Solid-state Storage Devices Research Articles

Advanced materials for solid state hydrogen storage: “Thermal engineering issues”

Hydrogen has been widely recognized as the “Energy Carrier” of the future. Efficient, reliable, economical and safe storage and delivery of hydrogen form important aspects in achieving success of the “Hydrogen Economy”. Gravimetric and volumetric storage capacities become important when one considers portable and mobile applications of hydrogen. In the case of solid state hydrogen storage, the gas is reversibly embedded (by physisorption and/or chemisorption) in a solid matrix. A wide variety of materials such as intermetallics, physisorbents, complex hydrides/alanates, metal organic frameworks, etc. have been investigated as possible storage media. This paper discusses the feasibility of lithium– and sodium–aluminum hydrides with emphasis on their thermodynamic and thermo-physical properties. Drawbacks such as poor heat transfer characteristics and poor kinetics demand special attention to the thermal design of solid state storage devices.

Applications of Magnetoelectric Materials for Solid-State Devices

A pplications of M agnetoelectric M aterials for S olid -S tate D evices B S J Karthik Gururangan In 1947, three men, William Shockley, Walter Brattain, and John Barden, revolutionized the world of electronics by developing the first operational transistor. The transistor made the creation of the computer and other digital electronics possible, ushering in a new era of technology and earning the three men the 1956 Nobel Prize in Physics. Armed with the transistor, scientists and engineers (notably, those from Intel Corporation) developed faster, smaller, and more efficient microprocessors and RAM storage devices throughout the 1980s and 1990s. The development of solid-state storage devices and hard drives is of utmost importance for our increasingly electronic world. Within the last decade, computers have reached unprecedented operation speeds, but we are approaching a plateau. Recently, scientists have found that we cannot overcome this hurdle simply by fitting more and more transistors onto a chip; we must actually change the fabric of the device by creating new materials in a fashion that takes advantage of their inherent quantum electronic properties. Currently, hard drives operate by using the principle of magnetoresistance. Magnetoresistance describes the phenomenon of a magnetic polarization affecting the resistance of a material. Figure 1. Diagram of parallel and anti-parallel magnetic tunnel junction (MTJ) configurations (Tsymbal, 1999) In Figure 1, we can see that the magnetic tunnel junction (MTJ) consists of a thin insulator sandwiched by two magnets. The insulator has a thickness on the order of a few nanometers; therefore, electrons from one magnet can tunnel across the dielectric to the other magnet. Tunneling essentially means that every particle has a non-zero probability of traversing a solid boundary. While the mechanics of tunneling are not well understood, we do know that it is due to the wave-particle duality of matter; thus, tunneling is a purely quantum phenomenon and can only happen to very small particles such as electrons or quarks. When electrons traverse the boundary, they create a current and we can measure a resistance across the MTJ device. The number of electrons that will tunnel across the insulator is dictated by the relative orientations of the two magnets. Suppose that there are only two magnetic orientations: “left” and “right.” If both magnets have the same orientation, we call that the parallel state. If they point in opposite directions, they are in the anti-parallel state. Tunneling is highly encouraged in the anti-parallel state and suppressed in the parallel configuration (Nishimura, 2002). Thus, we can conclude that for a given potential, the anti- parallel state has a very low resistance while the parallel state has a high resistance. In hard disk drives, a read head applies a set voltage over many of these MTJ bits, measuring the resulting resistance of each one. It interprets a high resistance as a “1” state and a low resistance as a “0” state; thus, magnetoresistance directly leads to a digital interpretation. In the case of RAM (random-access memory), these bits are constantly being changed and re- interpreted over and over. The problem is that imparting these magnetic polarizations (a process called writing) utilizes an external magnetic field and is energetically inefficient given how many times the polarization changes. Recently, there has been a huge interest in a new class of materials called multiferroics. These materials exhibit two or more so-called “ferroic parameters”: ferroelectricity, ferromagnetism, and ferroelasticity. Current research suggests that using specific multiferroic materials in place of the magnets in Figure 1 can revolutionize computer memory systems and usher in a new era of quantum electronic technology. Before we can understand what the different ferroic parameters are and how they occur, we need to understand crystalline materials and crystal structure. When one thinks of crystals, one often pictures 1 • B erkeley S cientific J ournal • S ynthetics • S pring 2014 • V olume 18 • I ssue 2

Prospects for hydrogen storage in graphene

Hydrogen-based fuel cells are promising solutions for the efficient and clean delivery of electricity. Since hydrogen is an energy carrier, a key step for the development of a reliable hydrogen-based technology requires solving the issue of storage and transport of hydrogen. Several proposals based on the design of advanced materials such as metal hydrides and carbon structures have been made to overcome the limitations of the conventional solution of compressing or liquefying hydrogen in tanks. Nevertheless none of these systems are currently offering the required performances in terms of hydrogen storage capacity and control of adsorption/desorption processes. Therefore the problem of hydrogen storage remains so far unsolved and it continues to represent a significant bottleneck to the advancement and proliferation of fuel cell and hydrogen technologies. Recently, however, several studies on graphene, the one-atom-thick membrane of carbon atoms packed in a honeycomb lattice, have highlighted the potentialities of this material for hydrogen storage and raise new hopes for the development of an efficient solid-state hydrogen storage device. Here we review on-going efforts and studies on functionalized and nanostructured graphene for hydrogen storage and suggest possible developments for efficient storage/release of hydrogen under ambient conditions.

Quantum Storage of a Photonic Polarization Qubit in a Solid

We report on the quantum storage and retrieval of photonic polarization quantum bits onto and out of a solid state storage device. The qubits are implemented with weak coherent states at the single photon level, and are stored for a predetermined time of 500 ns in a praseodymium doped crystal with a storage and retrieval efficiency of 10%, using the atomic frequency comb scheme. We characterize the storage by using quantum state tomography, and find that the average conditional fidelity of the retrieved qubits exceeds 95% for a mean photon number μ=0.4. This is significantly higher than a classical benchmark, taking into account the poissonian statistics and finite memory efficiency, which proves that our crystal functions as a quantum storage device for polarization qubits. These results extend the storage capabilities of solid state quantum light matter interfaces to polarization encoding, which is widely used in quantum information science.

Recent developments in MAD version 8

In view of experience with the language decoder and memory manager, large parts of MAD have been rewritten recently. The dynamic memory manager is replaced by the CERN-written ZEBRA package which has been used successfully in more than 100 other programs for HEP data analysis. A new dynamic table handler keeps large tables in memory as long as they fit; it dumps them on disk or to the Cray SSD (solid-state storage device) if necessary. Tables are accessed by simple subroutine calls in a manner transparent to the user. The input language has been extended to use a more object-oriented approach. Accelerator elements are described in terms of classes of objects, providing easy selection of single elements in a large machine. A powerful mechanism has been implemented for writing tables of selected optical functions and/or element parameters in selected positions of the machine. These tables are generated and written under control of the table handler, and can be read directly by the LEP control system. MAD contains a plot module with various options to plot data from internal tables. The tables can also be fed into a stand-alone plot program. At present the following program modulus are complete: Command Decoder; Optical Functions Calculation; Closed-Orbit Correction; Harmon (chromaticity calculation); Plotting. It is foreseen that by the end of 1989 the following other modules will be ready: Matching, including some new features; Lie Algebraic Analysis: Tracking by TRANSPORT and Lie Algebraic Methods; Electron Beam Parameters; Polarization.

Application of Sparse Matrix Solvers as Effective Preconditioners

In this paper the use of a new out-of-core sparse matrix package for the numerical solution of partial differential equations involving complex geometries arising from aerospace applications is discussed. The sparse matrix solver accepts contributions to the matrix elements in random order and assembles the matrix using fast sort/merge routines. Fill-in is reduced through the use of a physically based nested dissection ordering. For very large problems a drop tolerance is used during the matrix decomposition phase. The resulting incomplete factorization is an effective preconditioner for Krylov subspace methods, such as GMRES. Problems involving 200,000 unknowns routinely are solved on the Cray X-MP using 64MW of solid-state storage device (SSD).

Three‐dimensional acoustic modeling by the Fourier method

A three‐dimensional forward modeling algorithm, allowing arbitrary density and arbitrary wave propagation velocity in lateral and vertical directions, directly solves the acoustic wave equation through spatial and temporal discretization. Spatial partial differentiation is performed in the Fourier domain. Time stepping is performed with a second‐order differencing operator. Modeling includes an optional free surface above the spatial grid. An absorbing boundary is applied on the lateral and bottom edges of the spatial grid. Three‐dimensional forward modeling represents a challenge for computer technology. Computation of meaningfully sized models requires extensive calculations and large three‐dimensional data sets which must be retrieved and restored during the computation of each time step. The computational feasibility of the Fourier method is demonstrated by implementation on the multiprocessor CRAY X‐MP computer system using the large secondary memory of the solid‐state storage device (SSD). Calculations use vectorization and parallel processing architecture. The similarity of numerical and analytical results indicates sufficient accuracy for many applications.

Three‐dimensional elastic modeling by the Fourier method

Earlier work on three‐dimensional forward modeling is extended to elastic waves using the equations of conservation of momentum and the stress‐strain relations for an isotropic elastic medium undergoing infinitesimal deformation. In addition to arbitrary compressional (or P‐wave) velocity and density variation in lateral and vertical directions, elastic modeling permits shear (or S‐wave) velocity variation as well. The elastic wave equation is solved using a generalization of the method for the acoustic case. Computation of each time step begins by computing six strain components by performing nine spatial partial differentiation operations on the three displacement components from the previous time step. The six strains and two Lamé constants are linearly combined to yield six stress components. Nine spatial partial differentiation operations on the six stresses, three body forces, and density are used to compute second partial time derivatives of the three displacement components. Time stepping to obtain the three displacement components for the current time step is performed with second‐order difference operators. The modeling includes an optional free surface above the spatial grid. An absorbing boundary is applied on the lateral and bottom edges of the spatial grid. This modeling scheme is implemented on a four‐processor CRAY X‐MP computer system using the solid‐state storage device (SSD). Using parallel processing with four CPUs, a reasonable geologic model can be computed within a few hours. The modeling scheme provides a variety of seismic source types and many possible output displays. These features enable the modeling of a wide range of seismic surveys. Numerical and analytic results are presented.

Large-scale vectorized implicit calculations in solid mechanics on a Cray X-MP/48 utilizing EBE preconditioned conjugate gradients

The major costs of large implicit finite element calculations, particularly in three dimensions, arise from computing solutions to systems of linear equations. Direct methods, i.e., those based upon Gaussian elimination, can easily require prohibitively large amounts of both CPU time and storage, even on current supercomputers. Iterative procedures avoiding the formation and factorization of a global system of equations can circumvent these difficulties. The element-by-element (EBE) preconditioned conjugate gradients (PCG) algorithm is presented in the context of a vectorized implementation within the production nonlinear stress analysis code nike3 d. Due to continued confusion as to the ease of vectorizing finite element procedures, we include examples of the main EBE subroutines in their entirety. The concept of a fractal dimension of a finite element mesh is introduced, and proves useful in characterizing the efficiency of this iterative algorithm with respect to a variable band, active column direct method. Sample calculations on a Cray X-MP/48 with solid-state storage device (SSD) illustrate the economy and range of applicability of EBE/PCG. Asymptotic cost formulae derived for two linear problems underscore differences between the direct and iterative algorithms for large problems and lead to predictions of problem size limitations imposed by the computing environment.

Early MIMD experience on the CRAY X-MP

This paper describes some early experience with converting four physics simulation programs to the CRAY X-MP, a current Multiple Instruction, Multiple Data (MIMD) computer consisting of two processors each with an architecture similar to that of the CRAY-1. As a multi-processor, the CRAY X-MP together with the high speed Solid-state Storage Device (SSD) in an ideal machine upon which to study MIMD algorithms for solving the equations of mathematical physics because it is fast enough to run real problems. The computer programs used in this study are all FORTRAN versions of original production codes. They range in sophistication from a one-dimensional numerical simulation of collisionless plasma to a two-dimensional hydrodynamics code with heat flow to a couple of three-dimensional fluid dynamics codes with varying degrees of viscous modeling. Early research with a dual processor configuration has shown speed-ups ranging from 1.55 to 1.98. It has been observed that a few simple extensions to FORTRAN allow a typical programmer to achieve a remarkable level of efficiency. These extensions involve the concept of memory local to a concurrent subprogram and memory common to all concurrent subprograms.

A hysteresis effect in photoconductive cadmium selenide and its use in a solid-state image storage device

A brief description of a new hysteresis effect in cadmium selenide photoconductive powder will be given. Photocurrent plotted as a function of voltage has the form of a hysteresis loop, with triggering from low to high currents occurring at a voltage depending on the incident light level. At a suitably chosen voltage, triggering from low to high currents occurs with light pulse excitation. Return to the condition of low current flow is brought about by brief interruption of the supply voltage. Since the triggering is present with ac or dc operation, it is possible to use the effect in a series circuit of photoconductor and ac operated electroluminescent panel. If this is done on an area basis with due attention paid to the impedance match of the photoconductor and electroluminescent layer, it is possible to make an image storage device having no optical feedback. The construction and operation of such a device is described, and a photograph of a stored image on a 3-inch panel is shown.