Covalent engineering of a phenanthroline-modified NH2-MIL-53(Al) MOF for the dual-mode sensing of As3+ and Fe2+ in complex environmental and dietary matrices

Abstract

Simultaneous detection of toxic metalloids and transition metals in water systems is crucial for environmental safety and public health. The current work presents the design and fabrication of a novel dual-analyte spectrophotometric sensor, Phen-GA-MIL-53(Al), developed through the covalent post-synthetic modification of an amino-functionalized aluminum-based metal–organic framework NH₂-MIL-53(Al). Sensing architecture was constructed by anchoring 5-amino-1,10-phenanthroline onto the MOF scaffold using glutaraldehyde as a flexible molecular spacer to create a high-density chelating environment within hierarchical pores. XRD, SEM-EDX, and BET studies validated the framework's structure and high surface area 1384 m² g⁻¹ after functionalization. Experimental data demonstrated that the sensor had exceptional sensitivity to As³⁺ at pH 4.0 and Fe²⁺ at pH 7.0. Detection of As³⁺ was controlled by a complex system of hydrogen bonding and Lewis acid-base interactions. On the other hand, for Fe²⁺, there was an observable chromogenic change to “naked-eye” from pale cream to orange-red with a strong MLCT absorption band; the chromogenic transition can be seen by naked-eye and does not need any specialized instrumentation. This makes it suitable for many field-screening applications. Both analytes had fast kinetics and broad linear ranges, with low limits of detection (LOD). 2D-COS and DFT calculations have provided great insights into the sensing mechanism, which is based on a sequential coordination pathway and a large decrease in the HOMO–LUMO energy gap after binding with an analyte. The sensor also has very high selectivity, good storage stability up to six weeks, and excellent reusability using Thiourea and EDTA as stripping agents. Practical tests of Phen-GA-MIL-53(Al) were done by analyzing tap water and dietary supplements that gave recovery rates between 97.27–99.83%, matching well with standard ICP-OES results. This study offers a flexible and field-deployable platform for ultra-trace detection of multi-target pollutants in complicated environmental matrices.