73 research outputs found

    Fast resistive bolometry

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    Resistive bolometry is an accurate, robust, spectrally broadband technique for measuring absolute x-ray fluence and flux. Bolometry is an independent technique for x-ray measurements that is based on a different set of physical properties than other diagnostics such as x-ray diodes, photoconducting detectors, and P-I-N diodes. Bolometers use the temperature-driven change in element resistivity to determine the total deposited energy. The calibration of such a device is based on fundamental material properties and its physical dimensions. We describe the use of nickel and gold bolometers to measure x rays generated by high-power z pinches on Sandia's Saturn and Z accelerators. The Sandia bolometer design described herein has a pulse response of {approximately}1 ns. We describe in detail the fabrication, fielding, and data analysis issues leading to highly accurate x-ray measurements. The fundamental accuracy of resistive bolometry will be discussed

    Soft x-ray measurements of z

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    This article reports the experimental characterization of a z-pinch driven-vacuum hohlraum. The authors have measured soft x-ray fluxes of 5 x 10{sup 12} W/cm{sup 2} radiating from the walls of hohlraums which are 2.4--2.5 cm in diameter by 1 cm tall. The x-ray source used to drive these hohlraums was a z-pinch consisting of a 300 wire tungsten array driven by a 2 MA, 100 ns current pulse. In this hohlraum geometry, the z-pinch x-ray source can produce energies in excess of 800 kJ and powers in excess of 100 TW to drive these hohlraums. The x-rays released in these hohlraums represent greater than a factor of 25 in energy and more than a factor of three in x-ray power over previous laboratory-driven hohlraums

    Functional and informatics analysis enables glycosyltransferase activity prediction

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    The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical–bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions

    Characterization and error analysis of an N×N unfolding procedure applied to filtered, photoelectric x-ray detector arrays. II. Error analysis and generalization

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    A five-channel, filtered-x-ray-detector (XRD) array has been used to measure time-dependent, soft-x-ray flux emitted by z-pinch plasmas at the Z pulsed-power accelerator (Sandia National Laboratories, Albuquerque, New Mexico, USA). The preceding, companion paper [D. L. Fehl et al., Phys. Rev. ST Accel. Beams 13, 120402 (2010)PRABFM1098-4402] describes an algorithm for spectral reconstructions (unfolds) and spectrally integrated flux estimates from data obtained by this instrument. The unfolded spectrum S_{unfold}(E,t) is based on (N=5) first-order B-splines (histograms) in contiguous unfold bins j=1,…,N; the recovered x-ray flux F_{unfold}(t) is estimated as ∫S_{unfold}(E,t)dE, where E is x-ray energy and t is time. This paper adds two major improvements to the preceding unfold analysis: (a) Error analysis.—Both data noise and response-function uncertainties are propagated into S_{unfold}(E,t) and F_{unfold}(t). Noise factors ν are derived from simulations to quantify algorithm-induced changes in the noise-to-signal ratio (NSR) for S_{unfold} in each unfold bin j and for F_{unfold} (ν≡NSR_{output}/NSR_{input}): for S_{unfold}, 1≲ν_{j}≲30, an outcome that is strongly spectrally dependent; for F_{unfold}, 0.6≲ν_{F}≲1, a result that is less spectrally sensitive and corroborated independently. For nominal z-pinch experiments, the combined uncertainty (noise and calibrations) in F_{unfold}(t) at peak is estimated to be ∼15%. (b) Generalization of the unfold method.—Spectral sensitivities (called here passband functions) are constructed for S_{unfold} and F_{unfold}. Predicting how the unfold algorithm reconstructs arbitrary spectra is thereby reduced to quadratures. These tools allow one to understand and quantitatively predict algorithmic distortions (including negative artifacts), to identify potentially troublesome spectra, and to design more useful response functions

    Characterization and error analysis of an N×N unfolding procedure applied to filtered, photoelectric x-ray detector arrays. I. Formulation and testing

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    An algorithm for spectral reconstructions (unfolds) and spectrally integrated flux estimates from data obtained by a five-channel, filtered x-ray-detector array (XRD) is described in detail and characterized. This diagnostic is a broad-channel spectrometer, used primarily to measure time-dependent soft x-ray flux emitted by z-pinch plasmas at the Z pulsed-power accelerator (Sandia National Laboratories, Albuquerque, New Mexico, USA), and serves as both a plasma probe and a gauge of accelerator performance. The unfold method, suitable for online analysis, arises naturally from general assumptions about the x-ray source and spectral properties of the channel responses; a priori constraints control the ill-posed nature of the inversion. The unfolded spectrum is not assumed to be Planckian. This study is divided into two consecutive papers. This paper considers three major issues: (a) Formulation of the unfold method.—The mathematical background, assumptions, and procedures leading to the algorithm are described: the spectral reconstruction S_{unfold}(E,t)—five histogram x-ray bins j over the x-ray interval, 137≤E≤2300  eV at each time step t—depends on the shape and overlap of the calibrated channel responses and on the maximum electrical power delivered to the plasma. The x-ray flux F_{unfold} is estimated as ∫S_{unfold}(E,t)dE. (b) Validation with simulations.—Tests of the unfold algorithm with known static and time-varying spectra are described. These spectra included—but were not limited to—Planckian spectra S_{bb}(E,T) (25≤T≤250  eV), from which noise-free channel data were simulated and unfolded. For Planckian simulations with 125≤T≤250  eV and typical responses, the binwise unfold values S_{j} and the corresponding binwise averages ⟨S_{bb}⟩_{j} agreed to ∼20%, except where S_{bb}≪max⁡{S_{bb}}. Occasionally, unfold values S_{j}≲0 (artifacts) were encountered. The algorithm recovered ≳90% of the x-ray flux over the wider range, 75≤T≤250  eV. For lower T, the test and unfolded spectra increasingly diverged as larger fractions of S_{bb}(E,T) fell below the detection threshold (∼137  eV) of the diagnostic. (c) Comparison with other analyses and diagnostics.—The results of the histogram algorithm are compared with other analyses, including a test with data acquired by the DANTE filtered-XRD array at the NOVA laser facility. Overall, the histogram algorithm is found to be most useful for x-ray flux estimates, as opposed to spectral details. The following companion paper [D. L. Fehl et al., Phys. Rev. ST Accel. Beams 13, 120403 (2010)PRABFM1098-4402] considers (a) uncertainties in S_{unfold} and F_{unfold} induced by both data noise and calibrational errors in the response functions; and (b) generalization of the algorithm to arbitrary spectra. These techniques apply to other diagnostics with analogous channel responses and supported by unfold algorithms of invertible matrix form
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