Working Smarter: Leveraging Machine Learning to Optimize CO2 Adsorption
Scientists employ artificial intelligence to guide the design of biomass waste-derived novel materials for CO2 capture. Biomass waste can be used to produce porous carbons capable of sequestering CO2 gas emitted from large point sources (e.g., power plants, cement industries). However, there are no general guidelines on how such high-quality porous carbons should be synthesized or their optimal operational conditions. In a recent study, scientists employed machine learning-based method to determine which core factors should be prioritized in biomass waste-derived porous carbons to achieve the best CO2 adsorption performance, paving the way to a circular economy.
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