吸附
水溶液
介孔材料
多孔性
纳米孔
材料科学
化学工程
碳纤维
比表面积
电解质
纳米技术
复合材料
化学
有机化学
催化作用
电极
工程类
物理化学
复合数
作者
Piotr Kowalczyk,Artur P. Terzyk,Paulina Erwardt,Michael A. Hough,Artur P. Deditius,Piotr A. Gauden,Alexander V. Neimark,Katsumi Kaneko
出处
期刊:Carbon
[Elsevier]
日期:2022-07-20
卷期号:198: 371-381
被引量:7
标识
DOI:10.1016/j.carbon.2022.07.029
摘要
To accelerate the design and production of porous carbons targeting desired performance characteristics, we propose to incorporate machine learning (ML) regression into pore size distribution (PSD) analysis. Here, we implemented a ML algorithm for predicting paracetamol adsorption capacity of porous carbons from two pore structure parameters: total surface area and surface area of supermicropores-mesopores. These structural parameters of porous carbons are accessible from the software provided with automatic volumetric gas adsorption analyzers. It was shown that theoretical paracetamol capacities of porous carbons predicted using the ML algorithm lies within the range of experimental uncertainty. Nanoporous carbon beads with a high surface area of supermicropores (997 m2/g) and mesopores (628 m2/g) had the highest adsorption capacity of paracetamol (experiment: 480 ± 24 mg/g, ML predicted: 498 mg/g). The novel strategy for designing of porous carbon adsorbents using ML-PSD approach has a great potential to facilitate production of novel carbon adsorbents optimized for purification of aqueous solutions from non-electrolyte contaminates.
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