![]() However, the low selectivity of traditional amine-based absorbents towards CO 2 capture remains yet a critical issue impeding the closing of carbon-cycle and the achievement of net-zero CO 2 emission ( Haider and Kumar, 2020). The up-to-date applications of CO 2 capture technologies generally involve an absorption process, with the addition of amine-based solution like monoethanolamine (MEA), diethanolamine (DEA), or methyl-diethanolamine (MDEA), to convert CO 2 into carbonate solids ( Ghanbari et al., 2020). From these perspectives, it is of great urgency to develop advanced and effective techniques for CO 2 capture and collection from industrial flue gas and ambient environment. Besides, by converting it back to energy storage materials ( Wu et al., 2021), adopting it in extracting residual oils from aquifers ( Chen et al., 2022), etc., the centralized control of CO 2 also serves the overall benefits of energy production in a depleting era of fossil fuels. Although future trends are difficult to be specified due to unexpected geological activities, the Paris Climate Agreement identifies that, to achieve the grand 1.5☌ goal, a net-zero emission of CO 2 must be realized by 2050, because even assuming a preferred scenario, the CO 2 will exceed 550 ppm until then ( Wang et al., 2011). The over-accumulated CO 2 in the air is escorted by the rise of Earth’s surface temperature by 0.6–0.7 C ( Pera-Titus 2014). Notably, this increasing trend is accelerating in the past decades, with the rate boosting from ∼1.1% in the 1990 s to ∼3.0% in the 2000 s. Compared to preindustrial times before the 1750 s, the CO 2 concentration in the troposphere has increased from ∼280 ppm to ∼400 ppm, with an annual increase of approximately 1 ppm ( Pera-Titus 2014 Oschatz and Antonietti 2018). The main purpose of this critical review is bridging the previous achievements and further developments of ML-assisted design of CO 2 capture techniques.įighting against climate change, with emphasis on the over-accumulated issue of carbon dioxide (CO 2) in the air, is one of the most predominant challenges facing carbon-intensive energy industries and the environmental community in the 21st century ( Guan et al., 2022). The major concerns remain to be further addressed are derived based on the above discussions, namely 1) the development of consistent and integrated databases, 2) the wise digitalization of inherent properties of materials, and 3) the validation of the accuracy of ML-derived results under practical scenarios. Then, through categorizing the materials into two major groups, that is, adsorbents (containing metal organic frameworks, carbonaceous materials, polymers, and zeolites) and absorbents (involving ionic liquids, amine-based absorbents, and deep eutectic solvents), the applications of this effective tool in relevant areas are scrutinized. ![]() From these perspectives, this critical review firstly summarizes the technical backgrounds for the applications of ML-based methods in CO 2 capture. ![]() Considering the diversity and complexity of CO 2 capture materials, machine learning has stepped into this field years ago and become a powerful tool that promotes the screening and design of involving parameters. School of Energy Science and Engineering, Central South University, Changsha, ChinaĮffective carbon dioxide (CO 2) capture plays indispensable roles in closing the global carbon cycle, serving the sustainable production of energy, and achieving the grand 1.5 ☌ goal by 2050.Zequn Yang, Boshi Chen, Hongmei Chen and Hailong Li* ![]()
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