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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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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effects of penta‐coordinated Al 3+ sites and Ni defective sites on Ni/ Al 2 O 3 for CO methanation

Abstract Engineering sophisticated structure of Al 2 O 3 and controlling the structure of counterpart metal active sites remain challenges to achieve a high catalytic‐performance in heterogeneous catalysis. Herein, we present a confinement strategy to stabilize homogeneous Ni by penta‐coordinated Al 3+ anchoring sites in Al 2 O 3 . This approach is involved in using a metal–organic framework as host to load Ni 2+ ions, by the aim of producing a confined Ni/Al 2 O 3 catalyst after a standard calcination. Metal–support interaction between Ni and Al 2 O 3 was tailored to be medium to avoid the formation of inactive NiAl 2 O 4 , which favors the generation of more available Ni active sites accessible to the reactants. The resultant Ni/Al 2 O 3 exhibited superior catalytic performance in comparison with the control Ni/Al 2 O 3 in CO methanation owing to the presence of defective sites on sufficient Ni 0 surface. Furthermore, the presence of oxygen vacancies on Al 2 O 3 and hydrogen spillover contributed toward excellent coke resistance properties in the reaction.

Engineering↗
DOE OSTI2025

Computational screening of fly ash zeolite sorbents for boric acid removal

In the United States, many impoundments at coal-fired power plants contain elevated contaminants like arsenic, boron, barium, and selenium. Zeolites synthesized from fly ash show promise as sorbents for these contaminants. However, optimizing sorption capacity is challenging due to numerous possible topologies, silicon to aluminum (Si/Al) ratios, and cation types. In this study, molecular simulations are used to design cationic zeolites for boric acid adsorption. Force field models based on quantum mechanical calculations (PBE + D2) for Na-, Ca-, Mn-, and Fe-exchanged chabazite and LTA are presented. The new D2FF force fields reproduce DFT energies with about half the error of UFF. Zeolite performance depends on Si/Al ratio and cation type, with low Si/Al ratio chabazite (CHA) and phillipsite (PHI) zeolite frameworks exchanged with Ca 2+ or Na + /Ca 2+ mixtures showing the highest adsorption. In conclusion, these findings suggest tailored fly ash-derived zeolites could provide effective boron removal from leachate ponds.

CCR impoundment↗
DOE OSTI2020

Control of the Folding Dynamics of Self-Reconfiguring Magnetic Microbots Using Liquid Crystallinity

Reconfigurable microdevices are being explored in a range of contexts where their life-like abilities to move and change shape are important. While much work has been done to control the motion of these microdevices by engineering their geometry and composition, little is known about their dynamics in complex fluid environments with non-Newtonian rheology. Here in this paper, it is shown how the actuation dynamics of reconfigurable microdevices made by assembly of patchy magnetic microcubes, which are referred to as “microbots,” can be modulated by their interactions with the anisotropic viscoelastic environment of a liquid crystal (LC). The free energy arising from the elastic strain of LC and formation of topological defects around the microbots influences their folding dynamics, which can be tuned by tailoring both the far-field orientation of the LC and the local ordering of the LC at the microbot surfaces. These findings represent a first step toward establishing a general set of design rules to control the dynamics of microbots using complex anisotropic fluids.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2020

Strain‐Enhanced Metallic Intermixing in Shape‐Controlled Multilayered Core–Shell Nanostructures: Toward Shaped Intermetallics

Abstract Controlling the surface composition of shaped bimetallic nanoparticles could offer precise tunability of geometric and electronic surface structure for new nanocatalysts. To achieve this goal, a platform for studying the intermixing process in a shaped nanoparticle was designed, using multilayered Pd‐Ni‐Pt core–shell nanocubes as precursors. Under mild conditions, the intermixing between Ni and Pt could be tuned by changing layer thickness and number, triggering intermixing while preserving nanoparticle shape. Intermixing of the two metals is monitored using transmission electron microscopy. The surface structure evolution is characterized using electrochemical methanol oxidation. DFT calculations suggest that the low‐temperature mixing is enhanced by shorter diffusion lengths and strain introduced by the layered structure. The platform and insights presented are an advance toward the realization of shape‐controlled multimetallic nanoparticles tailored to each potential application.

Williams, Benjamin P.↗
DOE OSTI2020

Soft Polymer Janus Nanoparticles at Liquid–Liquid Interfaces

Abstract Soft polymeric Janus nanoparticles (JNPs), made from polystyrene‐ b ‐poly(butadiene)‐ b ‐poly(methylmethacrylate), PS‐PB‐PMMA, triblock terpolymers, assemble into a monolayer at the water–oil interface to reduce interfacial tension. The extent to which the polymer chains can deform influences the packing density of the JNPs at the interface. The longer the polymer chains are relative to the core, the softer are the JNPs, resulting in a JNPs assembly with a lower initial lateral packing density. The interfacial activity of JNPs can be further tuned by complexation of the PMMA chains with lithium ions that are introduced into the water phase. This work provides a fundamental understanding of soft JNPs packing at the water–oil interface and provides a strategy to tailor the areal density of soft JNPs at liquid–liquid interface, enabling the design of smart responsive structured‐liquid systems.

Jiang, Yufeng↗
DOE OSTI2020

Thermally Activated Adsorption in Metal–Organic Frameworks with a Temperature‐Tunable Diffusion Barrier Layer

Abstract Modification of the external surfaces of metal–organic frameworks offers a new level of control over their adsorption behavior. It was previously shown that capping of MOFs with ethylenediamine (EDA) can effectively retain small gaseous molecules at room temperature. Reported here is a temperature‐induced variation in the capping‐layer gate‐opening mechanism through a combination of in situ infared experiments and ab initio simulations of the capping layer. An atypical acceleration and increase in the loading of weakly adsorbed molecules upon raising the temperature above room temperature is observed. These findings show the discovery of novel temperature‐dependent kinetics that goes beyond standard kinetics and suggest a new avenue for tailoring selective adsorption by thermally tuning the surface barrier.

Tan, Kui↗
DOE OSTI2021

Heterometallic Actinide‐Containing Photoresponsive Metal‐Organic Frameworks: Dynamic and Static Tuning of Electronic Properties

Abstract Acquiring fundamental knowledge of properties of actinide‐based materials is a necessary step to create new possibilities for addressing the current challenges in the nuclear energy and nuclear waste sectors. In this report, we established a photophysics–electronics correlation for actinide‐containing metal‐organic frameworks (An‐MOFs) as a function of excitation wavelength, for the first time. A stepwise approach for dynamically modulating electronic properties was applied for the first time towards actinide‐based heterometallic MOFs through integration of photochromic linkers. Optical cycling, modeling of density of states near the Fermi edge, conductivity measurements, and photoisomerization kinetics were employed to shed light on the process of tailoring optoelectronic properties of An‐MOFs. Furthermore, the first photochromic MOF‐based field‐effect transistor, in which the field‐effect response could be changed through light exposure, was constructed. As a demonstration, the change in current upon light exposure was sufficient to operate a two‐LED fail‐safe indicator circuit.

Martin, Corey R.↗
DOE OSTI2021

Conductive Metallophthalocyanine Framework Films with High Carrier Mobility as Efficient Chemiresistors

Abstract The poor electrical conductivity of two‐dimensional (2D) crystalline frameworks greatly limits their utilization in optoelectronics and sensor technology. Herein, we describe a conductive metallophthalocyanine‐based NiPc‐CoTAA framework with cobalt(II) tetraaza[14]annulene linkages. The high conjugation across the whole network combined with densely stacked metallophthalocyanine units endows this material with high electrical conductivity, which can be greatly enhanced by doping with iodine. The NiPc‐CoTAA framework was also fabricated as thin films with different thicknesses from 100 to 1000 nm by the steam‐assisted conversion method. These films enabled the detection of low‐concentration gases and exhibited remarkable sensitivity and stability. This study indicates the enormous potential of metallophthalocyanine‐based conductive frameworks in advanced stand‐off chemical sensors and provides a general strategy through tailor‐make molecular design to develop sensitive and stable chemical sensors for the detection of low‐concentration gases.

Yue, Yan↗
DOE OSTI2021

Designer Nanomaterials through Programmable Assembly

Nanoparticles have long been recognized for their unique properties, leading to exciting potential applications across optics, electronics, magnetism, and catalysis. These specific functions often require a designed organization of particles, which includes the type of order as well as placement and relative orientation of particles of the same or different kinds. DNA nanotechnology offers the ability to introduce highly addressable bonds, tailor particle interactions, and control the geometry of bindings motifs. Here, we discuss how developments in structural DNA nanotechnology have enabled greater control over 1D, 2D, and 3D particle organizations through programmable assembly. This Review focuses on how the use of DNA binding between nanocomponents and DNA structural motifs has progressively allowed the rational formation of prescribed particle organizations. Here, we offer insight into how DNA-based motifs and elements can be further developed to control particle organizations and how particles and DNA can be integrated into nanoscale building blocks, so-called “material voxels”, to realize designer nanomaterials with desired functions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Multifunctional Charge and Hydrogen‐Bond Effects of Second‐Sphere Imidazolium Pendants Promote Capture and Electrochemical Reduction of CO 2 in Water Catalyzed by Iron Porphyrins**

Abstract Microenvironments tailored by multifunctional secondary coordination sphere groups can enhance catalytic performance at primary metal active sites in natural systems. Here, we capture this biological concept in synthetic systems by developing a family of iron porphyrins decorated with imidazolium (im) pendants for the electrochemical CO 2 reduction reaction (CO 2 RR), which promotes multiple synergistic effects to enhance CO 2 RR and enables the disentangling of second‐sphere contributions that stem from each type of interaction. Fe‐ ortho ‐im(H) , which poises imidazolium units featuring both positive charge and hydrogen‐bond capabilities proximal to the active iron center, increases CO 2 binding affinity by 25‐fold and CO 2 RR activity by 2000‐fold relative to the parent Fe tetraphenylporphyrin ( Fe‐TPP ). Comparison with monofunctional analogs reveals that through‐space charge effects have a greater impact on catalytic CO 2 RR performance compared to hydrogen bonding in this context.

Narouz, Mina R.↗
DOE OSTI2022

Anode‐Free Lithium–Sulfur Cells Enabled by Rationally Tuning Lithium Polysulfide Molecules

Abstract The two major barriers of practical lithium–sulfur batteries are the poor reversibility of lithium‐metal anode and sluggish kinetics of sulfur cathode. Here, we report a simple yet cogent, molecular tailoring approach for lithium polysulfides, enabling a synergistic enhancement of anode reversibility and cathode kinetics. We show that SnI 4 coordinates with lithium polysulfides to form soluble complexes, resulting in a Li 2 SnS 3 ‐rich anode interphase layer. As Li 2 SnS 3 is stable against parasitic reactions and has a lower ionic resistance over cycling, the Li plating/stripping efficiency is greatly improved. In addition, the formation of soluble complexes between SnI 4 and lithium polysulfides play a non‐negligible role in suppressing the clustering behavior of lithium polysulfide molecules, resulting in a significant enhancement in sulfur conversion kinetics under lean electrolyte conditions. The synergistic improvement is validated in anode‐free, lean‐electrolyte pouch cells with a Li 2 S cathode that displays capacity retention of 78 % after 100 cycles.

Ren, Yuxun↗
DOE OSTI2022

Maximum Impact of Ionic Strength on Acid‐Catalyzed Reaction Rates Induced by a Zeolite Microporous Environment

Abstract The intracrystalline ionic environment in microporous zeolite can remarkably modify the excess chemical potential of adsorbed reactants and transition states, thereby influencing the catalytic turnover rates. However, a limit of the rate enhancement for aqueous‐phase dehydration of alcohols appears to exist for zeolites with high ionic strength. The origin of such limitation has been hypothesized to be caused by the spatial constraints in the pores via, e.g., size exclusion effects. It is demonstrated here that the increase in turnover rate as well as the formation of a maximum and the rate drop are intrinsic consequences of the increasingly dense ionic environment in zeolite. The molecularly sized confines of zeolite create a unique ionic environment that monotonically favors the formation of alcohol‐hydronium ion complexes in the micropores. The zeolite microporous environment determines the kinetics of catalytic steps and tailors the impact of ionic strength on catalytic rates.

Liu, Qiang↗
DOE OSTI2023

Synergizing Electron and Heat Flows in Photocatalyst for Direct Conversion of Captured CO 2

Abstract We report a ternary hybrid photocatalyst architecture with tailored interfaces that boost the utilization of solar energy for photochemical CO 2 reduction by synergizing electron and heat flows in the photocatalyst. The photocatalyst comprises cobalt phthalocyanine (CoPc) molecules assembled on multiwalled carbon nanotubes (CNTs) that are decorated with nearly monodispersed cadmium sulfide quantum dots (CdS QDs). The CdS QDs absorb visible light and generate electron‐hole pairs. The CNTs rapidly transfer the photogenerated electrons from CdS to CoPc. The CoPc molecules then selectively reduce CO 2 to CO. The interfacial dynamics and catalytic behavior are clearly revealed by time‐resolved and in situ vibrational spectroscopies. In addition to serving as electron highways, the black body property of the CNT component can create local photothermal heating to activate amine‐captured CO 2 , namely carbamates, for direct photochemical conversion without additional energy input.

Choi, Chungseok↗
DOE OSTI2023

Hexaazatriphenylene‐Based Two‐Dimensional Conductive Covalent Organic Framework with Anisotropic Charge Transfer

Abstract The development of covalent organic frameworks (COFs) with efficient charge transport is of immense interest for applications in optoelectronic devices. To enhance COF charge transport properties, electroactive building blocks and dopants can be used to induce extended conduction channels. However, understanding their intricate interplay remains challenging. We designed and synthesized a tailor‐made COF structure with electroactive hexaazatriphenylene (HAT) core units and planar dioxin (D) linkages, denoted as HD‐COF. With the support of theoretical calculations, we found that the HAT units in the HD‐COF induce strong, eclipsed π–π stacking. The unique stacking of HAT units and the weak in‐plane conjugation of dioxin linkages leads to efficient anisotropic charge transport. We fabricated HD‐COF films to minimize the grain boundary effect of bulk COFs, which resulted in enhanced conductivity. As a result, the HD‐COF films showed an electrical conductivity as high as 1.25 S cm −1 after doping with tris(4‐bromophenyl)ammoniumyl hexachloroantimonate.

Kim, Seong‐Wook↗
DOE OSTI2023

Self‐Propelling Macroscale Sheets Powered by Enzyme Pumps

Abstract Nanoscale enzymes anchored to surfaces act as chemical pumps by converting chemical energy released from enzymatic reactions into spontaneous fluid flow that propels entrained nano‐ and microparticles. Enzymatic pumps are biocompatible, highly selective, and display unique substrate specificity. Utilizing these pumps to trigger self‐propelled motion on the macroscale has, however, constituted a significant challenge and thus prevented their adaptation in macroscopic fluidic devices and soft robotics. Using experiments and simulations, we herein show that enzymatic pumps can drive centimeter‐scale polymer sheets along directed linear paths and rotational trajectories. In these studies, the sheets are confined to the air/water interface. With the addition of appropriate substrate, the asymmetric enzymatic coating on the sheets induces chemically driven, buoyancy flows that controllably propel the sheet's motion on the air/water interface. The directionality and speed of the motion can be tailored by changing the pattern of the enzymatic coating, type of enzyme, and nature and concentration of the substrate. This work highlights the utility of biocompatible enzymes for generating motion in macroscale fluidic devices and robotics and indicates their potential utility for in vivo applications.

Song, Jiaqi↗