Atomic hydrogen storage method and apparatus

Atomic hydrogen, for use as a fuel or as an explosive, is stored in the presence of a strong magnetic field in exfoliated layered compounds such as molybdenum disulfide or an elemental layer material such as graphite. The compounds maintained at liquid helium temperatures and the atomic hydrogen is collected on the surfaces of the layered compound which are exposed during delamination (exfoliation). The strong magnetic field and the low temperature combine to prevent the atoms of hydrogen from recombining to form molecules

Quantum galvanomagnetic and thermomagnetic effects in graphite to 18.3 teslas /180 kG/ at low temperatures

Quantum galvanomagnetic and thermomagnetic effects in graphite in magnetic fields at low temperature

Hall effect magnetometer

A magnetometer which uses a single crystal of bismuth selenide is described. The rhombohedral crystal structure of the sensing element is analyzed. The method of construction of the magnetometer is discussed. It is stated that the sensing crystal has a positive or negative Hall coefficient and a carrier concentration of about 10 to the 18th power to 10 to the 20th power per cubic centimeter

Superconductivity in sputtered CuMO6S8

Samples were prepared by melting the metals, followed by annealing to various temperatures. The result was a structurally weak material. Sputtered films on sapphire substrates were prepared and studied. The substrates give the films mechanical strength and permit easy attachment of electrical leads. Materials were characterized by X-ray diffraction, electron microscopy, electrical resistance vs. temperature, and critical current measurements. Some of the results on CuMo6S8 are presented

Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network

Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (Rietbrock et al., 2012) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection

Diffusion length measurements in solar cells: An analysis and comparison of techniques

A brief review of the major techniques for measuring minority carrier diffusion lengths in solar cells is given. Emphasis is placed on comparing limits of applicability for each method, especially as applied to silicon cells or to gallium arsenide cells, including the effects of radiation damage

Ellipsometric study of InGaAs MODFET material

In(x)Ga(1-x)As based MODFET (modulation doped field effect transistor) material was grown by molecular beam epitaxy on semi-insulating InP substrates. Several structures were made, including lattice matched and strained layer InGaAs. All structures also included several layers of In(0.52)Al(0.48)As. Variable angle spectroscopic ellipsometry was used to characterize the structures. The experimental data, together with the calibration function for the constituent materials, were analyzed to yield the thickness of all the layers of the MODFET structure. Results of the ellipsometrically determined thicknesses compare very well with the reflection high energy electron diffraction in situ thickness measurements

Dielectric function of InGaAs in the visible

Measurements are reported of the dielectric function of thermodynamically stable In(x)Ga(1-x)As in the composition range 0.3 equal to or less than X = to or less than 0.7. The optically thick samples of InGaAs were made by molecular beam epitaxy (MBE) in the range 0.4 = to or less than X = to or less than 0.7 and by metal-organic chemical vapor deposition (MOCVD) for X = 0.3. The MBE made samples, usually 1 micron thick, were grown on semi-insulating InP and included a strain release structure. The MOCVD sample was grown on GaAs and was 2 microns thick. The dielectric functions were measured by variable angle spectroscopic ellipsometry in the range 1.55 to 4.4 eV. The data was analyzed assuming an optically thick InGaAs material with an oxide layer on top. The thickness of this layer was estimated by comparing the results for the InP lattice matched material, i.e., X = 0.53, with results published in the literature. The top oxide layer mathematically for X = 0.3 and X = 0.53 was removed to get the dielectric function of the bare InGaAs. In addition, the dielectric function of GaAs in vacuum, after a protective arsenic layer was removed. The dielectric functions for X = 0, 0.3, and 0.53 together with the X = 1 result from the literature to evaluate an algorithm for calculating the dielectric function of InGaAs for an arbitrary value of X(0 = to or less than X = to or less than 1) were used. Results of the dielectric function calculated using the algorithm were compared with experimental data