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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Large gyro-orbit model of ion velocity distribution in plasma near a wall in a grazing-angle magnetic field

A model is presented for the ion distribution function in a plasma at a solid target with a magnetic field $\boldsymbol {B}$ inclined at a small angle, $\alpha \ll 1$ (in radians), to the target. Adiabatic electrons are assumed, requiring $\alpha \gg \sqrt {Zm_{e}/m_{i}}$ , where $m_{e}$ and $m_{i}$ are the electron and ion mass, respectively, and $Z$ is the charge state of the ion. An electric field $\boldsymbol {E}$ is present to repel electrons, and so the characteristic size of the electrostatic potential $\phi$ is set by the electron temperature $T_{e}$ , $e\phi \sim T_{e}$ , where $e$ is the proton charge. An asymptotic scale separation between the Debye length $\lambda _{D} = \sqrt {\epsilon _0 T_{{e}} / e^{2} n_{{e}} }$ , the ion sound gyro-radius $\rho _{s} = \sqrt { m_{i} ( ZT_{e} + T_{i} ) } / (ZeB)$ and the size of the collisional region $d_{c} = \alpha \lambda _{\textrm {mfp}}$ is assumed, $\lambda _{D} \ll \rho _{s} \ll d_{c}$ . Here $\epsilon _0$ is the permittivity of free space, $n_{e}$ is the electron density, $T_{i}$ is the ion temperature, $B= |\boldsymbol {B}|$ and $\lambda _{\textrm {mfp}}$ is the collisional mean free path of an ion. The form of the ion distribution function is assumed at distances $x$ from the wall such that $\rho _{s} \ll x \ll d_{c}$ , that is, collisions are not treated. A self-consistent solution of the electrostatic potential for $x \sim \rho _{s}$ is required to solve for the quasi-periodic ion trajectories and for the ion distribution function at the target. The large gyro-orbit model presented here allows to bypass the numerical solution of $\phi (x)$ and results in an analytical expression for the ion distribution function at the target. It assumes that $\tau =T_{i}/(ZT_{e})\gg 1$ , and ignores the electric force on the quasi-periodic ion trajectory until close to the target. For $\tau \gtrsim 1$ , the model provides an extremely fast approximation to energy–angle distributions of ions at the target. These can be used to make sputtering predictions.

Physics↗
DOE OSTI2021

Charging and ion ejection dynamics of large helium nanodroplets exposed to intense femtosecond soft xray pulses

Ion ejection from charged helium nanodroplets exposed to intense femtosecond soft X-ray pulses is studied by single-pulse ion time-of-flight (TOF) spectroscopy in coincidence with small-angle X-ray scattering. Scattering images encode the droplet size and absolute photon flux incident on each droplet, while ion TOF spectra are used to determine the maximum ion kinetic energy, Ekin, of He+ j fragments (j = 1-4). Measurements span HeN droplet sizes between N similar to 10(7) and similar to 10(10) (radii R-0 = 78-578 nm), and droplet charges between similar to 9x10(-5) and similar to 3x10(-3) e/atom. Conditions encompass a wide range of ionization and expansion regimes, from departure of all photoelectrons from the droplet, leading to pure Coulomb explosion, to substantial electron trapping by the electrostatic potential of the charged droplet, indicating the onset of hydrodynamic expansion. The unique combination of absolute Xray intensities, droplet sizes, and ion Ekin on an event-by-event basis reveals a detailed picture of the correlations between the ionization conditions and the ejection dynamics of the ionic fragments. The maximum E-kin of He+ is found to be governed by Coulomb repulsion from unscreened cations across all expansion regimes. The impact of ion-atom interactions resulting from the relatively low charge densities is increasingly relevant with less electron trapping. The findings are consistent with the emergence of a charged spherical shell around a quasineutral plasma core as the degree of ionization increases. The results demonstrate a complex relationship between measured ion E-kin and droplet ionization conditions that can only be disentangled through the use of coincident single-pulse TOF and scattering data.

Helium↗
DOE OSTI2024

NbZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
NASA NTRS2016

Electric Potential and Electric Field Imaging with Applications

The technology and techniques for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field may be used for (illuminating) volumes to be inspected with EFI. The baseline sensor technology, electric field sensor (e-sensor), and its construction, optional electric field generation (quasistatic generator), and current e-sensor enhancements (ephemeral e-sensor) are discussed. Demonstrations for structural, electronic, human, and memory applications are shown. This new EFI capability is demonstrated to reveal characterization of electric charge distribution, creating a new field of study that embraces areas of interest including electrostatic discharge mitigation, crime scene forensics, design and materials selection for advanced sensors, dielectric morphology of structures, inspection of containers, inspection for hidden objects, tether integrity, organic molecular memory, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗
NASA NTRS2017

Electric Potential and Electric Field Imaging with Dynamic Applications & Extensions

The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field made be used for volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. Extensions to environment, Space and subterranean applications will be presented, and initial results for quantitative characterizing material properties are shown. A wearable EFI system has been developed by using fundamental EFI concepts. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, manufacturing quality control, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, weather prediction, earth quake prediction, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗
DOE OSTI2021

Solubility Behavior of CO 2 in Ionic Liquids Based on Ionic Polarity Index Analyses

Ionic liquids (ILs) can serve as effective CO 2 solvents with an appropriate selection of different anions and cations. However, due to the large library of potential IL compositions, rapid screening methods are needed for characterizing and ranking the expected properties. We have recently proposed the ionic polarity index (IPI) parameter, effectively connecting volume-based approaches and electrostatic potential analyses, providing a single metric that can potentially be used to rapidly screen for desirable IL properties. In this work, the corresponding anion and cation IPIs are used to generate correlations with respect to the CO 2 volumetric solubility in ILs. The relationships are generally applicable to groups of ILs within a homologous ion series, and this can be particularly valuable for pre-screening different ion pairings for maximizing gas solvation performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Electric Potential and Electric Field Imaging with Dynamic Applications: 2017 Research Award Innovation

The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field may be used for illuminating volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Initial results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗
NASA NTRS1996

Numerical Investigation of Two-Phase Flows With Charged Droplets in Electrostatic Field

A numerical method to solve two-phase turbulent flows with charged droplets in an electrostatic field is presented. The ensemble-averaged Navier-Stokes equations and the electrostatic potential equation are solved using a finite volume method. The transitional turbulence field is described using multiple-time-scale turbulence equations. The equations of motion of droplets are solved using a Lagrangian particle tracking scheme, and the inter-phase momentum exchange is described by the Particle-In-Cell scheme. The electrostatic force caused by an applied electrical potential is calculated using the electrostatic field obtained by solving a Laplacian equation and the force exerted by charged droplets is calculated using the Coulombic force equation. The method is applied to solve electro-hydrodynamic sprays. The calculated droplet velocity distributions for droplet dispersions occurring in a stagnant surrounding are in good agreement with the measured data. For droplet dispersions occurring in a two-phase flow, the droplet trajectories are influenced by aerodynamic forces, the Coulombic force, and the applied electrostatic potential field.

Kim, Sang-Wook↗
NASA NTRS1975

Effect of temperature-dependent electrical conductivity on transport processes in magnetosolidmechanics

The effect of temperature-dependent electrical conductivity on transport processes for a solid block is analyzed on the basis of a one-dimensional steady-state model under specified thermal boundary conditions. Assumptions are that the solid has an infinitely segmented electrode configuration, the magnetic field (By) may be resolved into a constant applied field and an induced field, the gradient of the electrochemical potential is equal to the electrostatic potential, a constant potential difference is applied externally across each pair of opposite electrodes, and all material properties except electrical conductivity are constant. Conductivity is expressed in normalized form in terms of a baseline conductivity and a constant for the material. The application of the assumptions of the model to the general phenomenological relations yields the governing equations. Solution of these equations gives the distribution of temperature, electric current density, and magnetic field strength along the length of the solid. It is shown that significant differences exist between the case for constant electrical conductivity and the case where electrical conductivity is temperature dependent.

Craig, G. T.↗
NASA NTRS1977

NASCAP user's manual

The NASCAP (NASA Charging Analyzer Program) code simulates the charging process for a complex object in either tenuous plasma or ground test environment. Detailed specifications needed to run the code are presented. The object definition section, OBJDEF, allows the test object to be easily defined in the cubic mesh. The test object is composed of conducting sections which may be wholly or partially covered with thin dielectric coatings. The potential section, POTENT, obtains the electrostatic potential in the space surrounding the object. It uses the conjugate gradient method to solve the finite element formulation of Poisson's equation. The CHARGE section of NASCAP treats charge redistribution among the surface cells of the object as well as charging through radiation bombardment. NASCAP has facilities for extensive graphical output, including several types of object display plots, potential contour plots, space charge density contour plots, current density plots, and particle trajectory plots.

Mandell, M. J.↗
NASA NTRS1982

Mapping of electrical potential distributions with charged particle beams

Methods for measuring electrostatic potentials on and near dielectric surfaces charged to several kilovolts are studied. Secondary emission from those charged dielectrics is measured. Candidates for potential measurement include the induced charge, from which potential is calculated; the trajectory endpoints of either high or low energy particles traversing the region near the surface; trajectory impact on the surface; and creating ions at points of interest near the surface. Some of the methods require computer simulations and iterative calculation if potential maps are to be generated. Several approaches are described and compared. A method using a half-cylinder as a test chamber and low-energy probing beams is adapted for the measurement of seconary emission.

Robinson, J. W.↗
DOE OSTI2026

How Does Water Dissociation Work in Bipolar Membranes?

Bipolar membranes (BPMs) create counteracting spatial gradients of pH and electrostatic potential in electrochemical systems, enabling applications in pH regulation, electrocatalysis, and separations. At the polarized junction of a BPM the water dissociation (WD, 2H2O ⇌ H3O+ + OH-) reaction can be driven, but it remains poorly understood. In this Perspective, we integrate molecular insights from bulk-water autoionization and the associated field effects with continuum descriptions of BPM electrostatics and experimental WD kinetic analyses to describe possible mechanisms of voltage-driven WD. Pristine BPM junctions highlight both the limits of primarily electric-field-driven WD and the practical challenges of junction stability at extreme reverse bias. Introducing heterogeneous catalyst layers, commonly metal oxides and graphene oxides, accelerates WD by orders of magnitude through hypothesized coupled effects in which surface acid-base functionality and high-density hydroxyl sites mediate proton-transfer steps, and catalyst mobile electronic/ionic charges redistribute the junction electric potential drop to shape the local electric fields and reactive microenvironments. Kinetic analyses suggest two regimes of heterogeneous WD mechanism, including field-driven ordering of interfacial water and a Second-Wien-Effect dissociation-barrier lowering. We conclude by defining the key unknown variables (local pH, electrostatic potential, catalyst charge state and relationships among mechanisms) and outlining experimental and multiscale modeling strategies needed for predictive WD catalysis and for controlling related ion-transfer reactions.

Wu, Yifan↗
DOE OSTI2022

Electronic Structure Calculations of the Fundamental Interactions in Solvent Extraction Desalination

Solvent extraction desalination (SED) is a promising membrane-free desalination technology, but new solvents are still needed that can improve the molecular-level water selectivity, while minimizing solvent contamination in the aqueous phase. Improvements in solvent design for SED require a fundamental understanding of the molecular interactions governing solvation behavior. Herein, quantum chemical calculations are used to delineate different fundamental interactions in water-solvent systems involving 38 different solvents, with a particular focus on the role of the electrostatic potential characteristics. Furthermore, these interactions are rigorously analyzed using the independent gradient model based on Hirshfeld partition (IGMH), Symmetry-Adapted Perturbation Theory (SAPT), as well as the energy decomposition analysis based on force field (EDA-FF) to compare against a standard force field model. The solvation free energies and partition coefficients are also calculated to estimate water extraction performance in a bulk solvent system.

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

Properties and Acceleration Mechanisms of Electrons Up To 200 keV Associated With a Flux Rope Pair and Reconnection X-Lines Around It in Earth's Plasma Sheet

The properties and acceleration mechanisms of electrons (<200 keV) associated with a pair of tailward traveling flux ropes and accompanied reconnection X-lines in Earth's plasma sheet are investigated with MMS measurements. Energetic electrons are enhanced on both boundaries and core of the flux ropes. The power-law spectra of energetic electrons near the X-lines and in flux ropes are harder than those on flux rope boundaries. Theoretical calculations show that the highest energy of adiabatic electrons is a few keV around the X-lines, tens of keV immediately downstream of the X-lines, hundreds of keV on the flux rope boundaries, and a few MeV in the flux rope cores. The X-lines cause strong energy dissipation, which may generate the energetic electron beams around them. The enhanced electron parallel temperature can be caused by the curvature-driven Fermi acceleration and the parallel electric potential. Betatron acceleration due to the magnetic field compression is strong on flux rope boundaries, which enhances energetic electrons in the perpendicular direction. Electrons can be trapped between the flux rope pair due to mirror force and parallel electric potential. Electrostatic structures in the flux rope cores correspond to potential drops up to half of the electron temperature. The energetic electrons and the electron distribution functions in the flux rope cores are suggested to be transported from other dawn-dusk directions, which is a 3-dimensional effect. The acceleration and deceleration of the Betatron and Fermi processes appear alternately indicating that the magnetic field and plasma are turbulent around the flux ropes.

58 GEOSCIENCES↗
DOE OSTI2026

Cosolvent-tuned interactions in ionic liquids: A vibrational and quantum-chemical study of ethylene glycol ratio effects

Ionic liquids (ILs) are attractive media for CO 2 capture but remain limited by viscosity and cost. Blending ILs with ethylene glycol (EG) is a practical route to mitigate these constraints, yet the molecular origins of cosolvent effects and their dependence on composition are not well resolved. We combine Fourier-transform infrared (FT-IR) spectroscopy with quantum-chemical (DFT) analysis to elucidate how the IL:EG molar ratio modulates intermolecular interactions and electronic structure. Computed vibrational frequencies enable mode assignment and deconvolution of overlapping bands, revealing systematic, ratio-dependent shifts and broadenings in (i) EG O–H stretching, (ii) cation and EG C–H stretchings (imidazolium C2–H, C4–H, C5–H, methyl and ethyl groups, -CH2 of EG), (iii) anion signature modes (e.g., CN motifs), and (iv) EG C–O and C–C stretchings, consistent with the redistribution of hydrogen-bonding networks. Molecular electrostatic potential (MESP) maps quantify attenuation of extreme potential regions with increasing EG, indicating progressive screening of cation–anion electrostatic interactions. Quantum Theory of Atoms in Molecules (QTAIM) identifies emergent bond critical points between EG and the IL ions, while Reduced Density Gradient–Noncovalent Interaction (RDG–NCI) analysis differentiates strong directional hydrogen bonds from dispersive contacts across compositions. Together, these results show that EG fraction controls a switch from predominantly ion–ion to mixed ion–EG coordination, altering local polarity and polarizability that underlie the observed FT-IR trends. The framework provides composition–structure–spectrum relationships that can guide rational selection of IL:EG ratios to balance favorable molecular interactions with practical performance targets in scalable CO 2 capture systems.

DAC↗