacent nodes or coordinate to a single node depending on both the node connectivity and the distances between open metal sites. The active peroxide precursors within the porous Zr-MOF carriers are stable during storage and easily regenerated once consumed. Importantly, these persulfate-loaded Zr-MOFs can be integrated onto textiles using a facile aqueous in-situ growth procedure, and these composites demonstrate potent and reusable biocidal activity against both Gram-negative bacteria and Gram-positive bacteria. Altogether, this approach presents a viable strategy to develop robust protective textiles capable of rapidly deactivating pathogens.
Impact of a Novel Nickel-Based Catalyst and Phenyl-Acrylate-Based Anion-Exchange Membrane in a Direct Urea Fuel Cell
Developing target-specific catalysts and anion-exchange membranes (AEMs) is crucial for direct urea fuel cell (DUFC) performance. To advance the DUFC system, we developed an anode catalyst with a nickel–iron oxyhydroxide/carbon (NiFeOOH/C) nanofibrous structure for the urea oxidation reaction (UOR), where we optimized the Ni/Fe molar ratio as 6:4. The enhanced electrocatalytic activity of the anode (Ni 6 Fe 4 OOH/C) is attributed to the hydroxide group, which responds with urea molecules to enhance the UOR in a pH-neutral system. Here, we employed a recently developed cross-linked phenyl-acrylate-based AEM (PA/M). A DUFC prepared with the anode and PA/M generates a maximum power density of 11.1 mW/cm 2 and 0.92 V open-circuit voltage under 3 M urea as fuel at 25 °C. We further analyzed the applicability of PA/M in a DUFC system by measuring the urea partition coefficients and permeabilities over a range of concentrations.
Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks
Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.
Natural Charge-Transfer Analysis: Eliminating Spurious Charge-Transfer States in Time-Dependent Density Functional Theory via Diabatization, with Application to Projection-Based Embedding
For many types of vertical excitation energies, linear-response time-dependent density functional theory (LR-TDDFT) offers a useful degree of accuracy combined with unrivaled computational efficiency, although charge-transfer excitation energies are often systematically and dramatically underestimated, especially for large systems and those that contain explicit solvent. As a result, low energy electronic spectra of solution-phase chromophores often contain tens to hundreds of spurious charge-transfer states, making LR-TDDFT needlessly expensive in bulk solution. More nefariously, intensity borrowing by the low-energy charge-transfer states can affect intensities of the valence excitations even if those excitation energies are accurate. At higher excitation energies, it is difficult to distinguish spurious CT states from genuine charge-transfer-to-solvent (CTTS) excitations. In this work, we introduce an automated diabatization scheme that enables fast and effective screening of the CTTS acceptor space in bulk solution. Our procedure introduces the concept of “natural charge-transfer orbitals”, which provide a means to isolate characteristic pairs of orbitals that are most likely to participate in a CTTS excitation. The projection of these orbitals onto solvent-centered virtual orbitals provides a criterion for defining the most important solvent molecules in a given excitation. We apply this method to analyze an ab initio molecular dynamics (MD) trajectory of I-(aq) and report the lowest-energy CTTS band in the absorption spectrum. Our results are in excellent agreement with experimental measurements for bulk I-(aq), and only one-third of the water molecules in the I-(H2O)96 simulation cell need to be described with LR-TDDFT in order to obtain excitation energies that are converged to < 0.1 eV. The tools introduced herein will improve the accuracy, efficiency, and usability of LR-TDDFT in solution-phase environments
Neural Network Water Model Based on the MB-Pol Many-Body Potential
The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.
Quantifying the Ligand-Induced Triplet Energy Transfer Barrier in a Quantum Dot-Based Upconversion System
During photon upconversion, quantum dots (QDs) transfer energy to molecules in solution through a long ligand shell. This insulating ligand shell imparts colloidal stability at the expense of efficient photosensitization. For the first time, we quantify the barrier these aliphatic ligands pose for triplet energy transfer in solution. Using transient absorption spectroscopy, we experimentally measure a small damping coefficient of 0.027 Å −1 for a ligand exceeding 10 carbons in length. The dynamic nature of ligands in solution lowers the barrier to charge or energy transfer compared to organic thin films. In addition, we show that surface ligands shorter than 8 carbons in length allow direct energy transfer from the QD, bypassing the need for a transmitter ligand to mediate energy transfer, leading to a 6.9% upconversion quantum yield compared with 0.01% for ligands with 18 carbons. Furthermore, this experimentally derived insight will enable the design of efficient QD-based photosensitizers for catalysis and energy conversion.
Potential Foldamers Based on an ortho- Terphenyl Amino Acid
We describe the synthesis and characterization of a new class of oligomers built from a terphenyl-based amino acid. Here, these oligomeric amides are of interest because the adoption of specific conformations could potentially be driven by the coordinated formation of inter-residue hydrogen bonds and aromatic interactions. Although high-resolution structural data have proven inaccessible, circular dichroism and nuclear magnetic resonance studies suggest that the new oligomers fold concomitantly with discrete self-association in chloroform. Molecular simulations find no sign of folding as a monomer, and in fact suggest that hydrogen bond patterning is anti-cooperative, inhibiting longer helices.
Understanding Mass Transport in Copper Electrolyte-Based Dye-Sensitized Solar Cells
Copper redox shuttles, particularly [Cu(tmby) 2 ] 2+/1+ (tmby = bis(4,4',6,6'-tetramethyl-2,2'-bipyridine)), proved to be among the best electrolytes for dye-sensitized solar cells (DSCs), realizing higher power conversion efficiencies both under full sun and indoor illumination than conventional iodide/triiodide and cobalt electrolytes. Even though [Cu(tmby) 2 ] 2+/1+ renders a relatively higher performance, this metal complex is bulky and is limited by mass transport. Since the regeneration of the dye ground state by Cu I and the reaction of Cu II at the counter electrode are comparatively faster processes, the efficiency of DSC involving Cu I /Cu II electrolytes under relatively high light intensities is largely governed by the diffusion of Cu I /Cu II species. Understanding mass transport in these solar cells will enable further improvements in the performance of such copper-based DSCs. Here in the present study, the role of illumination intensity on the photogenerated current and its relationship to mass transport is evaluated using the best cosensitized dye (D35:XY1) and copper electrolyte ([Cu(tmby) 2 ] 2+/1+ ) combination.
Model-Based Energy and Cost Analysis of Direct Air Capture Using ePTFE-Based Laminate-Structured Gas–Solid Contactors
Carbon dioxide removal (CDR) technologies will play a significant role in limiting global warming if implemented on a large scale. Direct air capture (DAC) is a scalable approach for removing atmospheric carbon, yet the true scope of its scalability remains unclear due to the early stage of technology development and high first plant costs. This study provides groundwork for understanding the technoeconomic trade-offs in developing DAC systems using laminate-structured gas–solid contactors, encompassing the analysis of both contactor and process design spaces. The robust mass transfer and process models outlined in this study provide tools for evaluating DAC processes and designing DAC plants based on cost and energy analysis. First, the key contactor geometrical parameters are identified to understand the CO 2 productivity–energy demand trade-offs, where geometries yielding higher mass transfer rates can achieve higher CO 2 productivities at the expense of energy consumption by fans and steam use. Next, a detailed process parametric study is conducted for DAC systems coupled with steam-assisted temperature-vacuum swing adsorption (S-TVSA) to visualize the trade-offs in the multidimensional design space. The main cost driver dramatically changes over different process conditions, but the operating cost prevailed on the Pareto front, with potential to operate as low as 150 $/tonne-CO 2 (within the cost range of 148–504 $/tonne-CO 2 in this study where the DAC system is coupled with industrial facilities for steam production).
Splitting Mono- and Di-Branched Alkane Isomers by a Robust Aluminum-Based Metal-Organic Framework Material with Optimal Pore Dimensions
The separation of alkanes with different degrees of branching, particularly mono- and dibranched isomers, represents a challenging yet important industrial process for the production of premium gasoline blending components with high octane number. We report here the separation of linear/monobranched and dibranched alkanes through complete molecular sieving by a robust aluminum-based MOF material, Al-bttotb (H 3 bttotb = 4,4',4"-(benzene-1,3,5-triyltris(oxy))tribenzoicacid). Single- and multicomponent adsorption experiments reveal that the material adsorbs linear and monobranched alkanes, but fully excludes their dibranched isomers. Adsorption sites of alkanes within the MOF channels have been identified by single-crystal X-ray diffraction studies, and the adsorption mechanism was explored through computational calculations and modeling. The highly selective adsorption of the MOF should be attributed to its optimal pore dimensions.
Active Reaction Control of Cu Redox State Based on Real-Time Feedback from In Situ Synchrotron Measurements
Abstract not provided
Photocatalytic Biocidal Coatings Featuring Zr 6 Ti 4 -Based Metal–Organic Frameworks
The world is currently suffering socially, economically, and politically from the recent pandemic outbreak due to the Coronavirus Disease 2019 (COVID-19), and those in hospitals, schools, and elderly nursing homes face enhanced threats. Healthcare textiles, such as masks and medical staff gowns, are susceptible to contamination of various pathogenic microorganisms, including bacteria and viruses. Here, metal–organic frameworks (MOFs) can potentially address these challenges due to their tunable reactivity and ability to be incorporated as porous coatings on textile materials. Here, we report how incorporating titanium into the zirconium-pyrene-based MOF NU-1000, denoted as NU-1012, generates a highly reactive biocidal photocatalyst. This MOF features a rare ligand migration phenomenon, and both the Ti/Zr center and the pyrene linker act synergistically as dual active centers and widen the absorption band for this material that results in enhanced reactive oxygen species (ROS) generation upon visible light irradiation. Additionally, we found the ligand migration process is generally applicable to other csq topology Zr-MOFs. Importantly, NU-1012 can be easily incorporated onto cotton textile cloths as a coating, and the resulting composite material demonstrates fast and potent biocidal activity against Gram-negative bacteria (E. coli), Gram-positive bacteria (S. epidermidis), and T7 bacteriophage virus with up to a 7-log (99.99999%) reduction within one hour under simulated daylight.
Mechanically interlocked pyrene-based photocatalysts
Triplet excited-state organic chromophores present countless opportunities for applications in photocatalysis. Here we describe an approach to the engineering of the triplet excited states of aromatic chromophores, which involves incorporating pyrene into pyridinium-containing mechanically interlocked molecules (MIMs). The π-extended nature of the pyrenes enforces [π···π] stacking, affording an efficient synthesis of tetrachromophoric octacationic homo[2]catenanes. These MIMs generate triplet populations and efficient intersystem crossing on account of the formation of a mixed charge-transfer/exciplex electronic state and a nanoconfinement effect, which leads to a high level of protection of the triplet state and extends the triplet lifetimes and yields. These compounds display excellent catalytic activity in photo-oxidation, as demonstrated by the aerobic oxidation of a sulfur-mustard simulant. Furthermore this research highlights the benefits of using the mechanical bond to fine-tune the triplet photophysics of existing aromatic chromophores, providing an avenue for the development of unexplored MIM-based photosensitizers and photocatalysts.
Squeezing the box: isoreticular contraction of pyrene-based linker in a Zr-based metal–organic framework for Xe/Kr separation
Isoreticular synthesis is a powerful tool to enhance specific attributes of a metal–organic framework (MOF). While the isoreticular expansion of MOF structure are prevalent in the literature, the compression of a topology holds great promise for separations due to the contracted pore apertures and/or cavities. Herein, we report the synthesis, characterization and Xe/Kr separation capability of a new Zr-based MOF, NU-1106, connected by the tetratopic linker 1,3,6,8-pyrene tetracarboxylate, which exhibits a compressed ftw topology compared to the extended ones reported previously. NU-1106 showed selective uptake of Xenon over Krypton, providing the potential for use for separations.
Synthesis of atomically precise single-crystalline Ru[subscript 2]-based coordination polymers
Abstract not provided