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“Perhaps some day the precision of the data will be brought so far that the mathematician will be able to calculate at his desk the outcome of any chemical combination, in the same way as he calculates the motions of celestial bodies.” —Antoine-Laurent Lavoisier
A successful product involves many factors, but none is more important than the formulation itself. However well a product may be marketed, it is the stuff in the bottle that earns consumer loyalty, and the chemical toolbox has a constantly expanding range of new raw materials. A formula has numerous ingredients and can be varied in thousands of ways—how can the best one be found? An even greater challenge arises when ingredients interact or change with circumstances.
The old way to formulate involved a chemist at a bench making multiple trials. Large companies have an army of R&D staff generating new products. Ideally, there are some general guidelines of how much of each material to use or the ratios of some materials—say the ratio of the anionics to the alkanolamide in a shampoo—but, still, a lot of trial and error is involved in getting just the right properties.
Experiments create data, and fortunately when data is graphed, patterns can emerge. In a shampoo formulation, the salt curve is an obvious example. In many systems, adding salt first makes the viscosity go up, but too much causes a precipitous decline. Aiming a bit to the left of the peak point gives maximum viscosity, with a little wiggle room if too much salt is added. A simple correlation of viscosity to salt content is a powerful formulation tool.
The situation gets more challenging when new materials are involved or the ingredients can interact. If order of addition is critical, suddenly the number of possibilities jumps exponentially. The poor chemist might wish for a robot to generate every possibility and present the optimum blend. That robot has arrived.