Surface Modification of Biochar for Dye Removal from Wastewater

Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization of biochar and biochar-based nanocomposites to play a key role in decontaminating dyes from wastewater. Numerous trials are underway to synthesize functionalized, surface engineered biochar-based nanocomposites that can sufficiently remove dye-contaminated wastewater. The removal of dyes from wastewater via natural and modified biochar follows numerous mechanisms such as precipitation, surface complexation, ion exchange, cation-pi interactions, and electrostatic attraction. Further, biochar production and modification promote good adsorption capacity for dye removal owing to the properties tailored from the production stage and linked with specific adsorption mechanisms such as hydrophobic and electrostatic interactions. Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy stage. The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications.

Automated summary

This review highlights the application of biochar and biochar-based nanocomposites for removing dyes from wastewater. Functionalized and surface engineered biochar-based nanocomposites are being synthesized to efficiently remove dye-contaminated wastewater. The mechanisms of dye removal include precipitation, surface complexation, ion exchange, cation-pi interactions, and electrostatic attraction. Biochar production and modification enhance its adsorption capacity. Smart technologies such as artificial neural networking and machine learning should be utilized for modeling and forecasting the potential of biochar modification.