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Cysteamine-Anchored MOF through Post-Synthetic Modification Strategy for the Effective Removal of Mercury from Water.

Minoo BagheriYeganeh AmidiMohammad Yaser Masoomi
Published in: Inorganic chemistry (2024)
The introduction of cysteamine functionality, referred to as Q-ZIF-67-SH, was successfully achieved through postsynthetic modification while maintaining the structural and thermal stability of the quasi metal-organic framework Q-ZIF-67. By subjecting ZIF-67 to controlled partial deligandation at 310 °C under an air atmosphere, a substantial number of unsaturated cobalt sites were generated within the quasi ZIF-67 (Q-ZIF-67) structure. These unsaturated cobalt sites facilitated effective coordination with cysteamine, resulting in the development of the thiol-functionalized framework Q-ZIF-67-SH. The potential of these metal-organic frameworks (MOFs) for the adsorptive removal of hazardous Hg(II) was investigated. Various factors, such as the type of sorbent, pH, adsorbent dosage, initial concentration of Hg(II), and presence of coexisting ions, were thoroughly examined and comprehensively explained. Thiol-anchored MOF significantly enhanced the efficiency of Hg(II) removal, achieving an impressive removal rate of up to 99.2%. Furthermore, it demonstrated a maximum adsorption capacity of 994 mg g -1 and a distribution coefficient of 2.5 × 10 6 mL g -1 . A good correspondence with pseudo-second-order kinetics and the Langmuir model was observed through the fitting of adsorption kinetics and the isotherm model. The thermodynamic data strongly indicate that the adsorptive removal of Hg(II) is characterized by endothermicity and spontaneity. This signifies that the process is energetically favorable and has potential for efficient Hg(II) removal. Therefore, the Q-ZIF-67-SH sorbent emerges as a promising and advantageous option for the removal of Hg(II) from water.
Keyphrases
  • metal organic framework
  • aqueous solution
  • fluorescent probe
  • living cells
  • computed tomography
  • electronic health record
  • quantum dots
  • machine learning
  • mass spectrometry
  • climate change
  • carbon nanotubes
  • human health