Daphne's Water Molarity Lab

I worked with Jube and Jazmean in the Water Potential and Molarity Mixup Lab to figure out what the molarity of six different solutions and the water potential of two vegetables were. We found that although each of the three experiments used the same solutions, we got slightly different results, majorly as a result of external forces. For this reason, it is important to examine all of the data for the first conclusion of this exercise, as Activity One was simply not conclusive enough to give an answer. In the first activity, the results were as followed, in order of the from 0.0M to 1.0M: Dark Green, Red, Orange, Light Green, Blue, and Yellow. In the second activity, we determined that the order was as follows based on data from the weight of vegetables: Dark Green, Blue, Light Green, Orange, Yellow, and Red. This order specifically was from the eggplant. This order is much more accurate, as there is a significantly less source of error in this experiment that in the okra results, which were Light Green, Orange, Dark Green, Yellow, Blue, and Red.  I deduced that the eggplant data is more accurate, as the okra was leaking ooze at the end of its experiment and it is possible that we measured significantly more or less for any one specific sample. When my group examined the data, we were able to calculate the water potential for eggplant, which was -8.240196 and for okra, which was -28.43974512.

In Activity One, we filled six dialysis bags with six different colored and molarity solution. Each solution was first weighed on the electric balance before being placed in a plastic container filled with plain tap water. They were all placed in one container covered with aluminum foil and left to soak overnight. This could have potentially been one source of error, as it may have been better to have placed them each in their own solution. The dye from the bags managed to escape and possibly mess with some of the measurements in the first experiment. This must have happened for sure to the Dark Green bag, which had the most negative percent change of -26.2%, from 39.3g to 29.2g. The next lowest percent change was the red bag with 1.98% from 45.4g to 46.3g. After this was orange with 12.7% from a mass of 40.8g to 46g. Light green had a percent change of 14.78%, going from a mass of 40.6g to 46.6g. Blue had the second most percent change of 31.4%, going from a mass of 43.3g to 56.9g after a night in the water solution. The solution with the highest percent change was yellow, at 52.26%, from a mass of 44.2g to 67.3

The solutions prior to being placed in dialysis tubing

The dialysis tubing shown  just prior to being weighed after a long
night of changing mass soaking in H2O

Although theoretically, one would think that we would get five positive percent changes and one zero, this is simply not the case. I would attribute this fault to the fact that the dialysis tubing was permeable to the dye as well as the water. We would expect to get these rates because five of the six solutions in the dialysis tubing have a higher concentration of solutes (0.2-1.0 molarity) and a lower concentration of water. One of them, which we deduced is the dark green solution, lacks solutes and therefore would have the exact same concentration as water. However, it shrunk because the plain tap water did not have as much dye as the green dialysis tubing bag. Because the other five solutions have a lower concentration of water in their bags, water would have to move into the dialysis bags because the tap water would clearly have a higher concentration of water. When water undergoes osmosis, it travels from a hypotonic solution to one which is hypertonic. The more solutes in a solution would mean that this solution also has a higher water potential and a higher molarity.

Although we did use information from activity one to understand which solution is which, this is certainly not the only activity we will be using, as there is the issue of how much human error is present. It is possible that either the presence of dye or distilled water in the bags may have skewed our numbers, so we chose to compare them with the rates we examined in Activity 2. In the second activity, we attempted to soak eggplant and okra in the six individual solutions. We did see a bit of a problem with our okra data, as it had a jellylike substance oozing from its center (as okra does) and it is possible that we acquired different amounts of this substance at any given point in time. We assessed the water potential of these two vegetables by cutting up samples and placing them in beakers filled with the water/sucrose/dye solutions and soaking them for one day.

The eggplant data seemed to be fairly consistent with what we expected them to be. In the eggplant experiment, we found that blue had a percent change of 7.4% from 9.4g to 10.1g, orange had a percent change of -8.08% from 9.9g to 9.1g, red had a percent change of -18.2%, from 9.9g to 8.1g, light green had a percent change of -3.92% from 10.2g to 9.8g, yellow had a percent change of -13.13g from 9.9g to 8.6g and dark green had a percent change of 22.6% from 11.5g to 14.1g. These numbers are reasonably in line with the data gathered from experiment one, as this shows the dark green and blue having slightly higher water content that, say, yellow and red, which had comparitively lower. As I mentioned earlier, water has a tendency to move from areas of higher concentration to lower concentration through dispersion properties. If we examine the data from the okra experiment, we see that it is far less consistent with the other two. Both data sets do, however, support the notion that the Red solution has the highest sucrose level, as it has the lowest percent increase in both scenarios.

In terms of the water potential of the vegetables themselves, this was also something that we gathered using this information. The graphs below show the trend in solution concentration versus the percent change in mass.The x-axis shows the molarity of each solution, while the y-axis shows percent change.