Magnetic covalent organic frameworks (mcofs): A sustainable solution for emerging organic contaminants (Eocs) from the river

The dataset presented in this study comprises the comprehensive experimental results obtained from the synthesis and characterization of MCOFs. This data forms the empirical foundation for all findings and conclusions discussed in this paper. It includes quantitative metrics such as yields, purity percentages, and final concentrations for each MCOF sample. Additionally, it contains raw and processed data points derived from the HPLC analysis, including retention times and peak areas, which serve as direct evidence for the separation and quantification of the compounds. The purpose of this dataset is to provide a complete and transparent record of the experimental work, enabling validation and future research. Under optimal conditions (pH 7, 100 mg adsorbent dosage, and 25-minute contact time), the MCOFs exhibited exceptional adsorption performance, with removal efficiencies of 90.0 % for DMP, 86.0 % for DBP, and 92.0 % for BPA. The developed analytical method achieved low detection limits (LODs) of 0.0058 mg/L for DMP, 0.0079 mg/L for DBP, and 0.0063 mg/L for BPA, indicating high sensitivity for trace-level contaminant detection in real water samples. Furthermore, the adsorbent demonstrated exceptional reusability, maintaining high performance after fifteen adsorption-desorption cycles, which is a significant improvement over conventional adsorbents.

Methods
The data for this study was meticulously collected and processed to ensure the accuracy and reproducibility of the results. Raw data was obtained through High-Performance Liquid Chromatography (HPLC) analysis, a technique employed to separate and quantify the individual components of the synthesized MCOFs. The specific parameters of the HPLC system were optimized for the chemical properties of the compounds under investigation, including solvent gradient, flow rate, and column temperature. Following the acquisition of the raw chromatographic data, all subsequent calculations and data processing were performed using Microsoft Excel. This included the determination of peak areas and retention times, which were then used to calculate yields, purity, and concentration of the MCOF samples. A series of standard calibration curves were generated within the Excel environment to ensure accurate quantification of the target analytes. Furthermore, statistical analyses and data visualization, such as the generation of graphs and charts, were also carried out in Excel to support the findings and conclusions presented in this paper.