Nuclear energy provides electricity for a large segment of the global population, but it has one Achilles’ heel that remains to be adequately addressed: the waste. According to a report from Business Standard, however, scientists from the DOE/Lawrence Berkeley National Laboratory have made a breakthrough finding that could lead to a viable solution to this lingering problem.

A team of scientists has developed a new material that can clean up nuclear waste gases created as a byproduct of fuel reprocessing plants. The researchers say the material allows for the efficient, safe and cheap disposal of harmful byproducts of generating nuclear energy.

The study was carried out by an international team based in Switzerland at the Ecole Polytechnique Federale de Lausanne (EPFL). Researchers identified the material, named SBMOF-1 as a nanoporous crystal belonging to a class of materials that are used to sequester carbon dioxide emissions and other dangerous gases from the air.

Researchers believe the material will be able to absorb gases like xenon and krypton, which are both emitted as byproducts of nuclear fuel reprocessing. As of now, the best available method for treating these gases involves distillation at low temperatures, which is costly and dangerous.

The researchers showed that SBMOF-1 is effective at separating xenon and krypton at room temperature, and can self-assemble into a variety of crystal structures. Scientists used an innovative machine-learning method to identify the material and its waste-processing capabilities.

According to the one of the study’s co-authors Maciej Haranczyk, “Identifying the optimal material for a given process, out of thousands of possible structures, is a challenge due to the sheer number of materials. Given that the characterization of each material can take up to a few hours of simulations, the entire screening process may fill a supercomputer for weeks. Instead, we developed an approach to assess the performance of materials base don their easily computable characteristics. In this case, seven different characteristics were necessary for predicting how the materials behaved, and our team’s grad student Cory Simon’s application of machine learning techniques greatly sped up the material discovery process by eliminating those that didn’t meet the criteria.”

A press release from the Lawrence Berkeley National Laboratory describing the details of the study can be found here.