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Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations
The crystal chemistry of americium-containing extended structures was investigated, and several classes of americium-containing solid-state oxide materials were obtained in single-crystal form via high-temperature flux crystal growth. This enabled the structural characterization of rare examples of ternary, quaternary, and penternary americium-containing silicates K 3 Am (Si 2 O 7 ) and Cs 6 Am 2 Si 21 O 48 , phosphates Na 3 Am (PO 4 ) 2 and K 3 Am (PO 4 ) 2 , borates Ba 3 Am 2 (BO 3 ) 4 and AmBO 3 , borate halides Ca 5 Am(BO 3 ) 4 Cl, molybdates Li 0.5 Am 0.5 MoO 4 , and fluorides CsAm 2 F 7 . Using these crystallographic data, the ionic radii of Am 3+ with coordination numbers of six (0.975 Å), seven (1.052 Å), and nine (1.162 Å) were established. A maximum entropy method (MEM) analysis was performed on the single-crystal X-ray diffraction data that were collected for K 3 Nd(PO 4 ) 2 /K 3 Am(PO 4 ) 2 , K 3 NdSi 2 O 7 /K 3 AmSi 2 O 7 , and NdBO 3 /AmBO 3 , to qualitatively compare the ionicities of the Nd–O and Am–O bonds. In conclusion, Raman spectroscopy data were collected on single crystals of K 3 Am(PO 4 ) 2 and compared to the calculated Raman spectrum of K 3 Am(PO 4 ) 2 obtained from DFT calculations.
The numerous types of noise in a typical radiograph include: quantum noise, read noise, thermal noise, scatter, etc. The character of these noise sources can be non-stationary, non-linear, Gaussian, Poisson, white, pink, additive, multiplicative, point, random and/or systematic. Furthermore these noise sources impact radiographic factors including: conjugates, exposure duration, filtration, detector type, Swank & Fano factors, Bucky grids, energy, collection optics, charge leakage, digitizer type, dynamic range, detector temperature, pixel size, calibration technique, system stability etc. In short, the “noise” in a radiograph is a very complex subject with many real-world tradeoffs. We delineate the noise sources in a typical silicon detector (e.g. CCD, CMOS, or AmSi, TFT) to illustrate the character of those sources and statistics behind some of the choices required to readout and calibrate those detectors.