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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The influence of hydrothermal synthesis temperature on the electronic structure and luminescence property of Cr-doped ZnGa 2 O 4 nanoparticles

Cr-doped ZnGa 2 O 4 (CZGO) is a near-infrared-emitting material with long-lasting persistent luminescence. This unique property makes it a great candidate in optical imaging and sensing for various biomedical applications. Nanosized CZGO can be synthesized via a hydrothermal approach, but the reaction temperatures reported in existing literature vary from 120°C to 220°C, approaching the highest temperature allowed for a standard autoclave vessel. The reason for choosing a particular hydrothermal synthesis temperature has rarely been discussed, and it is unclear whether the resulting CZGO possess the same optical properties. This work compares CZGO nanoparticles synthesized at temperatures from the lowest reported 120°C and to as high as 220°C. We find that although all the synthesized CZGO are light emitting, they exhibit different luminescence intensities and respond differently to the change of excitation energy. X-ray absorption fine structure (XAFS) analysis is employed to investigate the local chemical environment around Zn, Ga and Cr, respectively. We found synthesis temperature strongly influences the Ga species formed in these particles. The energy transfer mechanism is further elucidated using X-ray excited optical luminescence (XEOL) in combination with element-specific XAFS. Two energy transfer paths are identified, which explains the different excitation energy dependencies of the observed photoluminescence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Broadband Wide-Angle VElocity Selector (BWAVES) neutron spectrometer designed for the SNS Second Target Station

A recently proposed wide-angle velocity selector (WAVES) device for choosing the velocity of detected neutrons after they have been scattered by the sample paves the way for inverted geometry neutron spectrometers with continuously adjustable final neutron wavelength. BWAVES broadband inverted geometry spectrometer proposed for the Second Target Station at the Spallation Neutron Source at Oak Ridge National Laboratory is designed using WAVES to simultaneously probe dynamic processes spanning 4.5 decades in time (energy transfer). This makes BWAVES a uniquely flexible instrument which can be viewed as either a quasielasitc neutron scattering (QENS) spectrometer with a practically unlimited (overlapping with the vibrational excitations) range of energy transfers, or a broadband inelastic vibrational neutron spectrometer with QENS capabilities, including a range of accessible momentum transfer (Q) and a sufficiently high energy resolution at the elastic line. The new capabilities offered by BWAVES will expand the application of neutron scattering in ways not possible with existing neutron spectrometers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of Random Nanostructured Metallic Environments on Spontaneous Emission of HITC Dye

We have studied emission kinetics of HITC laser dye on top of glass, smooth Au films, and randomly structured porous Au nanofoams. The observed concentration quenching of luminescence of highly concentrated dye on top of glass (energy transfer to acceptors) and the inhibition of the concentration quenching in vicinity of smooth Au films were in accord with our recent findings. Intriguingly, the emission kinetics recorded in different local spots of the Au nanofoam samples had a spread of the decay rates, which was large at low dye concentrations and became narrower with increase of the dye concentration. We infer that in different subvolumes of Au nanofoams, HITC molecules are coupled to the nanofoams weaker or stronger. The inhibition of the concentration quenching in Au nanofoams was stronger than on top of smooth Au films. This was true for all weakly and strongly coupled subvolumes contributing to the spread of the emission kinetics. The experimental observations were explained using theoretical model accounting for change in the Förster radius caused by the strong energy transfer to metal.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Effect of Periodic Spatial Perturbations on the Emission Rates of Quantum Dots near Graphene Platforms

The quenching of fluorescence (FL) at the vicinity of conductive surfaces and, in particular, near a 2-D graphene layer has become an important biochemical sensing tool. The quenching is attributed to fast non-radiative energy transfer between a chromophore (here, a Quantum Dot, QD) and the lossy graphene layer. Increased emission rate is also observed when the QD is coupled to a resonator. Here, we combine the two effects in order to control the emission lifetime of the QD. In our case, the resonator was defined by an array of nano-holes in the oxide substrate underneath a graphene surface guide. At resonance, the surface mode of the emitted radiation is concentrated at the nano-holes. Thus, the radiation of QD at or near the holes is spatially correlated through the hole-array’s symmetry. We demonstrated an emission rate change by more than 50% as the sample was azimuthally rotated with respect to the polarization of the excitation laser. In addition to an electrical control, such control over the emission lifetime could be used to control Resonance Energy Transfer (RET) between two chromophores.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transfer-Learnt Energy Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data

Modeling energy consumption is critical for energy-efficient utilization of the electric appliances in a building, smart grid programs (like demand-response), and many other smart home applications. State-of-the-art energy modeling techniques either rely on theoretical models, or extensive instrumentation of the building envelope to gather ``big" data to train a deep neural network. While theoretical models are often limited by their estimation accuracy, it is not always feasible to gather a significant amount of field data. In this paper, we explore transfer learning-based strategies to train much more accurate model for energy estimation when using a sparse field data. We transferred knowledge, in the form of data and parameters, from the simulation framework to the field data. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that transfer learning-based models trained over one month data can perform comparative (and in some cases better) than the state-of-the-art machine learning and deep learning solutions.

Jain, Milan↗
DOE OSTI2021

Simultaneous velocity and density measurements of fully developed Rayleigh-Taylor mixing

Here, the dynamics of molecular mixing and the energy transfer process in the Rayleigh-Taylor instability (RTI) are studied through the collection of simultaneous density-velocity measurements. These experiments provide simultaneous density-velocity field measurements, in contrast to previous point measurements. Statistically stationary experiments are performed in a “convective-type” gas tunnel facility, with density contrast achieved through the injection of helium into the bottom stream. Three experiments at Atwood number ≈ 0.1 are captured at three outer scale Reynolds numbers Re = 520, 2260, and 4050. Particle image velocimetry and laser induced fluorescence are employed simultaneously. Statistics of the density and velocity show self-similar collapse of RTI profiles at large Reynolds number Re >2000. Flat velocity profiles indicate homogeneous turbulence characteristics in the core of the mixing region. Significant anisotropy develops in the flow, with horizontal velocity fluctuations being only 60% of the vertical velocity fluctuations. The turbulent mass flux is found to be asymmetric about the centerline, with increased peak towards the spike. Measurements of the molecular mixing show that mixing is maximized at the core of the flow and increases with increased Reynolds number. The transport equation of density-specific-volume correlation b shows that it is mostly produced in the core of the mixing region, and that the spatial evolution of its profile is the result of transport by bulk motion of the bubble and spike. Energy transfer from gravitational potential energy to turbulent kinetic energy and viscous dissipation is observed to occur in the experiment with a ratio of dissipated energy to potential energy released of 38%. The analysis of the turbulent kinetic energy transport equation budget reveals that production is the primary mechanism towards the growth of turbulent kinetic energy in the core of the flow, and is asymmetrically slightly skewed towards the spike. However, it is through the transport that the strong advection at the edges of the mixing region is maintained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗
DOE OSTI2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An Ab Initio Multiple Cloning Method for Non-Adiabatic Excited-State Molecular Dynamics in NWChem

The recently developed ab-initio multiple cloning (AIMC) approach based on the multicongurational Ehrenfest (MCE) method provides a powerful and accurate way of describing the excited-state dynamics of molecular systems. The AIMC method is a controlled approximation to non-adiabatic dynamics with a particular strength in the proper description of decoherence effects due to branching of vibrational wavepackets at a level crossing. In this work, we report a new implementation of the AIMC algorithm in the open source NWChem computational chemistry program. The framework combines linear-response time-dependent density functional theory with Ehrenfest mean-eld theory to determine the equations of motion for classical trajectories. The multi-dimensional wave function is decomposed into a superposition of Gaussian coherent states guided by Ehrenfest trajectories (i.e., MCE approach), which can clone with fully quantum mechanical amplitudes and phases. By using an efficient time-derivative based non-adiabatic coupling approach within the AIMC method, all observable are calculated on-the-y in the non-adiabatic molecular dynamics process. As a representative example, we apply our implementation to study the ultrafast photoinduced electronic and vibrational energy transfer in a pyridine molecule. The effects of the cloning procedure on electronic and vibrational coherence, relaxation and unidirectional energy transfer are discussed. This new AIMC implementation provides a high-level non-adiabatic molecular dynamics framework for simulating photoexcited dynamics in complex molecular systems and experimentally relevant ultrafast spectroscopic probes, such as nonlinear coherent optical and X-ray signals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Frequency-Domain Spectroscopic Study of the Photosystem I Supercomplexes, Isolated IsiA Monomers, and the Intact IsiA Ring

The PSI 3 -IsiA 18 supercomplex is one of the largest and most complicated assemblies in photosynthesis. The IsiA ring, composed of 18 IsiA monomers (IsiA 18 ) surrounding the PSI trimer (PSI 3 ), forms under iron-deficient condition in cyanobacteria and acts as a peripheral antenna. Based on the supercomplex structure recently determined via cryo-EM imaging, we model various optical spectra of the IsiA monomers and IsiA 18 ring. Comparison of the absorption and emission spectra of the isolated IsiA monomers and the full ring reveals that about 2.7 chlorophylls (Chls) are lost in the isolated IsiA monomers. The best fits for isolated monomers spectra are obtained assuming the absence of Chl 508, Chl 517 and 70% loss of Chl 511. The best model describing all three hexamers and the entire ring suggests that the lowest energy pigments are Chls 511, 514 and 517. Based on the modeling results presented in this work we conclude that there are most likely three entry points for EET from the IsiA6 hexamer to the PSI core monomer, with two of these entry points likely being located next to each other (i.e. nine entry points from IsiA 18 to the PSI 3 trimer). Lastly, we show that excitation energy transfer inside individual monomers is fast (< 2 ps at T = 5 K) and at least 20 times faster than inter-monomer energy transfer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rate coefficients for rotational state-to-state transitions in H 2 O + H 2 O collisions for cometary and planetary applications, as predicted by mixed quantum-classical theory

We present new calculations of collision cross sections for state-to-state transitions between the rotational states in an H 2 O + H 2 O system, which are used to generate a new database of collisional rate coefficients for cometary and planetary applications. Calculations were carried out using a mixed quantum-classical theory approach that is implemented in the code MQCT. The large basis set of rotational states used in these calculations permits us to predict thermally averaged cross sections for 441 transitions in para- and ortho-H 2 O in a broad range of temperatures. It is found that all state-to-state transitions in the H 2 O + H 2 O system split into two well-defined groups, one with higher cross-section values and lower energy transfer, which corresponds to the dipole-dipole driven processes. The other group has smaller cross sections and higher energy transfer, driven by higher-order interaction terms. We present a detailed analysis of the theoretical error bars, and we symmetrized the state-to-state transition matrixes to ensure that excitation and quenching processes for each transition satisfy the principle of microscopic reversibility. We also compare our results with other data available from the literature for H 2 O + H 2 O collisions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Localizing Low-Grade Heat Using Hybrid Photonic-Phononic Materials

Creating next-generation devices capable of transducing waste heat into useful work will require building blocks designed to direct low-grade thermal energy at length scales well below the diffraction limit. Characterizing thermally induced energy transfer at the nanoscale, however, is a formidable task due to the simultaneous demand of high spatial and spectral resolution at infrared energies. Leveraging recent advancements in electron energy loss (EEL) and gain (EEG) spectroscopy, we reveal that individual nanostructures can concentrate energy by the resonant thermal excitation of their photonic Fabry–Pérot modes. Specifically, we show that the spatially localized gain signal increases upon in situ heating. Finally, theoretical modeling elucidates the mechanism for these observations by showing that a Purcell enhancement in the local photonic density of states drives the increased rate of infrared energy transfer from the ambient environment to each nanostructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effect of polymer structure and material properties on mechanochemical reaction environments for polymer recycling

Plastic waste accumulation necessitates innovative recycling approaches to achieve sustainability goals. Mechanochemical depolymerization offers a solvent-free, energy-efficient route to convert polymers into valuable monomers. In addition to their chemical properties, the way that polymers absorb kinetic energy is a key parameter of any mechanochemical process. This perspective explores the principles underpinning mechanochemical recycling, emphasizing how deformation and localized transient heating mediate energy transfer between impacts and localized excitations. Key factors such as polymer crystallinity, molecular weight, viscoelasticity, and thermal effects are analyzed to elucidate their role in energy transfer mechanisms during ball milling. This work establishes a foundational framework for the design and optimization of mechanochemical recycling by connecting polymer response to mechanical energy with the intention to improve depolymerization efficiency. Future research opportunities are outlined to advance the integration of polymer science and mechanochemistry for scalable, sustainable plastic upcycling.

ball mill↗
DOE OSTI2020

An extended methodology for automated calculations of non-Boltzmann kinetic sequences: H + C 2 H 2 + X and combustion impact

It is generally assumed in phenomenological kinetic models that bimolecular reactions only occur between species whose rovibrational energy follows a Boltzmann (thermal) distribution. That is, any complexes initially formed in non-Boltzmann distributions are assumed to be thermalized by energy-transferring collisions prior to bimolecular reactions. Given the high mole fractions of reactive species, X, in combustion environments, reactive collisions of the complexes with X often occur on the same timescale as energy-transferring collisions – yielding sequences proceeding through non-Boltzmann intermediates across multiple potential energy surfaces. Recent studies have shown that such non-Boltzmann kinetic sequences can have substantial impact on the global reactivity in combustion systems. Simulations of these non-Boltzmann reaction sequences, which can be described in phenomenological kinetic models via chemically termolecular reactions, require that rovibrational excitation from one potential energy surface be carried over to the next. Here this paper presents an extended theoretical and computational methodology that couples multiple master equations and derives rate constants for phenomenological reactions describing the conversion of thermal reactants to thermal products for use in phenomenological kinetic schemes. The methodology is then implemented using in-house scripts for non-Boltzmann sequences involving C2H$^*_3$ + X (with X = O 2 , H, and OH) where C 2 H$^*_3$ is formed via H + C 2 H 2 association – which were identified as having strong potential for influencing combustion predictions in a recent study. The results reveal that non-Boltzmann reaction sequences for X = O 2 (the primary focus of this paper) significantly alters the total conversion rate from H + C 2 H 2 to products and product branching fractions from those of thermal sequential pathways. Furthermore, the present results demonstrate that non-Boltzmann reaction sequences have significant impact – as high as an order of magnitude – on predicted ignition delay times. Similarly, they yield significantly different dependence of ignition delay times with temperature and O 2 mole fraction – yielding signatures that are likely observable experimentally.

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

Biomimetic Light Harvesting Complexes Based on Self-Assembled Dye-DNA Nanostructures (Final Technical Report)

A fundamental limitation in the development of artificial molecular light harvesting structures is that generally if one places pigment molecules close enough to achieve very high energy transfer efficiency and absorbance cross section, the dyes interact in such a way that self-quenching occurs. This limits both the optical cross section and the overall efficiency of light capture in such systems. Nature overcomes this problem and achieves efficient energy transfer without self-quenching by controlling the environment, vibration characteristics, orientation and dynamics of the pigments involved in light harvesting. Natural light harvesting systems have evolved highly intricate chromophore architectures of protein-bound organic molecules that absorb and funnel solar energy with high efficiency and fast transfer rates using elegant excitonic circuitries. Nature achieves this precise control over the properties of pigment complexes by using specific proteins as templates to guide the formation of very well-defined pigment aggregates, groups of pigment molecules that are in close enough contact to undergo excitonic interactions resulting in new electronic properties that were not present in the monomer. In synthetic systems, pigment aggregates are well known, and J-aggregates and H-aggregates have been long studied for their unique properties. In Nature, the chlorosomes of green bacteria form what appear to be classical J-aggregates and the light harvesting complexes of purple nonsulfur bacteria form ring structures that have many of the properties of J-aggregates such as a red-shifted absorbance spectrum and excited states that are delocalized over multiple pigment molecules. These photosynthetic systems leverage the spectral and enhanced transport properties of J-like aggregates to achieve fast exciton transport within the antenna and thus high light-harvesting efficiency. The close interaction of the pigments results in substantial electronic coupling that lifts the degeneracy of excitonic states and therefore modifies the absorption and emission spectra of the monomer. In the case of J-aggregation, lower energy excitonic states acquire significant oscillator strength, resulting in a characteristic bathochromic shift of the absorption and fluorescence and often superradiant delocalized exciton states.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optimization of transformer ratio and beam loading in a plasma wakefield accelerator with a structure-exploiting algorithm

Plasma-based acceleration has emerged as a promising candidate as an accelerator technology for a future linear collider or a next-generation light source. We consider the plasma wakefield accelerator (PWFA) concept where a plasma wave wake is excited by a particle beam and a trailing beam surfs on the wake. For a linear collider, the energy transfer efficiency from the drive beam to the wake and from the wake to the trailing beam must be large, while the emittance and energy spread of the trailing bunch must be preserved. One way to simultaneously achieve this when accelerating electrons is to use longitudinally shaped bunches and nonlinear wakes. In the linear regime, there is an analytical formalism to obtain the optimal shapes. In the nonlinear regime, however, the optimal shape of the driver to maximize the energy transfer efficiency cannot be precisely obtained because currently no theory describes the wake structure and excitation process for all degrees of nonlinearity. In addition, the ion channel radius is not well defined at the front of the wake where the plasma electrons are not fully blown out by the drive beam. We present results using a novel optimization method to effectively determine a current profile for the drive and trailing beam in PWFA that provides low energy spread, low emittance, and high acceleration efficiency. We parameterize the longitudinal beam current profile as a piecewise-linear function and define optimization objectives. For the trailing beam, the algorithm converges quickly to a nearly inverse trapezoidal trailing beam current profile similar to that predicted by the ultrarelativistic limit of the nonlinear wakefield theory. For the drive beam, the beam profile found by the optimization in the nonlinear regime that maximizes the transformer ratio also resembles that predicted by linear theory. Furthermore, the current profiles found from the optimization method provide higher transformer ratios compared with the linear ramp predicted by the relativistic limit of the nonlinear theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗