Effect of Doping TiO 2 NPs with Lanthanides (La, Ce and Eu) on the Adsorption and Photodegradation of Cyanide-A Comparative Study.
Ximena Jaramillo-FierroRicardo LeónPublished in: Nanomaterials (Basel, Switzerland) (2023)
Free cyanide is a highly dangerous compound for health and the environment, so treatment of cyanide-contaminated water is extremely important. In the present study, TiO 2 , La/TiO 2 , Ce/TiO 2 , and Eu/TiO 2 nanoparticles were synthesized to assess their ability to remove free cyanide from aqueous solutions. Nanoparticles synthesized through the sol-gel method were characterized by X-ray powder diffractometry (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transformed infrared spectroscopy (FTIR), diffuse reflectance spectroscopy (DRS), and specific surface area (SSA). Langmuir and Freundlich isotherm models were utilized to fit the adsorption equilibrium experimental data, and pseudo-first-order, pseudo-second-order, and intraparticle diffusion models were used to fit the adsorption kinetics experimental data. Cyanide photodegradation and the effect of reactive oxygen species (ROS) on the photocatalytic process were investigated under simulated solar light. Finally, reuse of the nanoparticles in five consecutive treatment cycles was determined. The results showed that La/TiO 2 has the highest percentage of cyanide removal (98%), followed by Ce/TiO 2 (92%), Eu/TiO 2 (90%), and TiO 2 (88%). From these results, it is suggested that La, Ce, and Eu dopants can improve the properties of TiO 2 as well as its ability to remove cyanide species from aqueous solutions.
Keyphrases
- visible light
- quantum dots
- electron microscopy
- high resolution
- reactive oxygen species
- fluorescent probe
- public health
- healthcare
- cell death
- magnetic resonance
- dna damage
- aqueous solution
- molecular dynamics
- low grade
- computed tomography
- social media
- combination therapy
- risk assessment
- mental health
- reduced graphene oxide
- atomic force microscopy
- high grade
- deep learning
- mass spectrometry
- oxidative stress
- solid phase extraction
- ionic liquid
- human health