Efficient Adsorption of Methylene Blue by Porous Biochar Derived from Soybean Dreg Using a One-Pot Synthesis Method.
Zhiwei YingXinwei ChenHe LiXinqi LiuChi ZhangJian ZhangGuofu YiPublished in: Molecules (Basel, Switzerland) (2021)
Soybean dreg is a by-product of soybean products production, with a large consumption in China. Low utilization value leads to random discarding, which is one of the important sources of urban pollution. In this work, porous biochar was synthesized using a one-pot method and potassium bicarbonate (KHCO3) with low-cost soybean dreg (SD) powder as the carbon precursor to investigating the adsorption of methylene blue (MB). The prepared samples were characterized with scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analyzer (EA), Brunauer-Emmett-Teller (BET), X-ray diffractometer (XRD), Raman spectroscopy (Raman), Fourier transform infrared spectrometer (FTIR), and X-ray photoelectron spectroscopy (XPS). The obtained SDB-K-3 showed a high specific surface area of 1620 m2 g-1, a large pore volume of 0.7509 cm3 g-1, and an average pore diameter of 1.859 nm. The results indicated that the maximum adsorption capacity of SDB-K-3 to MB could reach 1273.51 mg g-1 at 318 K. The kinetic data were most consistent with the pseudo-second-order model and the adsorption behavior was more suitable for the Langmuir isotherm equation. This study demonstrated that the porous biochar adsorbent can be prepared from soybean dreg by high value utilization, and it could hold significant potential for dye wastewater treatment in the future.
Keyphrases
- electron microscopy
- aqueous solution
- heavy metals
- wastewater treatment
- raman spectroscopy
- low cost
- high resolution
- sewage sludge
- anaerobic digestion
- highly efficient
- antibiotic resistance genes
- risk assessment
- health risk assessment
- photodynamic therapy
- computed tomography
- particulate matter
- plant growth
- organic matter
- electronic health record
- tissue engineering
- single molecule
- microbial community
- artificial intelligence
- machine learning
- deep learning