One of the biggest challenges in fighting climate change is bridging the gap between scientific research and real-world application, often referred to as the “valley of death.”
This is especially true in carbon capture, a field focused on removing carbon dioxide (CO2) from industrial emissions to prevent it from entering the atmosphere.
Many new materials, like metal-organic frameworks, have been created to capture CO2 effectively.
However, while these materials show promise in the lab, it’s difficult to know how well they will work in real-life situations.
This uncertainty means that many of these materials never make it past the research stage.
To address this, a team of scientists from Heriot-Watt University has developed a groundbreaking platform called PrISMa (Process-Informed design of tailor-made Sorbent Materials).
This platform uses advanced simulations and machine learning to identify the most cost-effective and sustainable materials for capturing CO2, even before they are tested in real-world settings.
The research, led by Professor Susana Garcia, was published in the journal Nature.
Professor Garcia, who is also the Associate Director of Carbon Capture, Utilization, and Storage at the Research Center for Carbon Solutions at Heriot-Watt University, explains the significance of this work.
“Over the past decade, much effort has been devoted to finding materials that can capture CO2,” says Professor Garcia. “Chemists have proposed thousands of new materials, but we lacked the tools to quickly evaluate their effectiveness in carbon capture processes.
This evaluation requires a lot of experimental data and detailed knowledge of the process, as well as an understanding of the economics and environmental impact.”
PrISMa addresses these challenges by integrating various aspects of carbon capture into one modeling tool. It combines quantum chemistry, molecular simulation, and machine learning to predict the performance of new materials.
The platform can use experimental data or simulate data for over 60 different case studies worldwide, speeding up the discovery of the best materials for carbon capture.
The platform not only helps chemists and engineers but also informs stakeholders about the economic and environmental challenges of different carbon capture technologies. It provides valuable insights for CO2 producers, investors, and policymakers.
PrISMa has already shown impressive results. For example, it has been used to simulate carbon capture technologies in cement plants worldwide, identifying suitable materials for each location and cutting costs by half compared to previous technologies.
The platform also includes an interactive tool that lets users explore the potential of over 1,200 materials for carbon capture.
Fergus Mcilwaine, a Ph.D. student working with Professor Garcia, highlights the role of machine learning in this process. “Screening a large number of materials requires a lot of computational time,” he says. “We developed a machine learning model that speeds up this process, allowing us to find cost-effective materials more quickly.”
PrISMa was developed in collaboration with scientists from several institutions, including the Swiss Federal Institute of Technology Lausanne (EPFL), ETH Zurich, Lawrence Berkeley National Laboratory, and the University of California Berkeley.
Professor Garcia concludes, “This study shows the importance of a holistic approach to evaluating technologies for achieving our net-zero targets.
PrISMa speeds up materials discovery for carbon capture and focuses research efforts on achievable performance targets. It can significantly aid our industrial decarbonization efforts and inform investment and policy decisions on sustainable carbon-capture solutions.”
