Performance and modeling of Ni(II) adsorption from low concentrated wastewater on carbon microspheres prepared from tangerine peels by FeCl3-assisted hydrothermal carbonization uri icon

abstract

  • The authors are grateful to the FCT (Foundation for Science and Technology, Portugal) and FEDER (European Regional Development Fund) under Programme PT2020 for financial support to CIMO (UIDB/00690/2020). We would also like to thank the scientific collaboration under Base- UIDB/50020/2020 and Programmatic- UIDP/50020/2020 funding of LSRE-LCM, and LA/P/0045/2020 funding of ALiCE, funded by national funds through FCT and MCTES (Ministério da Ciência, Tecnologia e Ensino Superior, Portugal) by PIDDAC (Programa de Investimentos e Despesas de Desenvolvimento da Administraç˜ao Central, Portugal). Fernanda F. Roman and Adriano dos Santos Silva acknowledge the national funding by FCT and MIT (Massachusetts Institute of Technology, USA), and the ESF (European Social Fund) for individual research grants with reference numbers of SFRH/BD/143224/2019 and SFRH/BD/151346/2021, respectively.
  • The presence of heavy metals in the environment as a consequence of human activity is an issue that has caught the attention of researchers to find wastewater treatment solutions, such as adsorption. In this work, hydrochars and activated carbon microspheres are prepared from tangerine peels as carbon precursor and FeCl3 as activating and structure-directing agent in the hydrothermal carbonization, allowing to obtain hydrochar microspheres ranging from 50 to 3615 nm. In addition, a pyrochar was prepared by pyrolysis of the same precursor. The activated carbon shows the highest surface area (SBET up to 287 m2 g–1), but the basicity of the pyrochar (1.83 mmol g-1, SBET = 104 m2 g–1) was determinant in the adsorption of Ni, being considered the carbon-based material with the highest uptake capacity of Ni. Isotherm and kinetic adsorption of Ni on the most representative activated carbon microsphere, pyrochar and hydrochar microsphere are assessed by 10 and 7 models, respectively.

publication date

  • January 1, 2022