Today in SSP, the students learned about computational chemistry. Victoria Crockett began with a power point presentation introducing the first activity: the chemistry of transitional lenses. She introduced the product to the students: glasses that were clear indoors and dark outdoors. The students then made guesses as to how they worked. They suggested that the glasses reacted to UV light from the sun, which Victoria confirmed. She then explained what ultraviolet light was using a light wavelength chart. The class determined that the glasses must become dark when exposed to UV light. Victoria explained how the lenses do this. Conjugated bonds lower the wavelength that a molecule absorbs, and when exposed to UV light, the number of conjugated bonds in the lenses increased, so the molecule could absorb lower wavelengths of light, or visible light. She used diagrams and a 3D model of the molecule to show how one of the bonds in the molecule breaks to increase the number of conjugated bonds. She then issued a challenge to the students: how do we know which is the dark lens without comparing the conjugated bonds? For this, the students were introduced to a web tool called WebMO.
The students built their first molecule, Diene, on WebMO, and Victoria explained which engine and test to chose for this job. Their first test optimized the geometry for the molecule, and then they ran a UV-vis job on the new molecule. The class looked at a graph produced by the job. It showed that Diene absorbed light in the 150-250 range. The class looked at this range on a light wavelength chart and saw this wasn't in the visual light spectrum. They then compared these results to the results on a test of Beta-Carotene, which is in carrots. They saw that Beta-Carotene absorbed visual light except where it was orange, hence the orange color of carrots.
Using this information, the class then answered some questions about the lens problem: What kind of light do we expect the dark lens to absorb? Where will this be on the graph? Next, the class tested the two lens molecules: napthopyran and indenonapthopyran. They then compared the two: the first molecule didn't absorb visible light and the second did. The class determined that the second molecule, indenonapthopyran must be the dark lens, because absorbing visible light gives it the dark color.
The next activity revolved around a website called Reciprocal Net which has information on many molecules. The class compared the structure of a sugar molecule to various other molecules, and came to the conclusion that molecules that had a similar structure to sugar were often things that tasted sweet. Victoria explained how this concept was related to artificial sweeteners. The class then compared other molecules and found this general rule held true for many other structures: molecules with similar structures had similar properties.
After lunch, the students looked at a susceptible, infected, recovered (SIR) model made in Netlogo. They experimented with the infection rate, fatality rate, and number of doctors to see the effects on the model. They then opened Microsoft Excel to start making an SIR model. They started by creating an unrealistic model where only one person per day would get infected, but they were able to easily change the number of people that got infected per day by changing the value of "delta infected". Since they had already input all of the formulas using simplified numbers, the model would change automatically if any of its inputs were changed. They added a line graph that would plot the number of susceptible and infected people. The students then did an activity to represent the spread of a disease. They rolled dice to determine if a person would become infected or recover from the disease.
After taking a short break, the students started working with excel again. They created a table with the results of the activity in it and then made a graph of the data. The final model they made using excel was a more accurate model of a disease spread where each infected person had a chance to infect other people they interacted with. They then added a graph to the model. The next thing they did was let infected people recover and become immune to the disease. They then added slider bars to their model so that the values would be easier to adjust. The final thing they did was adding death to their model. After finishing the model, the students changed some of the values in the model to see how they would affect the graph.