Vapor pressures of Y,Ba, Cu precursors for the growth of YBa2Cu3O7 by MOVPE

The vapor pressures of theβ-diketonates Y(THD), Ba(THD), and Cu(THD), commonly used as precursors for MOVPE of YBa₂Cu₃O₇, and Y(MCP) were measured at different temperatures. A time-resolved static method recording the pressure vs. time at constant temperature was used, permitting us to deduce the vapor pressure even if the materials tend to decompose. The values of the constants of the Clausius-Clapeyron equation log₁₀p(T)/p ₀=A-B/T are,A=11.7, 8.7, 8.27, 16.6 andB=4359, 2654, 3602, and 6453 K for Y(THD), Y(MCP), Ba(THD), and Cu(THD), respectively withp ₀= 1 Pa and temperatureT in K. The thermal stability of the sources was measured and are discussed.     

Development of maraging steel compositions intended for quenching from the liquid state

A. A. Baikov Institute of Metallurgy, Moscow. Ulyanovsk Polytechnic Institute. Translated from Metallovedenie i Termicheskaya Obrabotka Metallov, No. 4, pp. 22–25, April, 1992.     

Properties of titanium-containing sol-gel films modified with copper and vanadium oxides

Translated from Steklo i Keramika, No. 1, pp. 4–6, January, 1994.     

Worm plots

Scientists sometimes spend hours conducting experiments only to find that the resulting data proves difficult to analyze with traditional visualization techniques. A typical laboratory experiment, for example, will setup several systems in each of two or more groups-a control group and various treatment groups. The investigator will then measure various parameters for each system over time (sometimes dozens to hundreds of parameters). The investigator tries to answer this question: What is different between the control and treatment groups? Field monitoring of polluted and unpolluted sites within a region result in the same kinds of data and the same difficulties in visualization. Plotting multiple lines on a single 2D plot quickly gets confusing. Plus, the dynamic interactions between parameters can be hard to see. 3D visualizations help researchers make qualitative insights about data more easily. We developed a 3D plotting technique, called “worm plots”, for visualizing these kinds of data. Our visualization tool examines how groups of points change over time. If we plot circles for each time slice, then these circles will change their relative sizes and positions as time goes by. To visualize these evolving dynamics, we connect these time slices with conic sections. This gives us spacetime worms that can be displayed graphically. In our experience, these simple circular cross sections work best when exploring data. They can be rendered quickly and give an easily intuited summary of each group. Further enhancements tend to make the image busy and difficult to interpret     

Singular value properties of LQ regulators

The closed-loop singular values of an LQ regulator transfer function are proved to be no greater than the singular values of the open-loop transfer function and, in the case of output-weighted cost function, to be no greater than the output-weighting parameter