Enhanced selective metal adsorption on optimised agroforestry waste mixtures

Area of Science:

• Environmental Science
• Materials Science
• Biotechnology

Background:

• Heavy metal contamination in water poses significant environmental and health risks.
• Agroforestry wastes are abundant, low-cost, and sustainable resources.
• Developing effective biosorbents from these wastes is crucial for environmental remediation.

Purpose of the Study:

• To evaluate the potential of different agroforestry wastes as biosorbents for heavy metal removal.
• To optimize a mixture of biosorbents for enhanced heavy metal adsorption.
• To investigate the adsorption mechanism and metal recovery.

Main Methods:

• Screening of fern (FE), rice husk (RI), and oak leaves (OA) for heavy metal removal (Cu(II), Ni(II), Mn(II), Zn(II), Cr(VI)).
• Optimization of biosorbent mixture proportions using simplex-centroid design.
• Adsorption kinetics and isotherm studies using pseudo-first order and Langmuir models.
• Analysis of functional groups involved in metal binding.
• Investigation of metal recovery efficiency.

Main Results:

• FE, RI, and OA showed high removal percentages for specific heavy metals.
• An optimized mixture (FE:OA:RI=50:13.7:36.3) improved overall removal by 10% to 19.25 mg/g.
• Adsorption followed pseudo-first order kinetics and Langmuir isotherm.
• Carboxylic, hydroxyl, and phenolic groups were key in metal-biomass binding.
• 100% recovery of cationic metals was achieved using acidic solutions.

Conclusions:

• Agroforestry wastes, particularly an optimized mixture of fern, rice husk, and oak leaves, are effective biosorbents for heavy metal removal.
• The adsorption process is well-described by pseudo-first order kinetics and Langmuir isotherm.
• The functional groups on the biomass play a significant role in metal adsorption.
• Acidic conditions facilitate efficient recovery of adsorbed cationic metals, enabling potential reuse of the biosorbent and recovery of valuable metals.