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DOE OSTI2023

Chemical Recycling of Thiol Epoxy Thermosets via Light-Driven C–C Bond Cleavage

Epoxy thermosets are high-volume materials that play a central role in a wide range of engineering applications; however, technologies to recycle these polymers remain rare. Here, we present a catalytic, light-driven method that enables chemical recycling of industrially relevant thiol epoxy thermosets to their original monomer at ambient temperature. This strategy relies on the proton-coupled electron transfer (PCET) activation of hydroxy groups within the polymer network to generate key alkoxy radicals that promote the fragmentation of the polymer through C–C bond β-scission. The method fully depolymerizes insoluble thiol epoxy thermosets into well-defined mixtures of small-molecule products, which can collectively be converted into the original monomer via a one-step dealkylation process. Notably, this process is selective and efficient even in the presence of other commodity plastics and additives commonly found in commercial applications. Finally, these results constitute an important step toward making epoxy thermosets recyclable and more generally exemplify the potential of PCET to offer a more sustainable end-of-life for a diverse array of commercial plastics.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Evaluation of historical precipitation interannual variability in CMIP6 over the United States

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Efficient Sampling of Complex Interdependent and Multiplex Networks

Efficient sampling of interdependent and multiplex infrastructure networks is critical for effectively applying failure and recovery algorithms in real-world settings, as well as to generate property-preserving reduced-order graph-based ensembles that address topological uncertainties. In this paper, we first explore the performance, i.e. the success in preserving graph properties, of graph sampling algorithms for interdependent and multiplex networks with synthetic and real-world graphs. We simulate sampling algorithms under different parameter settings. These settings include probabilistic graph generators, coupling patterns, and various performance metrics. Our results show that while Random Node and Random Walk sampling algorithms perform best for interdependent networks, Random Edge and Forest Fire sampling algorithms perform best for multiplex networks. Second, we propose and implement a novel similarity-based sampling algorithm for multiplex networks that samples only log(N) number of layers of an N-layer multiplex network while yielding computational savings with performance guarantees. Experimental results show that similarity sampling outperforms complete sampling of all layers while decreasing performance costs from a linear scale to a logarithmic one. Our results also indicate that similarity-based sampling outperforms complete sampling and random selection in nearly all scenarios when tested with real-world data.

Subasi, Omer↗

DOE OSTI2021

The Sensitivity Analysis of Coil Misalignment for a 200-kW Dynamic Wireless Power Transfer System with an LCC-S and LCC-P Compensation

The dynamic wireless power transfer systems may reduce the battery size of electric vehicles while maintaining the travel range. Similar to the stationary wireless power transfer systems, the compensation networks are desired in dynamic wireless charging systems at the primary and secondary sides to reduce the reactive power requirement from the source and improve the efficiency. Consequently, it is essential to understand the behavior of the compensation networks, especially the sensitivity to misalignments. This paper presents a sensitivity analysis of LCC-S and LCC-P compensation networks for a 200-kW power transfer with variations in the coupling coefficient due to the electric vehicle travel and misalignments between the pads. The sensitivity study combines the electromagnetic finite element analysis and circuit analysis to determine the impact of misalignments on the wireless power transfer system characteristics. The theoretical analysis is verified by conducting circuit simulations at discrete points to verify the accuracy of the mathematical model.

Kavimandan, Utkarsh↗

DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗

DOE OSTI2024

VOTCA: multiscale frameworks for quantum and classical simulations in soft matter

Many physical phenomena in liquids and soft matter are multiscale by nature and can involve processes with quantum and classical degrees of freedom occurring over a vast range of length- and timescales. Examples range from structure formation processes of complex polymers or even polymer blends (Svaneborg & Everaers, 2023) on the classical side to charge and energy transport and conversion processes (Lee et al., 2019) involving explicit electronic and, therefore, quantum information. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides multiscale frameworks built on a comprehensive set of methods for the development of classical coarse-grained potentials (VOTCA-CSG) as well as state-of-the art excited state electronic structure methods based on density-functional and many-body Green’s function theories, coupled in mixed quantum-classical models and used in kinetic network models (VOTCA-XTP).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

Novel low profile antenna candidates for EHF portable terminals

This paper presents three low profile antenna candidates for EHF portable communication terminals. The first structure is a planar, multilayer microstrip antenna utilizing electromagnetic coupling to minimize the complexity and losses associated with the feed network. The second candidate is a medium gain (15 dB) radiating cavity antenna utilizing a thick metallic ground plane. This element is amenable to device integration because a heat sink is incorporated and an area is available for fastening modular RF components. Thirdly, as an alternative to microstrip antennas, dielectric resonator antennas (DRA's) are presented. A broadband (28 percent) DRA is discussed. Experimental measurements are presented for all three antenna candidates.

D. Roscoe↗

NASA NTRS1999

Tensegrity and mechanoregulation: from skeleton to cytoskeleton

OBJECTIVE: To elucidate how mechanical stresses that are applied to the whole organism are transmitted to individual cells and transduced into a biochemical response. DESIGN: In this article, we describe fundamental design principles that are used to stabilize the musculoskeletal system at many different size scales and show that these design features are embodied in one particular form of architecture that is known as tensegrity. RESULTS: Tensegrity structures are characterized by use of continuous tension and local compression; architecture, prestress (internal stress prior to application of external force), and triangulation play the most critical roles in terms of determining their mechanical stability. In living organisms, use of a hierarchy of tensegrity networks both optimizes structural efficiency and provides a mechanism to mechanically couple the parts with the whole: mechanical stresses applied at the macroscale result in structural rearrangements at the cell and molecular level. CONCLUSION: Due to use of tensegrity architecture, mechanical stress is concentrated and focused on signal transducing molecules that physically associate with cell surface molecules that anchor cells to extracellular matrix, such as integrins, and with load-bearing elements within the internal cytoskeleton and nucleus. Mechanochemical transduction may then proceed through local stress-dependent changes in molecular mechanics, thermodynamics, and kinetics within the cell. In this manner, the entire cellular response to stress may be orchestrated and tuned by altering the prestress in the cell, just as changing muscular tone can alter mechanical stability and structural coordination throughout the whole musculoskeletal system.

NASA Discipline Cell Biology↗

NASA NTRS2018

High/Low Temperature Contactless Radio Frequency Probes

A contactless radio frequency (RF) probe with inverted microstrip lines. The RF probe includes a microstrip associated with a device under test, and a broadside coupled inverted microstrip line configured to exchange RF signals to a network analyzer.

Jordan, Jennifer L.↗

DOE OSTI2019

Laser Capture Microdissection–Liquid Vortex Capture Mass Spectrometry Metabolic Profiling of Single Onion Epidermis and Microalgae Cells

Laser capture microdissection is a valuable technique in individually isolating single cells whether in tissue networks or deposited from a cell suspension. New developments have enabled coupling of laser capture microdissection with mass spectrometry via liquid vortex capture sampling probe. This enables online metabolic profiling of sectioned cells. Here, we describe the protocol used to deposit, isolate, and individually chemically characterize single Allium cepa and Chlamydomonas reinhardtii cells by laser capture microdissection–liquid vortex capture mass spectrometry.

Cahill, John F.↗

DOE OSTI2021

Shared aperture antenna array

A shared aperture antenna array including an array of antennas is disclosed. Elements of neighboring antennas are shared to create additional antennas. The shared elements include radiating patches and apertures. Each antenna shares an aperture with neighboring antennas. The array of antennas may be linear or two-dimensional. A phase shifting network with single-pole-single-throw reflective switches may be coupled to the antennas.

Tedeschi, Jeffrey R.↗

DOE OSTI2024

Shared aperture antenna array

Tedeschi, Jonathan R.↗

NASA NTRS1980

Theory of electromagnetic transmission structures. I - Relativistic foundation and network formalisms

A new theorem on a class of four-dimensional skew-symmetric tensors is demonstrated. Coupled with the relativistic covariant form of Maxwell's equations, this theorem consolidates the classifications of guided waves by combining the three types - TE, TM, TEM - under a uniform condition applied to the generating four-potential which is Lorentz invariant. Each type corresponds to a potential of which a pair of the four components vanishes in a particular frame. Through appropriate normalization conditions, the resulting time-domain equations for the field amplitudes are readily reduced to modified telegraphist equations, which in turn lead to distributed network representations for each of the three types. The ambiguity of distributed network formalisms in general is elucidated and the concept of network parameter densities such as traditionally employed in TEM transmission line theory is questioned.

Gabriel, G. J.↗

DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Power matching to pulsed inductively coupled plasmas

Matching of power delivery to nonlinear loads in plasma processing is a continuing challenge. Plasma reactors used in microelectronics fabrication are increasingly multi-frequency and/or pulsed, producing a non-linear and, in many cases, non-steady state electrical termination that can complicate efficient power coupling to the plasma. This is particularly the case for pulsed inductively coupled plasmas where the impedance of the plasma can significantly change during the start-up-transient and undergo an E–H (capacitive-to-inductive) transition. In this paper, we discuss the results from a computational investigation of the dynamics of power matching to pulsed inductively coupled plasmas (Ar/Cl 2 mixtures of tens of mTorr pressure) using fixed component impedance matching networks and their consequences on plasma properties. In this investigation, we used set-point matching where the components of the matching network provide a best-case impedance match (relative to the characteristic impedance of the power supply) at a chosen time during the pulsed cycle. Matching impedance early during the pulse enables power to feed the E-mode, thereby emphasizing capacitive coupling and large excursions in the plasma potential. This early power coupling enables a more rapid ramp-up in plasma density while being mismatched during the H-mode later in the pulse. The early match also produces more energetic ion bombardment of surfaces. Matching late in the pulse diminishes power dissipated in the E-mode at the cost of also reducing the rate of increase in plasma density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue

In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D–0D–3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D–0D–3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.

multiscale blood flow models↗

DOE OSTI2023

Roles of Hydrogen Bonds and Alignment in Oriented Attachment of Gibbsite Nanoparticles: Insights from Molecular Dynamics

Oriented attachment (OA) of nanoparticles is an important crystal growth pathway in the synthesis of hierarchical structures. Although a significant understanding of OA has been made, the effect of atomistic misalignment and the roles of solvent/particle and particle/particle interactions on the structure–energy relationship during an OA remain elusive. In this study, we perform molecular dynamics simulations to calculate the potential of mean force (PMF) profile for gibbsite particle translation on a gibbsite slab with 1 or 2 intervening water layers (1W or 2W). The structures of the gibbsite surfaces and the confined water are analyzed to determine how the number and type of hydrogen bonds (H-bonds) influence the free energy profile during the translation. The PMF profile exhibits a periodicity of length 5.078 Å, consistent with the gibbsite unit cell size along the translation direction. The changes in the surface–water and water–water hydrogen bond network and water and surface OH groups’ orientations during the translation are strongly coupled with the changes in the PMF profile in the 1W case. However, when increasing the number of intervening water layers from 1W to 2W, the particle/slab misalignment becomes a dominant factor controlling the behavior of the PMF profile. We also establish a method to quantify misalignment between the particle and the slab, which exhibits a strong correlation with the free energy for the 2W case. These results shed more light into the roles of particle/slab misalignment and hydrogen bond network in the OA of mineral particles in aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Resonant amplification of enzymatic chemical oscillations by oscillating flow

Using theory and simulation, we analyzed the resonant amplification of chemical oscillations that occur due to externally imposed oscillatory fluid flows. The chemical reactions are promoted by two enzyme-coated patches located sequentially on the inner surface of a pipe that transports the enclosed chemical solution. In the case of diffusion-limited systems, the period of oscillations in chemical reaction networks is determined by the rate of the chemical transport, which is diffusive in nature and, therefore, can be effectively accelerated by the imposed fluid flows. In this work, we first identify the natural frequencies of the chemical oscillations in the unperturbed reaction–diffusion system and, then, use the frequencies as a forcing input to drive the system to resonance. We demonstrate that flow-induced resonance can be used to amplify the amplitude of the chemical oscillations and to synchronize their frequency to the external forcing. In particular, we show that even 10% perturbations in the flow velocities can double the amplitude of the resulting chemical oscillations. Particularly, effective control can be achieved for the two-step chemical reactions where during the first half-period, the fluid flow accelerates the chemical flux toward the second catalytic patch, while during the second half-period, the flow amplifies the flux to the first patch. The results can provide design rules for regulating the dynamics of coupled reaction–diffusion processes and can facilitate the development of chemical reaction networks that act as chemical clocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗