Cost-effective biomass-derived activated carbons with a high CO(2) adsorption capacity are attractive for carbon capture. Bamboo was found to be a suitable precursor for activated carbon preparation through KOH activation. The bamboo size in the range of 10-200 mesh had little effect on CO(2) adsorption, whereas the KOH/C mass ratio and activation temperature had a significant impact on CO(2) adsorption. The bamboo-derived activated carbon had a high adsorption capacity and excellent selectivity for CO(2) , and also the adsorption process was highly reversible. The adsorbed amount of CO(2) on the granular activated carbon was up to 7.0 mmol g(-1) at 273 K and 1 bar, which was higher than almost all carbon materials. The pore characteristics of activated carbons responsible for high CO(2) adsorption were fully investigated. Based on the analysis of narrow micropore size distribution of several activated carbons prepared under different conditions, a more accurate micropore range contributing to CO(2) adsorption was proposed. The volume of micropores in the range of 0.33-0.82 nm had a good linear relationship with CO(2) adsorption at 273 K and 1 bar, and the narrow micropores of about 0.55 nm produced the major contribution, which could be used to evaluate CO(2) adsorption on activated carbons.
Polymer nanocomposites consist of a polymer matrix and fillers with at least one dimension below 100 nanometers (nm) [L. Schadler et al., Jom 59(3), 53–60 (2007)]. A key challenge in constructing an effective data resource for polymer nanocomposites is building a consistent, coherent, and clear data representation of all relevant parameters and their interrelationships. The data resource must address (1) data representation for representing, saving, and accessing the data (e.g., a data schema used in a data resource such as a database management system), (2) data contribution and uploading (e.g., an MS Excel template file that users can use to input data), (3) concept and knowledge modeling in a computationally accessible form (e.g., generation of a knowledge graph and ontology), and (4) ultimately data analytics and mining for new materials discovery. This paper addresses the first three issues, paving the way for rich, nuanced data analysis. We present the NanoMine polymer nanocomposite schema as an XML-based data schema designed for nanocomposite materials data representation and distribution and discuss its relationship to a higher level polymer data core consistent with other centralized materials data efforts. We also demonstrate aspects of data entry in an accessible manner consistent with the XML schema and discuss our mapping and augmentation approach to provide a more comprehensive representation in the form of an ontology and an ontology-enabled knowledge graph framework for nanopolymer systems. The schema and ontology and their easy accessibility and compatibility with parallel material standards provide a platform for data storage and search, customized visualization, and machine learning tools for material discovery and design.
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