A little over a week ago, I attended Workshop 2 of the STLP down in Wellington. Where Workshop 1 had been heavily focused on 'Gathering and Interpreting Data' and 'Using Evidence', this workshop moved us on to 'Critiquing Evidence' and 'Interpreting Representations'. I'll reflect upon the sessions in Wellington in another post – right now I want to skip ahead and think about those capabilities in connection with the work I have been doing at Massey over the past week.
Ben Munro, PhD student, invited me to participate in his research on titanium dioxide (a.k.a. titania) emulsions. I am going to butcher this explanation of his research but I'll give it my best shot..... Essentially, he is wanting to see if he can alter the stability & viscosity of an emulsion by manipulating the particle charge that is present in that emulsion. He is testing emulsions at different concentrations of titania and at a range of pH levels. The tests being carrying out on the solutions include:
Light microscopy – How big are the droplets? Are the consistent? Can we see them coalescing or are they stable?
Mastersizer – What is the average droplet size? What is the range of the droplet size?
Zetasizer – What is the charge in mV of each emulsion?
Rheometer – How stable is the emulsion? How does it response to stress and strain?
Here are some microscope images of one of the emulsions I made. As you can see, there is not a particularly consistent droplet size and where they are gather, I observed some of them coalescing (joining together). Not particularly ideal because it indicated that this emulsion was possibly unstable.
With the exception of physically seeing the sample on the light microscope, the other tests were all done within an enclosed device, connected to a computer which outputs a stream of data. The ability to be able to accurately interpret the data and the representations that the computers and devices were outputting was essential to understanding to properties of the emulsions we were working with.
First, we put our emulsions into the Mastersizer. We tested two emulsions that had different pH levels but consistent concentrations of titania. This is the data we got from the first one:
The x axis indicates the size of droplets and the y axis indicates the % of measured droplets being at x particular size. The differing coloured lines indicate the 4 different time points we measured at. We noticed that a majority of the droplets sat between 10[2] and 10[3] micrometres (µm). In the perfect emulsion, we would want to see all particles approximately the same size. The tail at the end indicates that there is a small percentage of some much smaller droplets present. These results also show that the droplet size remained stable over the time it took to complete the tests.
Then we tested the second emulsion:
This data looks very different and tells us some very interesting things about this emulsion. Most obvious is that the different lines which indicate the time at which the test was taken are very different. This means that the droplet sizes were changing over time. Initially, the droplet size in the emulsion was consistent (black line) with a large peak at about 50µm. However, the more time that passed, the more the initial peak of the graph began to lower and a secondary peak at approximately 200-300µm began to grow. Over time, the 200-300µm peak overtook the other. This indicates that the droplets were coalescing during the test, gradually forming larger and larger droplets. This emulsion was not stable.
We then tested our samples on a rheometer to observe their response to small, controlled stress. This is what the rheometer looked like while processing a sample.
Again, there was very little to physically see here.
The movements of the rheometer, especially at the start of the test, are so minute that they can't be seen with the naked eye. We look to the computer to provide us with the data we need to understand our emulsions. The emulsion I tested on the rheometer was the same that gave us the funky looking graphs from the Mastersizer, directly above. We thought it would be interesting to observe how this particular emulsion, being unstable, responded to stress.
The rheometer was setup to lower the head to 400µm over the sample, begin rotating very slowly and increase speed, and therefore stress, over time. Here is the data it gave us:
The x axis represents the shear rate (how fast the head is rotating). The measurements were taken at consistent time intervals which is why the spacing of the line indicators is horizontally consistent. The left y axis represents the viscosity (how thick) the emulsion is and the right y axis represents the stress being applied to the emulsion. Logically, as the shear rate rises, the amount of stress rises.
These results are interesting because normally the viscosity of the emulsion should change at a constant rate. This would look like a perfectly straight diagonal line. The question in this data is, what happened between 10[-1] and 10[0]? We had a theory that as the stress increased, the rate of coalescing increased until it reached a 'breaking point' where the oil separated from the rest of the solution, suddenly altering its viscosity. After the testing had concluded and we raised the head of the rheometer, our theory was proven correct. There was a very distinct outline of clear oil surrounding the now thin and grey titania solution. We placed a small droplet of the original solution next to it to double check out thinking.
Taking our time with the "failed" sample emulsion was worthwhile because it gave us insight and solid data about a solution that won't get Ben to his overall goal. A failed sample doesn't mean it is going to provide bad data. What it does provide is data about an emulsion makeup that doesn't meet his criteria of stability. As he moves towards discovering the perfect emulsion, there will be more unstable samples but, ideally, he will start to get a greater quantity of increasingly stable ones.
All of this interpreting of representations got me thinking this morning about just how interdisciplinary the nature of science is. Over the past week, I have been engaged in material and physical world science thinking, calculus thinking, statistical analysis thinking, and processing all of that into some sort of coherent text all in an effort to be an accurate scientist and make the best sense of the data that I possibly can. I wish someone at my high school had had the insight to connect these things together in a meaningful way so that I could get a genuine feeling of purpose, especially in the mathematics domains. It reinforces for me the need for interdisciplinary teaching and learning. After all, it is how the real-world works.
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