Can we systematically rank the performance of any existing material for sorbent-based carbon capture?

A Roadmap for Achieving Scalable, Safe, and Low-cost Direct Air Carbon Capture and Storage

“…[18][19][20] There are numerous existing materials and millions of hypothetical ones that can be generated in silico to be explored for DAC applications. [108][109][110][111][112] Their properties (e.g., density, solubility, permeability, volatility, viscosity, porosity, heat of absorption, heat of adsorption, thermal conductivity) cover a broad design space, making their exploration challenging (#1). Further materials research could help reduce the energy consumption or increase CO 2 removal efficiency (#2).…”

“…Other studies have shown that metrics beyond material and process design, such as capital and OPEX (their evaluation requires a TEA of the process) and climate change (its evaluation requires a life cycle assessment (LCA) over the entire supply chain), may lead to different conclusions on the suitability of a material and/or a process. 108 There are currently limited studies that go beyond material metrics and explore the synergies between thousands of materials and different process designs with the help of a computer (#6).…”

“…Studies have shown that a lack of such a harmonization can lead to expensive delays in identifying optimally tailored materials. [108][109][110][111][112]114 Also, no framework exists for advising on optimal material and process characteristics needed catalyze large-scale implementation of DAC. This obstacle is observed among all DAC techniques, even those currently at higher TRL.…”

“…114 For example, in adsorption systems the regeneration method for a DACCS system cannot be easily selected as constraints on CO 2 purity might be imposed by the CO 2 destination option. 108 If a TVSA process is considered (Technology 1C), there is no straightforward process configuration to ensure the desired specifications in the product stream can be met at the lowest possible cost. Alternative heating methods such as indirect heating, steam injection, or an electrified method could significantly improve the process performance and reduce the energy consumption of DAC.…”

“…Exploration of material space and identification of optimal materials involves accessing existing material databases or creating new databases and computer models to better understand material properties. 108,112,154 Public access to these databases and models will accelerate knowledge transfer and provide the highest impact. Artificial intelligence and machine learning can be used to speed the exploration of materials.…”