We've posted before on the topic of fitting chemical kinetics to HPLC data. Some good experiment planning and design can make this much faster, easier and more informative than a retrospective 'hope for the best' attempt to fit kinetics to experiments coming out of an empirical DOE.
Once the data have been collected from one or two experiments, it's time to check the mole balance. That means checking that your mental model of the chemistry taking place (e.g. A>B>C) and to which your DynoChem model will rigorously adhere, is consistent with the data you have collected. There's a nice exercise in DC Resources to take you through this step by step, using chemistry inspired by a reaction on which Mark Hughes and colleagues of GSK have published and presented.
The exercise starts with HPLC area (not area percent) and after correcting for relative responses leads directly to a new insight into the reaction, even before the first simulation has been run. When the modeling and experiments are done alongside each other and at the same time, such early insight impacts subsequent experiments and makes them more valuable while reducing their number.
We encourage you to take the exercise to learn this important skill and how to build better, more rigorous and more reliable kinetic models.
Once the data have been collected from one or two experiments, it's time to check the mole balance. That means checking that your mental model of the chemistry taking place (e.g. A>B>C) and to which your DynoChem model will rigorously adhere, is consistent with the data you have collected. There's a nice exercise in DC Resources to take you through this step by step, using chemistry inspired by a reaction on which Mark Hughes and colleagues of GSK have published and presented.
The exercise starts with HPLC area (not area percent) and after correcting for relative responses leads directly to a new insight into the reaction, even before the first simulation has been run. When the modeling and experiments are done alongside each other and at the same time, such early insight impacts subsequent experiments and makes them more valuable while reducing their number.
We encourage you to take the exercise to learn this important skill and how to build better, more rigorous and more reliable kinetic models.