Multivariate analysis of biochar-derived carbonaceous nanomaterials for detection of heavy metal ions in aqueous systems

This article focuses on implementing multivariate analysis to evaluate biochar-derived carbonaceous nanomaterials (BCN) from three different feedstocks for the detection and differentiation of heavy metal ions in aqueous systems. The BCN were produced from dairy manure, rice straw and sorghum straw biochar using our NanoRefinery process. The NanoRefinery process transforms biochar into advanced nanomaterials using depolymerisation/chemical oxidation and purification of nanomaterials using solvent extraction. Dairy manure biochar-derived carbonaceous nanomaterials (DMB-CN), rice straw biochar-derived carbonaceous nanomaterials (RSB-CN) and sorghum straw biochar-derived carbonaceous nanomaterials (SSB-CN) were utilised as probes for the evaluation of their fluorescent properties and the detection of heavy metal ions. The BCN fluorescence quenching and fluorescence recovery was tested with lead (Pb2+). nickel (Ni2+). copper (Cu2+) and mercury (Hg2+). Principal component analysis (PCA) and discriminant analysis were used to differentiate among heavy metal ions in water samples. The BCN from different feedstocks had different characteristics and produced different interactions with heavy metal ions. DMB-CN had the highest quenching for Hg2+ and Ni2+ while SSB-CN and RSB-CN responded best to Cu2+ and Pb2+, respectively. The fluorescence quenching was modelled using linear and empirical functions. PCA and discriminant analysis used the quenching measurements to differentiate heavy metal ions in aqueous system. A key result was that the discriminant analysis had a 100% accuracy to detect Pb2+, 66% for Ni2+ and Cu2+, and 33% for Hg2+. This study has shown that biochar-derived carbonaceous nanomaterials could be used in heavy metal ions sensing applications. This is the first step in the development of a fast and accurate method for the detection of heavy metal ions in waters using environmentally friendly BCN. (C) 2019 Elsevier B.V. All rights reserved.