Exploration of Key Technologies for Enhancing the Adsorption Performance of Porous Carbon Materials

The key technologies for improving the adsorption performance of porous carbon materials mainly include the following aspects:

Nitrogen doping technology: Nitrogen doped porous carbon materials are considered an effective CO2 adsorbent due to their unique chemical structure and properties. Nitrogen doping can enhance the interaction between adsorbents and adsorbates, resulting in higher CO2 adsorption capacity. Although there is currently no clear explanation for the mechanism of nitrogen doping enhancing the adsorption performance of adsorbents, research has shown that the large number of micropores, specific surface area, and induction force may be the fundamental reasons.

Pore structure adjustment: The pore structure and surface chemical properties of porous carbon materials have a significant impact on their carbon dioxide adsorption process. For example, for activated carbon and activated carbon fibers, there is a significant difference in their adsorption capacity for CO2, CH4, and N2 gases, which is mainly related to the number of micropores and specific surface area that match the adsorbed gas molecules.

Surface chemical properties: The oxygen content on the surface of porous carbon materials also greatly affects their carbon dioxide adsorption capacity. By oxidizing porous carbon materials, the number of oxygen-containing functional groups on their surface can be altered, thereby affecting their adsorption performance.

The application of machine learning in adsorption performance research: Machine learning based methods have also been used to study the key factors of CO2 adsorption in porous carbon materials. This method can provide guidance for material design and optimization by analyzing the influence of various factors such as porosity, surface area, adsorption enthalpy, etc. on adsorption performance.

In summary, the key to improving the adsorption performance of porous carbon materials lies in optimizing their pore structure, surface chemical properties, and enhancing their interaction with adsorbates through methods such as nitrogen doping. Meanwhile, utilizing modern analytical tools such as machine learning can help better understand the properties of these materials and optimize their adsorption performance.