Dye Remediation from Water using Hematite Nanoparticles through performance of Response surface methodology (RSM), Photo Catalytic degradation and Equilibrium Adsorption Studies

Various industries like textile, paint, plastic manufacturing industry discharge harmful dye effluents in clean water reservoirs. Thus the water containing such dyes is unfit for aquatic life as well as humans. This work aimed to investigate the application of Dalbergia Sissoo (DS) sawdust and Acacia nilotica (AN) wood biomass modified Fe2O3 nanoparticles as effective adsorbents in order to take out methylene blue from contaminated water. The morphological studies of plant biomass showed that the surface was uneven having pores of different sizes hence allowing dye adsorption. The bands of various functional groups for example, hydroxyl, carbonyl, and C-H were found in spectra which were responsible for the stabilization of synthesized nanoparticles. Various analytical parameters such as contact time, methylene blue (MB) dye concentration, temperature, weight of adsorbate and photocatalytic degradation on the adsorption was studied. Maximum adsorption capacity qe (5.887 mg g-1) was obtained at the optimized settings. Dye remediation with ANFeNP was expanded up to 96% dye concentration was changed from 20mg/L to 100mg/L. The results best fitted Freundlich isotherm which proposed multi-layered adsorption occurred on adsorbent’s surface. The contact time parameter study revealed maximum dye removal of 97% at 105 minutes. Kinetic studies exhibited that process of adsorption obeys PSO and IPD models, which indicated that chemisorption was followed suggesting the diffusion of adsorbate in inner layers of adsorbent’s surface. With change in adsorbent’s dose from 0.1g to 0.5g, percentage removal was increased up to 96%. The photocatalytic activity of adsorbent at 120 minutes showed dye removal with a percentage of 97. The investigational data accurately fitted to Langmuir and Freundlich isotherms. Thermodynamic calculations indicated feasible adsorption which is also automatic and heat-absorbing. Statistical optimization studies were done using Response Surface methodology (RSM). The value of predicted and adjusted R2 indicates excellent relationship between predicted and experimental responses. Response surface methodology and central composite design were also applied to experimental data to predict the effect of multiple parameters revealing good correspondence between these parameters revealing good correspondence between these parameters.