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Results for “emerging technologies”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Demand reduction and energy saving potential of thermal energy storage integrated heat pumps [Réduction de la demande et potentiel d'économie d'énergie des pompes à chaleur avec stockage d'énergie thermique intégré]

A heat pump (HP) moves heat from a low-temperature source to a high-temperature sink with an input of energy. Often, one temperature body fluctuates with time (e.g., diurnal ambient temperature), causing the HP efficiency to vary. Integrating thermal energy storage (TES) into a HP system adds a third temperature body, enabling the HP to be advantageously coupled to any two: the application, the ambient, or the TES at strategic times. Although TES integration with HPs is an important emerging technology to lower energy consumption and decrease energy demand during critical times, the favorable circumstances for TES integration are poorly understood. Here, this paper establishes the energy reduction and demand reduction potential of TES-integrated HPs with both analytical and numerical HP models. All possible temperature arrangements are considered for HP-TES systems with two fixed temperature bodies (application and TES) and one variable temperature (ambient). Results show that overall energy savings are most attainable when the TES temperature is near the application temperature, whereas a large temperature difference between the TES and the application leads to the most peak demand reduction. The potential for overall energy savings increases as the magnitude of ambient temperature fluctuations increases.

25 ENERGY STORAGE↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ionic complexation of endblock-sulfonated thermoplastic elastomers and their physical gels for improved thermomechanical performance

Thermoplastic elastomers (TPEs) composed of nonpolar triblock copolymers constitute a broadly important class of (re)processable network-forming macromolecules employed in ubiquitous commercial applications. Physical gelation of these materials in the presence of a low-volatility oil that is midblock-selective yields tunably soft TPE gels (TPEGs) that are suitable for emergent technologies ranging from electroactive, phase-change and shape-memory responsive media to patternable soft substrates for flexible electronics and microfluidics. Many of the high-volume TPEs used for these purposes possess styrenic endblocks that are inherently limited by a relatively low glass transition temperature. To mitigate this shortcoming, we sulfonate and subsequently complex (and physically crosslink) the endblocks with trivalent Al3+ ions. Doing so reduces the effective hydrophilicity of the sulfonated endblocks, as evidenced by water uptake measurements, while concurrently enhancing the thermomechanical stability of the corresponding TPEGs. Chemical modification results, as well as morphological and property development, are investigated as functions of the degree of sulfonation, complexation and TPEG composition. (C) 2020 Published by Elsevier Inc.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sustainable bioleaching of lithium-ion batteries for critical materials recovery

The demand for lithium-ion batteries (LIBs) has increased substantially over the last few decades due to their longer lifetime, greater resistance to self-discharge, and higher output voltage compared to other battery types. With the global trend of electrifying vehicle fleets, the number of LIBs reaching their end-of-life (EOL) is expected to grow substantially in the next decade. These EOL LIBs represent a significant secondary source of materials (e.g., Li, Co, Ni, Mn) that can be recovered and reused in LIBs or other products. In this study, we developed a bioleaching process that could recover critical materials from EOL LIBs in an economical and environmentally sustainable manner under industrially relevant conditions. Black mass, i.e., cathode-containing powder, prepared from EOL LIBs was leached using a biolixiviant produced from corn stover by Gluconobacter oxydans bacteria. Iron(II) was used as a reducing agent to promote metal dissolution. Techno-economic analysis (TEA) estimated a potential average profit margin of 21% for processing 10,000 t of black mass per year, which represents approximately 30% of the available black mass in the US in 2020. Life cycle assessment (LCA) demonstrated that bioleaching of spent LIBs could be more environmentally sustainable than alternative hydrometallurgical recovery methods such as hydrochloric acid leaching (16-19 kg vs. 43-91 kg CO 2 equivalent global warming potential per kg of recovered cobalt). The TEA results are highly dependent on the cost of black mass production, which varies by EOL LIB collection and transportation costs. Finally, emerging technologies for deactivating used LIBs for fire safety at collection centers will allow the transport of EOL LIBs as non-hazardous materials, lower the cost of preparing black mass and thereby increase economic prospects for EOL LIBs recycling using this approach.

25 ENERGY STORAGE↗
DOE OSTI2022

Bimetallic NiCo boride nanoparticles confined in a MXene network enable efficient ambient ammonia electrosynthesis

Ambient electrocatalytic nitrogen fixation is an emerging technology for green ammonia synthesis, but the absence of optimized, stable and performant catalysts can render its practical application challenging. Herein, bimetallic NiCo boride nanoparticles confined in MXene are shown to accomplish high-performance nitrogen reduction electrolysis. Taking advantage of the synergistic effect in specific compositions with unique electronic d and p orbits and typical architecture of rich nanosized particles embedded in the interconnected conductive network, the synthesized MXene@NiCoB composite demonstrates extensive improvements in nitrogen molecule chemisorption, active area exposure and charge transport. As a result, optimal NH 3 yield rate of 38.7 μg h -1 mgcat. -1 and Faradaic efficiency of 6.92% are acquired in 0.1 M Na 2 SO 4 electrolyte. Moreover, the great catalytic performance can be almost entirely maintained in the cases of repeatedly-cycled and long-term electrolysis. Theoretical investigations reveal that the nitrogen reduction reaction on MXene@NiCoB catalyst proceeds according to the distal pathway, with a distinctly-reduced energy barrier relative to the Co 2 B counterpart. In conclusion, this work may inspire a new route towards the rational catalyst design for the nitrogen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Capturing the Page curve and entanglement dynamics of black holes in quantum computers

Quantum computers are emerging technologies expected to become important tools for exploring various aspects of fundamental physics in the future. Therefore, we pose the question of whether quantum computers can help us to study the Page curve and the black hole information dynamics, which has been a key focus in fundamental physics. In this regard, we rigorously examine the qubit transport model, a toy qubit model of black hole evaporation on IBM’s superconducting quantum computers, to shed light on this question. Specifically, we implement the quantum simulation of the scrambling dynamics in black holes using an efficient random unitary circuit. Furthermore, we employ the swap-based many-body interference protocol and the randomized measurement protocol to measure the entanglement entropy of Hawking radiation qubits in this model. Finally, by incorporating quantum error mitigation techniques into our challenging implementation of entanglement entropy measurement protocols on the IBM quantum hardware, we accurately determine the Rényi entropy in the qubit transport model, thus showcasing the utility of quantum computers for future investigations of complex quantum systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2023

Electrochemical leaching of critical materials from lithium-ion batteries: A comparative life cycle assessment

The manufacturing of lithium-ion batteries (LIB) requires critical materials such as cobalt (Co) and lithium (Li) that are essential for clean-energy products including electric vehicles. Because of their rapidly increasing demand and limited supply, the recycle and reuse of these materials from end-of-life LIB have garnered a lot of interest. Electrochemical leaching has emerged as a sustainable method to extract critical materials out of LIBs, so life cycle assessment was conducted to compare the environmental impacts with traditional peroxide-based leaching and another emerging technology – SO 2 -based leaching. The results showed that electrochemical leaching reduces the global warming potential (GWP) by 80%-87% compared to peroxide-based leaching due to a lower acid consumption, avoidance of hydrogen peroxide, and regeneration of reducing agent iron (II) sulfate and compares well with SO 2 -based leaching in most impact categories. Furthermore, the analysis suggested renewable energy can further reduce the environment footprint of electrochemical leaching.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Demonstration and characterization of insertable passive thermal switches for dynamic building envelopes

A dynamic building envelope integrated with thermal energy storage, such as phase change material (PCM), is an emerging technology that offers a promising solution to improve the energy efficiency of buildings. This study reports the development of insertable thermal switches, which modulate thermal resistance, thereby making building envelopes dynamic and enhancing the use of free ambient heating and cooling. The reported thermal switches are passive, meaning they work solely based on indoor and outdoor temperatures. A single switch when inserted into 10 × 10-in (0.064-m 2 ) XPS foam board insulation demonstrates effective thermal conductivity of 0.050 W/m-K in the resistive state and 0.285 W/m-K in the conductive state. Thermal switches exhibit an effective switching ratio of 5.7, with no noticeable degradation in performance over 770 cycles. Additionally, when integrated into a wall sample containing a PCM layer, switches significantly reduce the PCM solidification time by 43.2% during the cooling process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. Here, in this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influencing Bonding Interactions of the Neptunyl (V, VI) Cations with Electron-donating and -withdrawing Groups

Neptunium makes up the largest percentage of minor actinides found in spent nuclear fuel, yet separations of this element have proven difficult due to its rich redox chemistry. Developing new reprocessing techniques should rely on understanding how to control the Np oxidation state and its interactions with different ligands. Designing new ligands for separations requires understanding how to properly tune a system toward a desired trait through functionalization. Emerging technologies for minor actinide separations focus on ligands containing carboxylate or pyridine functional groups, which are desirable due to their high degree of functionalization. So we use DFT calculations to study the interactions of carboxylate and polypyridine ligands with the neptunyl cation [Np(V/VI)O2] +/2+ . A systematic study is performed by varying the electronic properties of the carboxylate and polypyridine ligands through the inclusion of different electron-withdrawing and electron-donating R groups. We focus on how these groups can affect geometric properties, electronic structure, and bonding characterization as a function of the metal oxidation state and ligand character and discuss how these factors can play a role in neptunium ligand design principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ion Solvation and Transport in Narrow Carbon Nanotubes: Effects of Polarizability, Cation-π Interaction, and Confinement

Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.1 and 1.5 nm, using a combination of first-principles and classical MD simulations. Our simulations show that the inclusion of both polarization and cation-π interactions is essential for the description of ion solvation under confinement, particularly for large ions with weak hydration energies. Beyond the variation in ion solvation, we find that cation-π interactions can significantly influence the transport properties of ions in CNTs, particularly for KCl, where our simulations point to a strong correlation between ion dehydration and diffusion. Finally, our study highlights the complex interplay between nanoconfinement and specific intermolecular interactions that strongly control the solvation and transport properties of ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Colloidal Stability of PFSA-Ionomer Dispersions. Part I. Single-Ion Electrostatic Interaction Potential Energies

Charged colloidal particles neutralized by a single counterion are increasingly important for many emerging technologies. Attention here is paid specifically to hydrogen fuel cells and water electrolyzers whose catalyst layers are manufactured from a perfluorinated sulfonic acid polymer (PFSA) suspended in aqueous/alcohol solutions. Partially dissolved PFSA aggregates, known collectively as ionomers, are stabilized by the electrostatic repulsion of overlapping diffuse double layers consisting of only protons dissociated from the suspended polymer. We denote such double layers containing no added electrolyte as "single ion". Size-distribution predictions build upon interparticle interaction potential energies from the Derjaguin-Landau-Verwey-Overbeek (DLVO) formalism. However, when only a single counterion is present in solution, classical DLVO electrostatic potential energies no longer apply. Accordingly, here a new formulation is proposed to describe how single-counterion diffuse double layers interact in colloidal suspensions. Part II (Srivastav, H.; Weber, A. Z.; Radke, C. J. Langmuir 2024 DOI: 10.1021/acs.langmuir.3c03904) of this contribution uses the new single-ion interaction energies to predict aggregated size distributions and the resulting solution pH of PFSA in mixtures of n-propanol and water. A single-counterion diffuse layer cannot reach an electrically neutral concentration far from a charged particle. Consequently, nowhere in the dispersion is the solvent neutral, and the diffuse layer emanating from one particle always experiences the presence of other particles (or walls). Thus, in addition to an intervening interparticle repulsive force, a backside osmotic force is always present. With this new construction, we establish that single-ion repulsive pair interaction energies are much larger than those of classical DLVO electrostatic potentials. The proposed single-ion electrostatic pair potential governs dramatic new dispersion behavior, including dispersions that are stable at a low volume fraction but unstable at a high volume fraction and finite volume-fraction dispersions that are unstable with fine particles but stable with coarse particles. Finally, the proposed single-counterion electrostatic pair potential provides a general expression for predicting colloidal behavior for any charged particle dispersion in ionizing solvents with no added electrolyte.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effects of Quantum Dot Loading on the Radioluminescence Efficiency in Quantum-Dot-Embedded Composites

Nanoparticle-embedded plastic scintillators are an emerging technology for fast, large-area, high-resolution radiation detection and imaging. Here, this study investigates the properties of such composites, focusing on the effects of the quantum dot (QD) concentration on the radioluminescence (RL) intensity, spectra, and dynamics. Experiments using CdSe/CdS QDs in a polymer reveal a superlinear increase in RL with the QD concentration despite optical losses from inner filtering and interparticle interactions. When corrected for inner filtering, RL shows a quadratic concentration dependence, consistent with simple analytical models of improving the secondary electron capture. Practically, the benefits of high QD concentrations are muted by optical losses, but the findings apply to other systems with insulating hosts. In addition to manipulating emission for large effective Stokes shifts, future improvements may come from hosts with higher stopping power and better charge transport, which enable more effective funneling of excitations but without concomitant optical losses associated with high nanoparticle concentrations.

Auger recombination↗
DOE OSTI2023

Laser-Induced Trapping of Metastable Amorphous AlO x /C (2.5 < x ≤ 3.5) Nanocomposites: Implications for Use as Solid Phase Gas Generators

The synthesis of kinetically "frozen" metastable nanostructures remains elusive. This limitation has severely restricted the current paradigms in materials discovery. We overcame this challenge by phase-stabilizing unusually hyperoxidized and metastable amorphous aluminum oxide (a-AlO x ; 2.5 < x ≤ 3.5) nanostructures. Specifically, we employed laser ablation synthesis in solution (LASiS) as a one-pot nonequilibrium technique to achieve this pivotal advancement. Structural and compositional characterizations of the as-synthesized material reveal highly disordered a-AlO x nanoparticles that are remarkably stabilized by interfacial monolayers of ordered carbon atoms. Both structure and chemical composition were confirmed with disparate characterization methods at different length scales. The nanoparticles of sizes less than10 nm were stable even at elevated temperatures. Only at temperatures higher than 750 °C do the a-AlO x structures undergo a solid-solid phase transition that culminates in the formation of stable α-Al 2 O 3 while releasing excess trapped gases. In conclusion, such disruptive materials can find applications in emerging technologies seeking metastable phase-change materials for solid phase gas generators, batteries, neuromorphic computing, and energetic additives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Pulsed-Potential Electrolysis Enhances Electrochemical C–N Coupling by Reorienting Interfacial Ions

The electrochemical processing of anthropogenic CO 2 is an emerging technology aimed at utilizing renewable energies to synthesize valuable chemicals. Recently, developments in broadening the scope of the CO 2 reduction reaction (CO 2 RR) by enabling heteroatom coupling have surged with a focus on C–N bond formation. Herein, we investigate the factors that govern the selectivity and activity in synthesizing urea from environmentally malignant chemical feedstocks (CO 2 and NO 3 – ). Through a combination of electrolyte optimization and pulsed potential electrolysis, electrochemical urea production was optimized to a Faradaic efficiency of 60.4% with current densities reaching as high as 310 μA cm –2 . This work was further supported by in situ surface enhanced infrared absorbance spectroscopy that reveals the formation of C–N-related species at low overpotentials. Density functional theory calculations revealed that the reaction progresses between early reduction intermediates for the CO 2 RR and NO 3 RR and offered insights into the impacts of pulsed-potential on substrate transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Inelastic Neutron Scattering Observation of Plasma-Promoted Nitrogen Reduction Intermediates on Ni/γ-Al 2 O 3

Plasma-assisted catalysis is an emerging technology for the atmospheric pressure and low bulk gas temperature synthesis of ammonia from molecular nitrogen and hydrogen. Direct evidence for plasma-induced surface reaction intermediates relevant to ammonia production, including surface hydrides and NH x (x = 1, 2, 3) species, has remained elusive. In this work, we report inelastic neutron scattering (INS) observations of alumina-supported Ni particles after treatment with N 2 and H 2 plasmas. INS experiments reveal the presence of NH x species and hydrides on Ni sites after exposure to sequential N 2 and H 2 plasma treatments. By separating exposure, we exclude the presence of plasma-phase reactions and demonstrate that these species are generated through plasma-facilitated surface reactions. Computed synthetic INS spectra of NH 3 , NH 2 and NH adsorbates on Ni support the experimental assignments of surface intermediates. The results directly implicate plasma stimulation of dinitrogen in generation of surface-bound nitrogen that participates in further hydrogenation reactions driven either thermally or with H 2 plasma.

36 MATERIALS SCIENCE↗