Defect-Engineered Metal-Organic Frameworks with Coordinative Dye Modification for Cost-Effective Fluoride Detection...

Area of Science:

• Environmental Science
• Materials Science
• Analytical Chemistry

Background:

• Fluoride contamination in drinking water poses a global health risk, leading to fluorosis, particularly in regions like the Great Rift Valley.
• Accurate and affordable detection methods are crucial for managing fluoride levels in drinking water sources.

Purpose of the Study:

• To develop a cost-effective material for sensitive fluoride detection in water.
• To optimize metal-organic frameworks (MOFs) through defect engineering and post-synthetic modification for enhanced fluoride sensing.
• To validate the developed method using real-world water samples and established analytical techniques.

Main Methods:

• Synthesis of metal-organic frameworks (MOFs) with engineered defects.
• Coordinative post-synthetic modification of MOFs using Rhodamine B dye.
• Optimization of material response to fluoride ions under various conditions.
• Characterization of fluoride-responsive MOFs and investigation of dye release kinetics.
• Analysis of commercial mineral water and Tanzanian groundwater samples.

Main Results:

• A low-cost MOF-based material was successfully synthesized and optimized for fluoride detection.
• The material demonstrated a significant response to fluoride ions, with optimized design parameters enhancing sensing capabilities.
• Kinetics of Rhodamine B dye release in the presence of fluoride were elucidated.
• The method showed accurate fluoride quantification in real water samples, validated against ion-selective electrodes.

Conclusions:

• The developed MOF-based material offers a viable, low-cost solution for sensitive fluoride detection in drinking water.
• The defect engineering and modification strategy effectively enhances the sensing performance of MOFs for fluoride ions.
• This approach provides a valuable tool for water quality monitoring, especially in fluoride-affected areas like the Great Rift Valley.