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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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A method of making a stable, sulfur binding composite comprising impregnating a solid aggregate with an organic modifier comprising unsaturated hydrocarbons with at least one double or triple covalent bond between adjacent carbon atoms to create a modifier-impregnated aggregate; heating and drying the modifier-impregnated aggregate to activate the surface of the modifier-impregnated aggregate for reaction with sulfur.
State-of-the-art numerical models describing the kinetics of aerosol particle nucleation and growth from a cooling vapor primarily use a nodal method, in which particles that are smaller than the critical size are omitted from consideration because they are thermodynamically unfavorable. This omission is based on the assumption that most newly formed particles are above the critical size, so that subcritical-size particles are not important to take into account. Due to the nature of the nodal method, it suffers from numerical diffusion, which can cause an artificial broadening of the cluster size distribution leading to a significant overestimation of the number of large-size particles. To address these issues, we propose a more accurate numerical method that explicitly models particles of all sizes, and uses a special numerical scheme that substantially reduces the numerical diffusion and provides high solution accuracy and numerical stability. We extensively compare this novel method to the commonly used nodal solver of the general dynamic equation (GDE) for particle growth and demonstrate that it offers GDE solutions with higher accuracy with low numerical diffusion. Incorporating small subcritical clusters into the solution is crucial for: 1) more precise determination of the entire particle size distribution function and 2) wider applicability of the model to experimental studies with non-monotonic temperature variations leading to particle evaporation. The computational code implementing this numerical method in Python is available upon request.
With the polymer encapsulation of quantum dots via seeded emulsion polymerization we present a powerful tool for the preparation of fluorescent nanoparticles with an extraordinary stability in aqueous solution. The method of the seeded emulsion polymerization allows a straightforward and simple in situ functionalization of the polymer shell under preserving the optical properties of the quantum dots. These requirements are inevitable for the application of semiconductor nanoparticles as markers for biomedical applications. Polymer encapsulated quantum dots have shown only a marginal loss of quantum yields when they were exposed to copper(II)-ions. Under normal conditions the quantum dots were totally quenched in presence of copper(II)-ions. Furthermore, a broad range of in situ functionalized polymer-coated quantum dots were obtained by addition of functional monomers or surfactants like fluorescent dye molecules, antibodies or specific DNA aptamers. Furthermore the emulsion polymerization can be used to prepare multifunctional hybrid systems, combining different nanoparticles within one construct without any adverse effect of the properties of the starting materials.{sup 1,2}.
Ultrathin atomic layer deposition (ALD) coatings on the electrodes of Li-ion batteries can enhance the capacity stability of the Li-ion batteries. To commercialize ALD for Li-ion battery production, spatial ALD is needed to decrease coating times and provide a coating process compatible with continuous roll-to-roll (R2R) processing. The porous electrodes of Li-ion batteries provide a special challenge because higher reactant exposures are needed for spatial ALD in porous substrates. This work utilized a modular rotating cylinder spatial ALD reactor operating at rotation speeds up to 200 revolutions/min (RPM) and substrate speeds up to 200 m/min. The conditions for spatial ALD were adjusted to coat flexible porous substrates. The reactor was initially used to characterize spatial Al2O3 and ZnO ALD on flat, flexible metalized polyethylene terephthalate foils. These studies showed that slower rotation speeds and spacers between the precursor module and the two adjacent pumping modules could significantly increase the reactant exposure. The modular rotating cylinder reactor was then used to coat flexible, model porous anodic aluminum oxide (AAO) membranes. The uniformity of the ZnO ALD coatings on the porous AAO membranes was dependent on the aspect ratio of the pores and the reactant exposures. Larger reactant exposures led to better uniformity in the pores with higher aspect ratios. The reactant exposures were increased by adding spacers between the precursor module and the two adjacent pumping modules. The modular rotating cylinder reactor was also employed for Al2O3 ALD on porous LiCoO2 (LCO) battery electrodes. Uniform Al coverages were obtained using spacers between the precursor module and the two adjacent pumping modules at rotation speeds of 25 and 50 RPM. The LCO electrodes had a thickness of ~49 um and pores with aspect ratios of ~12-25. Coin cells were then constructed using the ALD-coated LCO electrodes and were tested to determine their battery performance. The capacity of the Al2O3 ALD-coated LCO battery electrodes was measured versus the number of charge-discharge cycles. Both temporal and spatial ALD processing methods led to higher capacity stability compared with uncoated LCO battery electrodes. The results for improved battery performance were comparable for temporal and spatial ALD-coated electrodes. The next steps are also presented for scale-up to R2R spatial ALD using the modular rotating cylinder reactor.
Organic−inorganic metal halide perovskites (MHPs) have garnered significant attention due to their outstanding performance in optoelectronic devices, but they are prone to degradation through a variety of pathways. High-pressure studies are being used to understand the mechanical and structural stability of MHPs and investigate new methods for improving these. In this study, we map the high-pressure, low-temperature structural phase diagram of CH 3 NH 3 PbBr 3 (MAPbBr 3 ) between 15−300 K and up to 1.5 GPa. We first compare the temperature and pressure effects on the global and local structures of MAPbBr 3 using synchrotron X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS), respectively, and find evidence of PbBr 6 octahedral distortion. Our results also suggest that He inserts into the MAPbBr 3 structure. The thermodynamics of He insertion into MAPBHe x are calculated to be plausible at room temperature for x ≤ 1 and P = 1−3 GPa and shown to stabilize higher-symmetry phases. This phenomenon increases the fitted bulk modulus of MAPbBr 3 . Structural stabilization by inert atom insertion is proposed as a method to improve the mechanical robustness of MHPs and other soft hybrid materials.
Metal halide perovskite photovoltaic performance required for commercial technology encompasses both efficiency and stability. Advances in both these parameters have recently been reported; however, these strategies are often difficult to directly compare due to differences in perovskite composition, device architecture, fabrication methods, and accelerated stressors applied in stability tests. In particular, it is found that there is a distinct lack of elevated temperature, operational (light and bias) stability data. Furthermore, significant testing is required to understand the interactions when combinations are used (e.g., additives used with posttreatments). Herein, individual and combined additive, posttreatment, and contact layer strategies from recent literature reports under standardized operational stability tests of p-i-n CsMAFA perovskites at 70 degrees C are evaluated. Through analysis of over 1000 devices, it is concluded that the hole-transport layer (HTL) is the most significant component impacting elevated temperature operational stability. This analysis motivates future development of high-performance HTLs.
Quantum mechanical calculations require the repeated solution of a Schrödinger equation for the wavefunctions of the system, from which materials properties follow. Recent work has shown the effectiveness of enriched finite element type Galerkin methods at significantly reducing the degrees of freedom required to obtain accurate solutions. However, time to solution has been adversely affected by the need to solve a generalized rather than standard eigenvalue problem and the ill-conditioning of associated system matrices. In this work, we address both issues by proposing a stable and efficient orbital-enriched partition of unity method to solve the Schrödinger boundary-value problem in a parallelepiped unit cell subject to Bloch-periodic boundary conditions. In the proposed partition of unity method, the three-dimensional domain is covered by overlapping patches, with a compactly-supported weight function associated with each patch. A key ingredient in our approach is the use of non-negative weight functions that possess the flat-top property, i.e., each weight function is identically equal to unity over some finite subset of its support. This flat-top property provides a pathway to devise a stable approximation over the whole domain. On each patch, we use $p$th degree orthogonal (Legendre) polynomials that ensure $p$th order completeness, and in addition include eigenfunctions of the radial Schrödinger equation. Furthermore, we adopt a variational lumping approach to construct a (block-)diagonal overlap matrix that yields a standard eigenvalue problem for which there exist efficient eigensolvers. The accuracy, stability, and efficiency of the proposed method is demonstrated for the Schrödinger equation with a harmonic potential as well as a localized Gaussian potential. In conclusion, we show that the proposed approach delivers optimal rates of convergence in the energy, and the use of orbital enrichment significantly reduces the number of degrees of freedom for a given desired accuracy in the energy eigenvalues while the stability of the enriched approach is fully maintained.
This paper presents a methodology for generating restoration sequences in distribution systems and microgrids with emphasis on deriving optimal switching times for remote control switches (RCS). Microgrid architecture considered are assumed to be operating in islanded single master mode. Control methods for handling the unbalance inherent in distribution systems using single-phase controlled PQ inverters were developed and incorporated in the proposed method. The problem of considering simultaneously both energy optimization and dynamic stability of the system, which is lacking in existing methods, was addressed by studying the transient behavior of microgrids based on extended EMTP simulations and then approximate and incorporate these behaviors into the optimization formulation for restoration. The approach is based on multi-objective optimization of final restored load and total restoration time. The methodology was studied on a modified IEEE 123 node test feeder. An EMTP simulation in PSCAD of the formed microgrids was used to validate the accuracy of the dynamic and steady-state characteristics of the resulting systems. By considering the transient behavior of DGs during restoration, a better restoration sequence that does not trigger protection relays could be realized.
Atomization and spraying are well-established methods for the production of submicrometer- and micrometer- sized powders. In addition, they could be of interest to the immobilization of photocatalytic nanoparticles onto supports because they enable the formation of microporous films with photocatalytic activity. Here, we provide a comparison of aerosol-assisted immobilization methods, such as spray-drying (SD), spray atomization (SA), and spray gun (SG), which were used for the deposition of TiO 2 dispersions onto fibrous filter media. The morphology, microstructure, and electronic properties of the structures with deposited TiO 2 were characterized by SEM and TEM, BET and USAXS, and UV-Vis spectrometry, respectively. The photocatalytic performances of the functionalized filters were evaluated and compared to the benchmark dip-coating method. Our results showed that the SG and SA immobilization methods led to the best photocatalytic and operational performance for the degradation of toluene, whereas the SD method showed the lowest degradation efficiency and poor stability of coating. We demonstrated that TiO2 sprays using the SG and SA methods with direct deposition onto filter media involving dispersed colloidal droplets revealed to be promising alternatives to the dip-coating method owing to the ability to uniformly cover the filter fibers. In addition, the SA method allowed for fast and simple control of the coating thickness as the dispersed particles were continuously directed onto the filter media without the need for repetitive coatings, which is common for the SG and dip-coating methods. Our study highlighted the importance of the proper immobilization method for the efficient photocatalytic degradation of VOCs.
Enzymes have shown promise in various industries due to their functional specificity, catalytic efficiency, and environmental sustainability. These biological catalysts can be a pivotal component of manufacturing pipelines like continuous flow chemistry. For this, there exists a need to robustly immobilize enzymes on solid supports and assess the effects of the solid supports on catalytic performance and stability. Here, we use an industrially relevant model enzyme, C. ensiformis (Jack bean) urease, to demonstrate immobilization and assess performance in the context of continuous flow manufacturing. Various immobilization strategies were screened focusing on immobilization efficiency, protocol simplicity, and urease biocatalyst kinetics. Based on this, CDI-agarose and NHS-agarose resins were identified as the best-performing immobilization strategies for urease. CDI-agarose-urease and NHS-agarose-urease were then scaled up and applied to a large-scale continuous flow reactor to evaluate product yields, operational stability, and long-term stability. These experiments identified differences in stability and performance depending on the immobilization method tested. This highlights the importance of screening immobilization methods and subsequent enzyme performance for each candidate biocatalyst used in manufacturing to promote optimal performance and stability. As such, this work provides a framework for evaluating enzyme biocatalyst immobilization approaches to improve performance and enable transition into industrial processes.
Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.
In this work, we demonstrate a method to fabricate single-layer graphene cantilevers by utilizing multiwalled carbon nanotube (MWNT) stabilizing elements. Cantilevers are produced by focused ion beam milling of suspended graphene with MWNTs on the surface. Stable structures with an aspect ratio of at least 5 can be reliably produced and are found to be considerably stiffer than expected based on the theoretical bending stiffness of a graphene sheet. In conclusion, this process substantially extends the current limits of single-layer graphene cantilever fabrication, facilitating the study of atomically thin membranes and production of previously unrealized nanoelectromechanical systems.
Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.
Surface passivation is an effective way to boost the efficiency and stability of perovskite solar cells (PSCs). However, a key challenge faced by most of the passivation strategies is reducing the interface charge recombination without imposing energy barriers to charge extraction. Here, a novel multifunctional semiconducting organic ammonium cationic interface modifier inserted between the light-harvesting perovskite film and the hole-transporting layer is reported. It is shown that the conjugated cations can directly extract holes from perovskite efficiently, and simultaneously reduce interface non-radiative recombination. Together with improved energy level alignment and the stabilized interface in the device, a triple-cation mixed-halide medium-bandgap PSC with an excellent power conversion efficiency of 22.06% (improved from 19.94%) and suppressed ion migration and halide phase segregation, which lead to a long-term operational stability, is demonstrated. Finally, this strategy provides a new practical method of interface engineering in PSCs toward improved efficiency and stability.
Abstract The battery community has devoted significant effort to improve the performance and reliability of Li‐ion batteries through the development of new materials. The integration of autonomous function provides an alternative strategy to further extend performance. Autonomous material systems mimic the ability of biological systems to self‐protect, sense, regulate, and heal in response to damage and other environmental changes. When incorporated in batteries, these materials enable autonomic restoration of battery performance upon degradation, as well as autonomic shutdown or extinguishment in response to thermal runaway. Successful strategies for improving Li‐ion battery performance include controlled release of microencapsulated functional additives and the integration of self‐healing or adaptive interlayers and binders. These methods have been effective for stabilizing the solid electrolyte interface, preventing battery fires, restoring electrode conductivity, and suppressing Li dendrite growth. Challenges and opportunities exist in the further application of autonomous strategies in the battery environment. A greater variety of encapsulated additives with precise triggered‐release are needed to address specific battery degradation mechanisms. Incorporation of self‐healing polymers in solid‐state batteries facilitates improvement in interfacial and mechanical stability. The integration of autonomous strategies with battery management systems potentially enables enhanced control over multiple battery cycling parameters.
Abstract A representative, low‐temperature gas‐phase reaction mechanism synthesizing polyacenes via ring annulation exemplified by the formation of pentacene (C 22 H 14 ) along with its benzo[ a ]tetracene isomer (C 22 H 14 ) is unraveled by probing the elementary reaction of the 2‐tetracenyl radical (C 18 H 11 . ) with vinylacetylene (C 4 H 4 ). The pathway to pentacene—a prototype polyacene and a fundamental molecular building block in graphenes, fullerenes, and carbon nanotubes—is facilitated by a barrierless, vinylacetylene mediated gas‐phase process thus disputing conventional hypotheses that synthesis of polycyclic aromatic hydrocarbons (PAHs) solely proceeds at elevated temperatures. This low‐temperature pathway can launch isomer‐selective routes to aromatic structures through submerged reaction barriers, resonantly stabilized free‐radical intermediates, and methodical ring annulation in deep space eventually changing our perception about the chemistry of carbon in our universe.
Available data on amine-based sorbents used for the direct air capture (DAC) process were gathered and analyzed to identify the correlations between various aspects of these sorbents and the operating conditions they are used in. It is demonstrated that a moderately high temperature (∼ 50 °C) can help with higher CO 2 capture capacity. Furthermore, the effect of sorbent preparation method on its activity and stability was studied. Also, the influence of amine groups and support choice on amine efficiency and CO 2 capture capacity was discussed. The DAC process conditions proved to play a major role in determining the optimal sorbent. An outlook for characteristics to be sought for in future DAC sorbents for CO 2 removal is proposed.
The electrodes and solid-state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machine learning potential (MLP) are employed to elucidate the volume expansions of the proton-conducting PrNi x Co 1-x O 3-δ (PNC) lattices, manifested under a combined influence of oxygen vacancies (V$^{\cdot\cdot}_O$ ) and proton uptake (OH$^{\cdot}_O$ ) in the bulk at varying Ni/Co occupancies. It is revealed that the Ni/Co occupancy contributes to thermal and chemical expansions differently, where thermal expansions are related to Co occupancy. In contrast, chemical expansions are more closely associated with the Ni occupancy. Both V$^{\cdot\cdot}_O$ and OH$^{\cdot}_O$ lead to higher thermal expansions when compared to the pristine PNC. The temperature increase will negatively impact the hydration-induced chemical expansions. For combined thermal and chemical expansions, it is predicted that the strategies that boost the PCEC's electrochemical performance may harm the electrode–electrolyte interfacial stability, when the Ni occupancy is high, due to severe chemical expansions. Mitigating chemical expansions of the Ni-abundant PNC will benefit the interfacial stability. Finally, the presented computational methods for phonon calculations, based on emerging machine learning interatomic potential techniques are anticipated to have a lasting impact on future PCEC development.