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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 19 records

The influence of protein electrostatics on potential inversion in flavoproteins

Biology uses relatively few electron-transfer cofactors, tuning their potentials, electronic couplings, and reorganization energies to carry out the required chemistry. It is remarkable that the potential ordering of two-electron transfer active flavins can be normal (first oxidation at low potential and second oxidation at high potential) or inverted, and the gap between the potentials can be as large as one volt. Analysis based on structural bioinformatics and electrostatics indicates that the ordering of the flavin redox potential is influenced by protein electrostatics. In all 36 flavoproteins examined, the introduction of a negative charge near the flavin in silico increases the extent of potential inversion (by lowering the electrochemical potential of the second electron-transfer step); the introduction of a positive charge near the flavin favors normally ordered potentials. We also find that the addition of positive charges increases the electrochemical potential for the naturally occurring one-electron transition in flavodoxins (between deprotonated hydroquinone and neutral semiquinone) and also increases the second one-electron transition in bifurcating flavins (between anionic semiquinone and fully oxidized flavin). Finally, we find that proximity of a proton acceptor, notably conserved arginine, supports proton-coupled electron transfer because it may act as a proton acceptor, promoting potential inversion. This key arginine residue may enable two-electron transfer chemistry by promoting the proton-coupled electron transfer process over the pure electron transfer process, suggesting how a protein's flavin environment may influence one- or two-electron chemistry in flavoproteins.

Singh, Niven [Duke Univ., Durham, NC (United State↗

ATR-SEIRAS Reveals Potential Inversion and Associated Electron Transfer Kinetics in the Reduction of Surface-Confined Anthraquinone

The detection of stable semiquinone radicals on an anthraquinone (AQ) layer chemically grafted to an electrode surface in aqueous electrolytes has been elucidated by using attenuated total reflection surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). In very alkaline conditions (pH 13), the reduction of the AQ involves no proton transfer, but surface sensitive infrared spectroscopy reveals that the anthraquinone dianion forms a strong hydrogen bonding network with coadsorbed water, leading to irreversible features in the voltammetry. The potential dependence of the IR band assigned to the AQ radical is consistent with the enhanced hydrogen bonding network causing increased stabilization of the quinone radical and supports the predicted response of a system under mild potential inversion, whereby the formal potential for the reduction of the anthraquinone radical is positive of the reduction potential of the neutral AQ molecule. Time-resolved ATR-SEIRAS is used to measure the transient formation of the AQ •– radical, from which rate constant information can be extracted using the Butler–Volmer model involving two one-electron transfers without a direct disproportionation reaction. The potential dependence of the rate constants is consistent with the potential inversion and can be used to qualitatively simulate the measured cyclic voltammograms. In conclusion, the thermodynamic and kinetic analyses re-emphasize long established deficiencies associated with using one-electron reaction formalisms to characterize multi-electron systems.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

Excited electronic states of Na 2 and K 2 : The potential for long-lived “reservoir” states leading to collision induced population inversions

Potential energy curves (PECs) for the spin-free (ΛS) and spin–orbit (Ω) states associated with the four lowest-lying dissociation channels of Na 2 and K 2 were calculated at the SA-CASSCF/SO-CASPT2/aug-cc-pwCVQZ-DK level. The PECs of Na 2 were consistent with the experimental data and with the FS-CCSD (2,0) calculations, reproducing the double-well and the “shelf” character for some of the potentials of the excited states. For K 2 , the PECs behaved in a similar way and the spectroscopic parameters for the ground and the excited states are in good agreement with the available experimental values. The dissociation energy of K 2 was predicted to be D e = 4454 cm −1 , within an agreement of 5 cm −1 with the experiments. For Na 2 , D e = 5789 cm −1 compared to the experimental value of 6022 cm −1 . The inclusion of spin–orbit coupling effects resulted in avoided crossings, which affect the PECs. Spin–orbit changes the predicted curves for some excited Ω states arising from ΛS states that overlap each other, affecting their associated vibrational frequencies and bond distances. Here, the current studies of the low-lying states in K 2 reveal a similar structure to those of Na 2 , which suggests the accessibility of long-lived energy storing reservoir states and possible population inversions in K 2 following prior experimental work on the reaction of halogen atoms with Na 3 to produce excited states of Na 2 .

Ab-initio methods↗

Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning

In this article, an innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

36 MATERIALS SCIENCE↗

Inferring colloidal interaction from scattering by machine learning

A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which extracts the targeted static two-point correlation functions from experimentally measured scattering cross sections, and a Gaussian process framework which probabilistically infers the relevant structural parameters from the inverted correlation functions. Via a case study of charged colloidal suspensions, the feasibility of this approach for quantitative study of molecular interaction is critically benchmarked and its merit over existing deterministic approaches, in terms of numerical accuracy and computationally efficiency, is demonstrated.

36 MATERIALS SCIENCE↗

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗

Electron Inversion and Tunneling at Silicon Thermal Oxide Interfaces for Solar-Driven Molecular Catalysis to Syngas

Semiconductor photoelectrodes are regularly coupled to solid-state heterogeneous catalysts to perform solar-driven reduction of CO 2 . Less frequently, molecular catalysts are employed to better control the reactivity toward desired products, yet the development of robust semiconductor/molecule interfaces has proven challenging. Here, we demonstrate that a 2–3 nm thermal oxide layer on Si exhibits stability in aqueous solution, high photovoltage, and a photocurrent density of ∼10 mA/cm 2 for the solar-driven photoelectrochemical reduction of a homogeneous molecular catalyst, producing syngas with an ∼2:1 H 2 to CO ratio. Because of a low defect density, the oxide interface forms an electron inversion layer with metal-like electron density at cathodic potentials. This inversion layer facilitates electron transfer to redox-active molecules via tunneling even if the molecule’s reduction potential is beyond the semiconductor’s conduction band edge. Using an electrolyte solution composed of a homogeneous cobalt bis(terpyridine) catalyst in a water/organic solvent mixture, stable photoelectrochemistry was observed under 1-sun illumination, exhibiting an ∼30% Faradaic efficiency for CO that was similar to a glassy carbon electrode under comparable conditions. Furthermore, the results demonstrate that an ultrathin thermal oxide interface is a robust platform for development of aqueous-stable, molecule-driven photoelectrocatalysis.

Catalysts↗

In Situ Characterization of Surface Recombination in p-Si/SiO x Based Photoelectrochemical Cells

Time-resolved infrared (TRIR) and electrochemical impedance spectroscopy (EIS) were utilized to quantify surface states present at silicon oxide (SiO x ) protected crystalline p-Si electrolyte interfaces. The primary goal was to identify p-Si/SiO x photoelectrodes with both low surface recombination rates and efficient multi-electron transfer to an acceptor present in the external electrolyte. Three SiO x layers were investigated: native oxide (nOx), chemical oxide (cOx), and rapid thermal annealed (RTA) thermal oxide (tOx). Comparative study with [Ru(bpy) 3 ](PF 6 ) 2 as the electron acceptor indicated that tOx was most optimal with a small effective recombination rate, multi-electron transfer capability, and photovoltage of 500 ± 50 mV. A secondary goal was to analyze the surface recombination rates with the Shockley–Read–Hall (SRH) kinetic model. Two surface states were identified from this analysis, one closer to the CB edge (V t,1 ) and the other near the midgap (V t,2 ). EIS and SRH analyses revealed that a forming gas (5% H 2 /N 2 ) anneal (FGA) decreased surface recombination for tOx and nOx through a lower density of surface states. In the case of tOx, the infrared data indicated that V t,2 was completely removed. Here, the energetic positions of the band edges were correlated with the surface state density; low densities corresponded to more favorable potentials for inversion layer formation, which is expected to be most optimal for photocatalysis. Collectively this study indicates that the free carrier dynamics provided by TRIR represent a powerful in situ probe of the band edge and the surface state energetics in silicon based photoelectrochemical cells.

Electrochemical Impedance Spectroscopy↗

Non-equilibrium anti-Stokes Raman spectroscopy for investigating Higgs modes in superconductors

Abstract Even before its role in electroweak symmetry breaking, the Anderson-Higgs mechanism was introduced to explain the Meissner effect in superconductors. Spontaneous symmetry-breaking yields massless phase modes representing the low-energy excitations of the Mexican-Hat potential. Only in superconductors the phase mode is shifted towards higher energies owing to the gauge field of the charged condensate. This results in a low-energy excitation spectrum governed by the Higgs mode. Consequently, the Bardeen-Cooper-Schrieffer-like Meissner effect signifies a macroscopic quantum condensate in which a photon acquires mass, representing a one-to-one analogy to high-energy physics. We report on an innovative spectroscopic technique to study symmetries and energies of the Higgs modes in the high-temperature superconductor Bi 2 Sr 2 CaCu 2 O 8 after a soft quench of the Mexican-Hat potential. Population inversion induced by an initial laser pulse leads to an additional anti-Stokes Raman-scattering signal, which is consistent with polarization-dependent Higgs modes. Within Ginzburg-Landau theory, the Higgs-mode energy is connected to the Cooper-pair coherence length. Within a Bardeen-Cooper-Schrieffer weak-coupling model we develop a quantitative and coherent description of single-particle and two-particle channels. This opens the avenue for Higgs Spectroscopy in quantum condensates and provides a unique pathway to control and explore Higgs physics.

Science & Technology - Other Topics↗

Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems

Abstract The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn–Sham potential of a time-dependent Kohn–Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn–Sham system and does not require any data on the exact Kohn–Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn–Sham potential matches the exact Kohn–Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn–Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn–Sham potential in the TDKS system.

97 MATHEMATICS AND COMPUTING↗

Uniform-density Bose-Einstein condensates of the Gross-Pitaevskii equation found by solving the inverse problem for the confining potential

Here, in this work, we consider a “reverse-engineering” approach to construct confining potentials that support exact, constant density kovaton solutions to the classical Gross-Pitaevskii equation (GPE) also known as the nonlinear Schr¨odinger equation (NLSE). In the one-dimensional case, the exact solution is the sum of stationary kink and anti-kink solutions, i.e. a kovaton, and in the overlapping region, the density is constant. In higher dimensions, the exact solutions are generalizations of this wave function. In the absence of self-interactions, the confining potential is similar to a smoothed out finite square well with minima also at the edges. When self-interactions are added, a term proportional to ±gψ*ψ gets added to the confining potential and ±gM, where M is the norm, gets added to the total energy. In the realm of stability analysis, we find (linearly) stable solutions in the case with repulsive self-interactions which also are stable to self-similar deformations. For attractive interactions, however, the minima at the edges of the potential get deeper and a barrier in the center forms as we increase the norm. This leads to instabilities at a critical value of M (related to the number of particles in the BEC). Comparing the stability criteria from Derrick’s theorem and Bogoliubov-de Gennes analysis stability results, we find that both predict stability for repulsive self-interactions and instability at a critical mass M for attractive interactions. However, the numerical analysis gives a much lower critical mass. The numerical analysis shows further that the initial instabilities violate the symmetry x → -x assumed by Derrick’s theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Application of PRIM for understanding patterns in carbon dioxide model-observation differences

Reducing uncertainties in regional carbon balances requires a better understanding of CO 2 transport in synoptic weather systems. Here, we apply the Patient Rule Induction Method (PRIM), a data-mining method to identify high-density regions for a target-class within an input parameter space, to airborne observations of potential temperature, wind speed, water vapor mixing ratio, and CO 2 dry mol fraction gathered during the Atmospheric Carbon and Transport (ACT)-America Summer 2016 and Winter 2017 campaigns. ACT observations were targeted at expert-designated cases of fair weather and near-frontal warm and cold sector air at atmospheric boundary-layer, lower-, and higher free tropospheric levels (ABL, LFT, and HFT, respectively). We investigate atmospheric characteristics of these pre-defined cases and associated CO 2 model-observation-differences in the mesoscale WRF-Chem model. PRIM results separate winter- and summertime observations as well as observations from ABL, LFT, and HFT with enrichment factors of 4.0–20.5 inside the PRIM box compared to the entire dataset but cannot distinguish between near-frontal warm and cold sector observations in the higher free troposphere. Analyzing of the parameter space constrained by PRIM, we find that large magnitude model observation differences preferentially associated with times when atmospheric conditions are less typical. This association suggests that PRIM could provide a useful tool for isolating atmospheric conditions with large-magnitude and non-Gaussian CO 2 -residuals for targeted transport model evaluation and to potentially improve inversion results during synoptically active periods.

Gerken, Tobias [James Madison Univ., Harrisonburg,↗

Reliability of Copper Inverse Opal Surfaces for Extreme-Heat-Flux Micro-Coolers in Low-Global-Warming-Potential Refrigerant R-1233zd Pool Boiling Experiments

This presentation provides a brief snapshot of the InterPACK paper InterPACK2023-113781. The paper explores copper inverse opal (CIO) surface reliability in pool boiling experiments in water and a new, low-global-warming-potential (GWP = 1) hydrofluoroolefin (HFO) refrigerant R-1233zd. The CIO-based structure is intended to develop enhanced two-phase heat transfer surfaces for extreme-heat-flux (approximately 1 kW/cm2) micro-coolers. In this study, a limited number of pool boiling experiments were performed using water and HFO-1233zd fluid, and the reliability of the CIO-based surfaces was evaluated. Critical heat flux (CHF) values in HFO-1233zd at 40 Degrees Celsius to 45 Degrees Celsius saturation temperatures and the corresponding saturation pressures were also measured. The CHF values with the refrigerant are significantly lower compared to those with water, but the refrigerant allows for a wider usable temperature range in the end application of the micro-coolers and is not limited to data centers with controlled ambient conditions. Reliability experiments with CIO surface samples - involving pool boiling with water on the CIO surfaces for approximately 48 hours and with HFO-1233zd for 144 hours - showed no structural degradation of the enhanced surface or any significant performance drop in heat transfer coefficients. The CIO surface samples in water were oxidized, most likely due to the presence of air in water and in the experimental vessel.

critical heat flux↗

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗

Temperature and density dependent pair potential for deuterium under shock

Large-scale classical molecular dynamics (CMD) simulations naturally include the microscopic physics necessary for atomistic modeling of shock release at the ablator-fuel interface in an inertial confinement fusion (ICF) capsule. Here, the multi-megabar shocks utilized in ICF experiments can drive the deuterium fuel from ambient to electron volt temperatures (T) and multi-fold compression. Modeling interatomic interactions over such an extreme range of conditions is challenging for empirical bond order potentials. We generate a pair potential for deuterium with explicit temperature and mass density dependence from ab initio density functional theory molecular dynamics using the iterative Boltzmann inversion method. This potential accurately reproduces the radial distribution functions and pressures from DFT in CMD equilibrium simulations across a wide range of thermodynamic conditions, yet fails to return the expected Hugoniot relations when used in direct CMD shock simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗

Extraction and selective promotion of zero-group velocity and cutoff frequency resonances in bi-dimensional waveguides using the electromechanical impedance method

This study showcases the electromechanical impedance (EMI) technique for extracting and promoting zero-group velocity (ZGV) and cutoff frequency resonances in a waveguide structure. Here, we identify the mechanisms of multiple resonances in the EMI spectra via a wave propagation perspective. Both simulation and experiments reveal the fact that sharp resonances in the conductance spectra are associated with either ZGV or cutoff frequency points. Consequently, we design four test configurations to enhance local resonances by aligning induced motions with considered mode shapes. Reasonable agreement between simulation and experiment results is observed. We evaluate the performance of considered configurations in terms of mode enhancement, and configurations that can selectively promote certain mode families are summarized. This study also shines the light on the EMI technique for quantitative non-destructive evaluation (NDE) by potentially supporting the inverse characterization of mechanical properties of host structures.

42 ENGINEERING↗

Signature of Correlated Insulator in Electric Field Controlled Superlattice

On a two-dimensional crystal, a “superlattice” with nanometer-scale periodicity can be imposed to tune the Bloch electron spectrum, enabling novel physical properties inaccessible in the original crystal. While creating 2D superlattices by means of nanopatterned electric gates has been studied for band structure engineering in recent years, evidence of electron correlations-which drive many problems at the forefront of physics research-remains to be uncovered. Here, in this work, we demonstrate signatures of a correlated insulator phase in Bernal-stacked bilayer graphene modulated by a gate-defined superlattice potential, manifested as resistance peaks centered at integer multiples of single electron per superlattice unit cell carrier densities. The observation is consistent with the formation of a stack of flat low-energy bands due to the superlattice potential combined with inversion symmetry breaking. Our work paves the way to custom-designed superlattices for studying band structure engineering and strongly correlated electrons in 2D materials.

36 MATERIALS SCIENCE↗