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The sound velocities of water in the Hugoniot states are investigated by laser shock compression of precompressed water in a diamond anvil cell. The obtained sound velocities in the off-Hugoniot region of liquid water at precompressed conditions are used to test the predictions of quantum molecular dynamics (QMD) simulations and the SESAME equation-of-state (EOS) library. It is found that the prediction of QMD simulations agrees with the experimental data while the prediction of SESAME EOS library underestimates the sound velocities probably due to its improper accounting for the ionization processes.

Traditional methods that are used to deal with radioactive surface contamination, which are time-consuming and expensive. As one effective measure of radioactive material purification, strippable coating, which effectively coats the pollutant, and settles them on the surface of objects. However, there are some shortcomings in terms of film formation and peelability, such as a brittle coating and poor peelability. Therefore, in order to meet the treatment methods for radioactive contaminants needs, the strippable coating must have excellent sealing, corrosion resistance, weather resistance, low environmental pollution, short film formation time, and good mechanical properties, in addition, the spraying process should be simple, with moderate adhesion, and it should be capable of being quickly and completely peeled off. In this paper, a ternary system was prepared by pre-emulsion polymerization with butyl-acrylate, methyl methacrylate, acrylic acid as the reactive monomer, sodium dodecyl sulfate as the active agent, potassium persulfate as the initiator, and water as the dispersion medium. The Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (1H-NMR), ICP emission spectrometer, surface tension tester, and universal testing machine were used to characterize the structure and morphology of the composite materials. The results show that the decontaminant can quickly wet the powder particles and the surface pollutants. The sealing efficiency of Fe and Cu was over 90%. After the decontaminant was cured, it could be continuously formed on the surface of different substrates and be completely peeled off, as well as having excellent film formation and peelability.

Although the streaked optical pyrometer (SOP) system has been widely adopted in shock temperature measurements, its reliability has always been of concern. Here, two calibrated Planckian radiators with different color temperatures were used to calibrate and verify the SOP system by comparing the two calibration standards using both multi-channel and single-channel methods. A high-color-temperature standard lamp and a multi-channel filter were specifically designed for the measurement system. To verify the reliability of the SOP system, the relative deviation between the measured data and the standard value of less than 5% was calibrated out, which demonstrates the reliability of the SOP system. Furthermore, a method to analyze the uncertainty and sensitivity of the SOP system is proposed. A series of laser-induced shock experiments were conducted at the ‘Shenguang-II’ laser facility to verify the reliability of the SOP system for temperature measurements at tens of thousands of kelvin. The measured temperature of the quartz in our experiments agreed fairly well with previous works, which serves as evidence for the reliability of the SOP system.

Temperature is one of the most important parameters for characterizing the thermodynamic state of matter in extreme conditions. However, there is as of yet no universal and accurate way to measure the temperature associated with a shock wave propagating in an opaque material, let alone an inversion method for determining how this temperature evolves. Based on the current strong generalization and learning abilities of artificial neural networks, this paper proposes using an artificial neural network to determine (i) how the shock-wave temperature in a material evolves and (ii) the surface temperature of the interface between the material and vacuum when a shock wave propagates through the material. Data generated using a one-dimensional numerical hydrodynamic simulation are used to train the artificial neural network by applying backpropagation and optimization to many datasets. Once the artificial neural network is trained sufficiently, it becomes an excellent approximator that can estimate the shock-wave temperature from a given streaked-optical-pyrometer image and other known information from the experiment. The paper ends with various possible extensions to the present research.

With the rapid development of ultrashort-pulse laser technology, short-pulse, high-flux neutron sources based on the interactions of laser plasmas have become increasingly important. However, the extremely strong gamma rays generated by the interaction between a picosecond laser beam and a target make it difficult to apply traditional neutron-diagnostic methods. Here, we introduce a neutron-diagnostic method that employs a BF3 detector array for use in the strong-gamma-ray environments associated with ultrashort-pulse-laser neutron sources. This method determines the neutron yield by collecting signals from each of six independent BF3 counting tubes, effectively reducing statistical-fluctuation uncertainties. We first propose a time-partition calibration method for the BF3 detector array that uses counts in different time intervals. With this calibration method, the dynamic range for neutron-yield measurements is expanded from 1× 103~ 1× 108 to 1× 103~ 1× 1010 neutrons (in 4π space). We have applied this neutron-diagnostic method for the first time to the ultrashort-pulse-laser neutron source at the SG-II upgrade facility.