Idaho National Library developing models for so-called geopolymers

In Idaho, researchers at the Idaho National Library are developing models that allow highly accurate simulations of the evolution of so-called geopolymers. Like all polymers, they are composed of small units, or monomers. These basic units can link together in a variety of ways, and different arrangements can result in polymers with very different properties.

Polymeric materials are the backbone of many industries. Rechargeable batteries, for example, depend upon polymers to safely function and perform as desired, and so, too, do fuel cells. Polymers are found in filters and are an essential aspect of biofuel manufacturing and hydrocarbon extraction.

The last class of materials is geopolymers. What starts as animal and plant life is transformed by pressure and heat over millions of years. Along the way, it becomes a waxy material known as kerogen, which is found in oil shales around the world. With more heat, pressure and time, kerogen then evolves into liquid and gaseous hydrocarbons.

This is a complex process with many different possible paths. As a result, petroleum and kerogen in one place differ from that in another.

One challenge with geopolymers is that researchers have only a final product as a validation point for the model. They don’t have starting conditions or all the details of the transformation. So, they must make assumptions about the starting point and intervening conditions, with the model then suggesting how the geopolymer changed over time.

Another challenge is that a highly accurate model requires extensive computer resources. The computer does many calculations repeatedly, simulating how what started as organic matter converts into a hydrocarbon. Doing this with high fidelity demands that these calculations be made at many points in time and space. That can be computationally intensive. So, researchers may do an initial assessment at a lesser accuracy, using these results to select likely starting points and intervening environments from among many possibilities. They will then repeat the simulation, using a finer mesh of points in space and smaller steps in time to achieve greater accuracy.