Tailoring innovative adsorbents from discarded weathered basalt waste by calcination and activated carbon impregnation for efficient Fe (III) and Zn (II) remediation

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
Ahmed M. Zayed, Modather F. Hussein, Bahaa S. Metwally, Hussain Shendy, Mahmoud M. Abdelsatar, Mahmoud F. Mubarak, Atef Mohamed Gad Mohamed, Ahmed M. A. Abdel Gawad, Mahmoud S. M. Abdel Wahed, Mostafa A. Masoud
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Abstract

This study explores the potential of utilizing weathered basalt waste, discarded from basalt stone quarrying, as a resource for producing efficient adsorbents to remove Fe (III) and Zn (II) from aqueous and real wastewater. Raw weathered basalt (RWB), and its calcined derivatives at 750 °C for 3 h (CWB-750) and at 950 °C for 1 h with activated carbon impregnation (CWB/AC-950), were prepared and characterized. Characterization using XRD, FTIR, SEM, and surface area analyzer revealed that calcination improved porosity and surface area with some privilege for CWB/AC-950. CWB/AC-950 revealed remarkable removal efficiency for Fe (III) at a pH value of 5, achieving 98.30%, closely matching that of RWB (98.00%), and outperforming CWB-750 (96.20%). In contrast, RWB exhibited the highest removal capacity for Zn (II) at a pH value of 6, with an efficiency of 55%, surpassing both CWB-750 and CWB/AC-950, which achieved approximately 36%. For both contaminants, Pseudo-2nd-order equation (R2 > 0.98) provided a superior fit, showcasing favorable sorption process by all the addressed materials. The Fe(III) sorption data for all the investigated materials were better described by the Freundlich (FL) model compared to the Langmuir (LM) model. Similarly, the Zn(II) sorption data for the calcined derivatives (CWB-750 and CWB/AC-950) were well-explained by the FL model. These findings are supported by the very high determination coefficients (R2 > 0.96) and significantly lower average relative error (ARE) values (8.66 and 13.69) compared to those obtained from the LM model (55.99 and 189.25, orderly). In contrast, for RWB, despite the very high R2 values (> 0.98) for both models, neither adequately captured the Zn(II) sorption behavior, as evidenced by the exceptionally high ARE values (52.67 and 161.19 for LM and FL, respectively). These findings are supported by the very high determination coefficients (R2 > 0.96) and significantly lower average relative error (ARE) values compared to those obtained from the LM model. In contrast, for RWB, despite the very high R2 values (> 0.98) for both models, neither adequately captured the Zn(II) sorption behavior, as evidenced by the exceptionally high ARE values (52.67 and 161.19 for LM and FL, respectively). The remediation mechanism of both Fe (III) and Zn (II) by all adsorbents was not exclusively governed by inter-particle diffusion. Eventually, these findings highlight the sustainable potential of repurposing RWB waste and its calcined derivatives for water remediation applications.

通过煅烧和活性炭浸渍,从废弃的风化玄武岩废物中定制创新吸附剂,用于高效的铁(III)和锌(II)修复
本研究探索了利用从玄武岩采石中丢弃的风化玄武岩废物作为生产有效吸附剂的潜力,以去除水中和实际废水中的铁(III)和锌(II)。采用活性炭浸渍法(CWB/AC-950),在750℃下焙烧3 h (CWB-750)和950℃下焙烧1 h (CWB/AC-950)制备了原生风化玄武岩(RWB)及其衍生物,并对其进行了表征。通过XRD、FTIR、SEM和比表面积分析仪的表征表明,煅烧改善了CWB/AC-950的孔隙率和比表面积,并具有一定的优势。在pH = 5时,CWB/AC-950对Fe (III)的去除率达到了98.30%,与RWB(98.00%)接近,优于CWB-750(96.20%)。相比之下,RWB在pH值为6时对Zn (II)的去除率最高,达到55%,超过了CWB-750和CWB/AC-950的约36%。对于这两种污染物,伪二阶方程(R2 > 0.98)提供了优越的拟合,显示了所有处理材料的良好吸附过程。与Langmuir (LM)模型相比,Freundlich (FL)模型更好地描述了所有研究材料的Fe(III)吸附数据。同样,焙烧衍生物(CWB-750和CWB/AC-950)的Zn(II)吸附数据也可以用FL模型很好地解释。这些发现得到了非常高的决定系数(R2 > 0.96)和显著低于LM模型(55.99和189.25,有序)的平均相对误差(are)值(8.66和13.69)的支持。相比之下,对于RWB,尽管两种模型的R2值都非常高(> 0.98),但都没有充分捕获Zn(II)的吸附行为,这可以从LM和FL的异常高的ARE值(分别为52.67和161.19)中得到证明。与LM模型获得的结果相比,这些发现得到了非常高的决定系数(R2 > 0.96)和显着降低的平均相对误差(are)值的支持。相比之下,对于RWB,尽管两种模型的R2值都非常高(> 0.98),但都没有充分捕获Zn(II)的吸附行为,这可以从LM和FL的异常高的ARE值(分别为52.67和161.19)中得到证明。所有吸附剂对Fe (III)和Zn (II)的修复机制并不完全受颗粒间扩散的支配。最后,这些发现强调了将RWB废物及其煅烧衍生物重新利用于水修复应用的可持续潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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