Removal of Cr(VI) from aqueous solution using ball mill modified biochar: multivariate modeling, optimization and experimental study

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Abstract

Chromium (Cr(VI)) pollution has attracted wide attention due to its high toxicity and carcinogenicity. Modified biochar has been widely used in the removal of Cr(VI) in water as an efficient and green adsorbent. However, the existing biochar prepared by chemical modification is usually complicated in process, high in cost, and has secondary pollution, which limits its application. It is urgent to explore modified biochar with simple process, low cost and environmental friendliness. Therefore, ball milling wheat straw biochar (BM-WB) was prepared by ball milling technology in this paper. The adsorption characteristics and mechanism of Cr(VI) removal by BM-WB were analyzed by functional group characterization, adsorption model and response surface method. The results showed that ball milling effectively reduced the particle size of biochar, increased the specific surface area, and more importantly, enhanced the content of oxygen-containing functional groups on the surface of biochar. After ball milling, the adsorption capacity of Cr(VI) increased by 3.5–9.1 times, and the adsorption capacity reached 52.21 mg/g. The adsorption behavior of Cr(VI) follows the pseudo-second-order kinetics and Langmuir isotherm adsorption model rate. Moreover, the Cr(VI) adsorption process of BM-WB is endothermic and spontaneous. Under the optimized conditions of pH 2, temperature 45 °C, and adsorbent dosage 0.1 g, the removal rate of Cr(VI) in the solution can reach 100%. The mechanism of Cr(VI) adsorption by BM-WB is mainly based on electrostatic attraction, redox and complexation. Therefore, ball milled biochar is a cheap, simple and efficient Cr(VI) removal material, which has a good application prospect in the field of remediation of Cr(VI) pollution in water.

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Tan, Y., Wang, J., Zhan, L., Yang, H., & Gong, Y. (2024). Removal of Cr(VI) from aqueous solution using ball mill modified biochar: multivariate modeling, optimization and experimental study. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-55520-9

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