Virtual Substance experiments

In this lab, the students determine the compression factor, (9) Z = PV/nRT, for Argon using the hard sphere model, the soft sphere model, and the Lennard-Jones model and compare those results to the compression factor calculated using the van der Waals equation of state and experimental data obtained from the NIST (70) web site. Figure 3 shows representative results from these experiments. The numerical accuracy of the Virtual Substance program is reflected by the mapping of the Lennard-Jones simulation data exactly onto the NIST data as seen in Figure 3. 

It gave me a better understanding of what was really going on in the real experiments in lab. Virtual Substance allowed you to see inside the container, visualizing the actual gas particles. ... 

For liquid droplets with diameters less than 0.01 mm, the LFL is virtually the same as the substance in vapor form. For mechanically formed mists with drop diameters between 0.001 and 0.2 mm, the LFL decreases as the drop diameter increases. In experiments with larger drop diameters the LFL was less than one-tenth of the vapor LFL. Thus, suspended droplets have a profound effect on flammability. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Whether they realize it or not, Leroy and Veronica have just embarked on an unforgettable process in which they will learn valuable life lessons through hands-on experience, lessons pertaining to one of the most basic and necessary substances in the human experience— water. They will learn about the unique properties of water, how water is used, the chemistry of water, and how water use regulations affect not only the environment but also virtually every aspect of every person s life, in the home, in the workplace, and in society in general. In short, Leroy and Veronica are about to experience contextual teaching and learning (CTL) at its best. 

The majority of experimental quantitative evaluations of the cavitation strength of liquids were carried out using water and its solutions as model systems. This is because of the reasonable simplicity of such experiments in this easier-to-handle low-temperature fluid. Measurements in a liquid metal, particularly in molten aluminum and its alloys which react and dissolve virtually all known substances, result in significant difficulties. These are connected with the methods of introduction of ultrasound into the melt as well as with the methods of control of the experimental conditions during the development of cavitation. 

Use the Interactive Virtual Experiment Separate the Substances found on Disc 1 of the CD-ROM. 

In essence, a powder diffractogram contains as much information as a single crystal experiment. When the intensity and the positions of the diffraction pattern are taken into account, the pattern is unique for a single substance. The X-ray diffraction pattern of a substance can be likened to a fingerprint. In effect, the pattern of lines on the powder diffraction pattern of a single phase is virtually unique, and mixtures of different crystals can be analysed if a reference set of patterns is consulted. 

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