Abstract

Over recent decades, industries have discharged significant volumes of arsenic-laden wastewater, resulting in severe contamination of groundwater and drinking water sources. Consequently, there has been a rise in the number of individuals afflicted with arsenic-related ailments. To tackle this issue, we explored the efficacy of a novel metal-organic framework (MOF) nanomaterial named 2HAP=N-MIL-88(Fe) for the removal of arsenic ions (As(III)) from water. This nanomaterial was synthesized through the reaction between 2-hydroxyacetophenone and an amino-functionalized iron-based MOF. We successfully produced and characterized the adsorbent using a range of techniques, including XRD, FT-IR, SEM, TGA, and BET. These methods affirmed the thermal resilience of the adsorbent and its substantial surface area of 180 m2/g. Batch trials were carried out to investigate various parameters, such as adsorbent dosage, concentration, duration, pH, and temperature. The optimum pH for effective adsorption was determined to be 4. Our findings revealed that the adsorption mechanism adhered to the Langmuir model concerning concentration and the pseudo-second-order model concerning duration. Moreover, the adsorption efficiency exhibited a rise with increasing temperature, suggesting an endothermic nature of the process. Additionally, we assessed the recyclability of the adsorbent and observed sustained high efficiency for up to 6 cycles. The sorption mechanism was identified as chemisorption, with an associated energy of 28.85 kJ·mol−1. In contrast to alternative adsorbents employed for As(III) elimination, the 2HAP=N-MIL-88(Fe) nanosorbent displayed remarkable efficacy, boasting a capacity of 265.5 mg/g. We delved into the mechanism of interaction between As(III) and the adsorbent, which could encompass ion exchange, electrostatic attraction, or hydrogen bonding. Notably, the adsorbent demonstrated chemical robustness, as evidenced by negligible deviations in the XRD patterns pre and post-regeneration. To enhance the adsorption outcomes, we employed the Box Behnken-design (BBD) method.

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