Fouzia Mashkoor , Mohd Shoeb , Sayed Mohammed Adnan , Javed Alam Khan , Mohammed Ashraf Gondal , Changyoon Jeong
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Machine learning models (ANN, NLR, and SVR) were employed to infer Sb(III) adsorption efficacy, with the ANN model showing superior predictive accuracy. High-resolution X-ray photoelectron spectroscopy revealed chemical state evolution of La, Mo, Sb, and O before and after Sb(III) adsorption and thermal activation. The appearance of distinct Sb 3d peaks confirmed successful immobilization, while post-activation spectral shifts indicated partial oxidation to Sb(V), consistent with Sb-O-M (<em>M</em> = La, Mo) coordination and enhanced redox activity. The Sb-adsorbed nanocomposites were repurposed as supercapacitor electrodes, delivering a specific capacitance of 824.44 F/g at 1.5 A/g and surpassing the performance of pristine LaMo/CNT (783.21 F/g). The assembled symmetric device retained 90.2 % capacitance after 15,000 cycles and achieved an energy density of 73.53 Wh/kg at 750 W/kg. 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引用次数: 0
摘要
开发既能解决环境修复问题又能储存能源的多功能材料对于推进可持续技术至关重要。本研究制备了La2Mo4O15/碳纳米管(LaMo/CNT)纳米复合材料,并对其作为Sb(III)去除和高性能超级电容器电极的吸附材料进行了研究。纳米复合材料对Sb(III)的去除效率为88.7%,吸附动力学遵循伪ii级模型。吸附性能受静电吸引、氢键和球内络合等协同作用的支配。采用机器学习模型(ANN, NLR和SVR)来推断Sb(III)的吸附效果,其中ANN模型具有较好的预测准确性。高分辨率x射线光电子能谱揭示了Sb(III)吸附和热活化前后La、Mo、Sb和O的化学状态演变。明显的Sb 3d峰的出现证实了成功的固定化,而激活后的光谱位移表明部分氧化为Sb(V),与Sb- o -M (M = La, Mo)配位和氧化还原活性增强一致。吸附sb的纳米复合材料被重新用作超级电容器电极,在1.5 a /g下提供824.44 F/g的比电容,超过了原始LaMo/CNT (783.21 F/g)的性能。经过15,000次循环后,组装的对称器件保持了90.2%的电容,并在750 W/kg时实现了73.53 Wh/kg的能量密度。这项工作通过整合机器学习、界面化学和环境和能源应用的多功能设计,提出了一个可持续的废物转化为能源的途径。
Molecular-level insight into CNT-decorated La2Mo4O15 nanocomposites for antimony adsorption from aqueous media: Machine learning simulation, interfacial behavior, and recyclability in supercapacitor electrolytic environments
The development of multifunctional materials capable of addressing both environmental remediation and energy storage is essential for advancing sustainable technologies. In this study, La2Mo4O15/carbon nanotube (LaMo/CNT) nanocomposites were prepared and investigated as adsorbent material for Sb(III) removal and high-performance supercapacitor electrodes. The nanocomposites demonstrated removal efficiency of 88.7 % for Sb(III), with adsorption kinetics following a pseudo-II order model. The adsorption performance was governed by synergistic interactions including electrostatic attraction, hydrogen bonding, and inner-sphere complexation. Machine learning models (ANN, NLR, and SVR) were employed to infer Sb(III) adsorption efficacy, with the ANN model showing superior predictive accuracy. High-resolution X-ray photoelectron spectroscopy revealed chemical state evolution of La, Mo, Sb, and O before and after Sb(III) adsorption and thermal activation. The appearance of distinct Sb 3d peaks confirmed successful immobilization, while post-activation spectral shifts indicated partial oxidation to Sb(V), consistent with Sb-O-M (M = La, Mo) coordination and enhanced redox activity. The Sb-adsorbed nanocomposites were repurposed as supercapacitor electrodes, delivering a specific capacitance of 824.44 F/g at 1.5 A/g and surpassing the performance of pristine LaMo/CNT (783.21 F/g). The assembled symmetric device retained 90.2 % capacitance after 15,000 cycles and achieved an energy density of 73.53 Wh/kg at 750 W/kg. This work proposes a sustainable waste-to-energy pathway by integrating machine learning, interfacial chemistry, and multifunctional design for environmental and energy applications.
期刊介绍:
The aim of the journal is to provide a respectful outlet for ''sound science'' papers in all research areas on surfaces and interfaces. We define sound science papers as papers that describe new and well-executed research, but that do not necessarily provide brand new insights or are merely a description of research results.
Surfaces and Interfaces publishes research papers in all fields of surface science which may not always find the right home on first submission to our Elsevier sister journals (Applied Surface, Surface and Coatings Technology, Thin Solid Films)