If somebody says the word temperature, you probably instantly think about hot or cold, depending on your environment. Ideally, we would like to maintain a temperature of 20 degrees C (68F) in an analytical laboratory. Just for some background, when you hear the word laboratory, you should automatically be thinking about a controlled environment. A controlled environment is key to every experiment or analysis, the purpose of which is to remove as many variables as possible that could influence results, thus enabling the ability to reproduce results anywhere else if needed. From laboratory studies, we can approximate real world results, or results in the field.
If you work in a laboratory, I don’t have to tell you this explicitly, it is the world we work in, however, we may have taken this information for granted. We learned this in school, but I seem to recall that there was a lot going on for me back when I was a student, so we may intrinsically understand this, but we might have overlooked the significance. Especially because labs tend to be cold, so we don’t need to think about it.
The majority of the labs that I have worked in have been cold, but I have worked in a room filled with instruments that more closely resembled a garage than a lab. I learned a lot about working in an uncontrolled environment. I am a problem solver at my core, and I chase perfection. To achieve perfection in an analytical laboratory, you have to understand all of the variables that affect your analysis.
One of the laboratories I worked in was completely out of control. When I started, we had a temporary HVAC system installed, picture temporary yellow ducts with a diameter of nearly 3 feet sprawling through the doors of the laboratory being strung temporarily from the ceiling. Picture buckets strategically placed under low points of the ducts to capture the condensation, then picture absorbent pads placed on the floor tracking these ducts to capture what didn’t make it in the bucket.
Part of my job scope was to ensure the quality of the data, so I step in the lab for the first time and I am sure I looked like the Waterboy when Coach Klein showed him the water he was serving his team (link to clip here). Never one to back down from a challenge, I took it on. Our quality control charts were all over the place, the majority of our analysis was being performed by a third party testing lab. Under normal circumstances, we have a protocol for dealing with instruments that are out of control, it usually involves recalibrating and establishing a new mean using 20 data points. This wasn’t working for us, as you can imagine. Essentially to get a good mean, the instrument has to be stable for 10 days, 2 points a day. We would get a new mean, place the instrument in service, only to fail again in the next day or two.
A particularly problematic analysis was ASTM D5599, if you are not familiar, this is used to measure the percentage of ethanol in gasoline, like the sign at the pump that says this product may contain up to 10% ethanol. D5599 is what we used to confirm the ethanol content. But, most refineries do not produce ethanol, instead they purchase it and add it to the gasoline as it is being loaded at the truck rack. There is no way to get a representative sample until the truck is loaded, and once it is loaded, it's too late to do anything about it, so we have to mimic the blending process in the laboratory. This is where ASTM D7717 comes in, the method for preparing volumetric blends of fuel ethanol with gasoline. We call these hand blends. It's interesting because this is a fairly unsophisticated method, utilizing less than precise measuring tools to make the blend. In fact, I will submit to you that the tools used to blend ethanol with gasoline at the truck rack are far superior to what is being done in the lab, flow controllers are extremely precise. In this scenario, the field is definitely more controlled than the lab, even if the lab was under ideal conditions.
Keep in mind that most experiments start by comparing one set of parameters to another. If you are designing an experiment, you might take a hypothesis and prove the concept by initially comparing to extreme sets of parameters and see if the results are directionally what was expected, and then you fine tune the experiment from there. So while the conditions of this laboratory were an analytically horrible and challenging situation, it was an incredible learning opportunity. I explain why in my previous article “Why Challenges Matter”.
Let’s define the problem: the concentration of ethanol does not meet specifications. The ethanol is blended in gasoline using a 1000 milliliter graduated cylinder. Here is a link I found that provides the uncertainty of measurement on different apparatus, for a 1000 ml graduated cylinder, the uncertainty of measurement is plus or minus 5 milliliters. This could potentially swing the error to plus or minus 0.62%, keep in mind that the repeatability of D5599 should be 0.25%. The results for an analytical test can only be as good as the least precise measurement tool, in this example, the graduated cylinder is the least precise, remember, this is in a controlled environment.
Cue temperature.
Density of any liquid is dependent on temperature, a good way to think about temperature is the difference between solid and liquid phases of a chemical (excluding water, water is special and behaves differently in terms of density). Whenever you see the density of a chemical, you will see at which temperature the density is measured, for good reason, at lower temperatures, the molecules move very slowly, and are more likely to cling closely to each other, at higher temperatures the molecules are moving faster, essentially demanding room to move around. In other words, the density of a chemical will be higher at lower temperatures and vice versa. For a pure component like ethanol, you can expect a difference of about 0.0087 g/ml, a complex mixture like gasoline could have a difference of 0.015 g/ml, if not more.
Why does temperature matter?
Temperature matters because it affects the amount of sample that is being tested, due to the change in density. Especially when you consider sample preparation, in this example we are using glassware, the volume of glass is also affected by temperature, which is why it is labeled with the temperature that it was calibrated at. I actually proved that temperature affects results by tracking lab environmental conditions along with QC results, when the temperature changes, the QC results would change. The next time you see a shift in your QC results, consider if it corresponds to a shift in temperature, this could save you lots of time and effort.