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From Surface Area to Functionality: Data-Driven Insights into MIL-100(Fe) Synthesis for Enhanced Dye Removal Efficiency
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DOI：10.1039/D5NR04817F.png)
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Abstract
En 中文
Traditional MOF design often maximizes generic metrics such as BET surface area and crystallinity; assuming they universally predict performance. We present a machine-learning-guided optimization framework for MIL-100(Fe) from experimentally synthesized samples. All materials were synthesized via an acid-free; water-based hydrothermal route. We trained small-data ML models to link synthesis parameters; including temperature; time; metal-to-ligand molar ratio; and ion concentration; to key properties comprising surface area; total pore volume; average crystallite size; crystallinity; yield; and methylene blue (MB) removal. SHAP analysis showed that time and metal-to-ligand molar ratio dominated dye removal; whereas surface area was more sensitive to temperature and time. The most accurate model; Gaussian process regression was coupled with a genetic algorithm (GA) to optimize synthesis for property-specific targets. Through optimization; the BET-optimized sample increased surface area from the highest baseline value in the initial experimental dataset; 1748 to 1841.9 m²/g; corresponding to a 5.37% relative increase. The MB-optimized sample increased MB removal from the highest baseline value in the initial experimental dataset; 88.6% to 98.3%; corresponding to a 9.7 percentage-point improvement and a 10.9% relative increase. The MB removal optimized sample with a surface area of 1274.3 m²/g and 17.4% crystallinity; delivers the highest MB uptake of 98.3%; corresponding to the highest adsorption capacity under the benchmark test conditions. While; optimized sample for surface area; reaches 1841.9 m²/g (about 44% higher) with 34.9% crystallinity but achieves only 85.1% removal; about 13% lower than optimized MB sample; indicating that even substantial increases in surface area do not govern adsorption performance. A qualitative t-SNE embedding of the descriptor space shows that the optimized samples occupy distinct neighborhoods; elucidating that MOF synthesis should be tailored to the target application rather than a single metric such as surface area.
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