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Cost-effective microwave-assisted hydrothermal rapid synthesis of analcime-activated carbon composite from coal gangue used for Pb 2+ adsorption.

Qi LiLiang LvXudong ZhaoYong WangYongzhen Wang
Published in: Environmental science and pollution research international (2022)
Heavy metal contamination of water has brought about serious harm to the ecological environment and also threatens human health to a certain extent. In this study, a composite structure comprised of analcime-activated carbon (ANA-AC) was synthesized in situ via a microwave-assisted hydrothermal method using coal gangue (CG) for the potential treatment of Pb 2+ from aqueous solution. The products were systematically characterized using XRD, SEM, BET, FTIR, and XPS. The results showed that activated carbon was successfully integrated with the structure of the analcime and the BET surface area of the ANA-AC (20.82 m 2 /g) was much greater than that of the CG (9.33 m 2 /g) and ANA (10.04 m 2 /g) independently. The relationship between Pb 2+ adsorption capacity and the initial solution concentration, adsorbent dosages, contact time, pH, and temperature was studied. Under optimal conditions (Pb 2+  = 100 mg/L, dosage = 0.1 g, contact time = 6 h, pH = 5.4-6, temperature = 298 K), the maximum adsorption capacity of ANA-AC can reach 100%, which was higher than that of CG and ANA. The Langmuir isotherm model was in good agreement with the data obtained for Pb 2+ adsorption, and the pseudo-second-order kinetic model was more suitable for describing the experimental data, showing that chemical adsorption was the controlling step during the adsorption process. In summary, analcime-activated carbon composite prepared from coal gangue could be used as an appropriate adsorbent for Pb 2+ adsorption from an aqueous solution.
Keyphrases
  • aqueous solution
  • human health
  • heavy metals
  • risk assessment
  • particulate matter
  • climate change
  • electronic health record
  • big data
  • drinking water
  • machine learning
  • anaerobic digestion
  • mass spectrometry
  • deep learning