Green Synthesis of Fe2O3/PEI/MIL-53(Al) from Upcycled Linkers for Organophosphate Antiscalant Cleanup

IF 9 Q2 ENGINEERING, CHEMICAL

ACS Engineering Au Pub Date : 2026-10-21 Epub Date: 2026-08-27 DOI:10.1021/acsengineeringau.6c00027

Jirawan Jindakaew, Chalita Ratanatawanate, Abdelhamid Elaissari, Erwann Jeanneau, Ruben Vera, Pakorn Opaprakasit, Chariya Kaewsaneha, Xiao Hu, Hidetoshi Sekiguchi, Mantana Opaprakasit

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Abstract

An environmentally friendly magnetic metal–organic framework (MMOF) based on MIL-53(Al) was synthesized using terephthalic acid (TPA) recovered from polyethylene terephthalate (PET) waste as the organic linker. The Fe₂O₃@MIL-53(Al) adsorbent was fabricated through a layer-by-layer assembly, with polyethylenimine (PEI) bridging the Fe₂O₃ core (∼200 nm) and MIL-53(Al) shell (∼25 nm). A uniform MOF layer was achieved in a single cycle under green synthesis conditions with PEI concentrations ≥ 10 g/L. The MMOF efficiently removed organophosphate pollutants, particularly 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC), a common scale and corrosion inhibitor. Adsorption kinetics across a pH range of 3–10.5 closely match the pseudo-second-order kinetic model, indicating chemisorption. The adsorption capacity ranged from 250 to 460 mg/g, with a maximum surface-normalized capacity of 0.44 mg/m² at pH 5.5, attributed to favorable electrostatic interactions. Adsorption performance was strongly influenced by both the MMOF zeta potential and the ionization state of PBTC. The well-fitted adsorption isotherms (R² > 0.9) to the Freundlich, Temkin, and Henry models indicate heterogeneous surface adsorption mechanisms. Electrostatic attraction, hydrogen bonding, and surface complexation are proposed as the dominant mechanisms. Overall, the Fe₂O₃/PEI/MIL-53(Al) composite exhibits high adsorption capacity, broad pH stability, and low toxicity, highlighting its promise for efficient PBTC antiscalant removal applications.

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绿色合成Fe2O3/PEI/MIL-53（Al）的研究

以聚对苯二甲酸乙二醇酯（PET）废料中回收的对苯二甲酸（TPA）为有机连接剂，合成了基于MIL-53（Al）的环保型磁性金属有机骨架（MMOF）。通过层层组装制备Fe2O3@MIL-53（Al）吸附剂，聚乙烯亚胺（PEI）桥接Fe2O3核心（~ 200 nm）和MIL-53（Al）壳（~ 25 nm）。在绿色合成条件下，PEI浓度≥10 g/L，单循环即可得到均匀的MOF层。MMOF有效地去除了有机磷污染物，特别是2-膦丁烷-1,2,4-三羧酸（PBTC），一种常见的阻垢缓蚀剂。在3 ~ 10.5的apH范围内，吸附动力学符合拟二级动力学模型，为化学吸附。由于良好的静电相互作用，吸附容量在250 ~ 460 mg/g之间，pH为5.5时的最大表面标准化容量为0.44mg/m2。吸附性能受MMOF zeta电位和PBTC的电离状态的强烈影响。Freundlich、Temkin和Henry模型的吸附等温线（R2 > 0.9）拟合良好，表明了非均质表面吸附机制。静电吸引、氢键和表面络合被认为是主要的机制。总体而言，Fe2O3/PEI/MIL-53（Al）复合材料具有高吸附能力，广泛的pH稳定性和低毒性，突出了其高效去除PBTC抗垢剂的前景。

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

ACS Engineering Au 化学工程技术-

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期刊介绍：）ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)