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From surface area to functionality: data-driven insights into MIL-100(Fe) synthesis for enhanced dye removal

Saeid Zahedi Asl1, Shayan Abaei2, Hosein Alimardani2

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Summary

Machine learning optimizes metal-organic framework (MOF) synthesis for specific applications. Tailoring synthesis parameters, rather than maximizing surface area, significantly improves performance metrics like dye removal.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Traditional metal-organic framework (MOF) design often prioritizes generic metrics like BET surface area and crystallinity, assuming universal performance prediction.
  • This approach may not yield optimal results for specific applications, necessitating a more targeted synthesis strategy.

Purpose of the Study:

  • To develop and apply a machine learning-guided optimization framework for MIL-100(Fe) synthesis.
  • To link synthesis parameters to key material properties and application-specific performance (methylene blue removal).

Main Methods:

  • Experimental synthesis of MIL-100(Fe) using an acid-free, water-based hydrothermal route.
  • Training small-data machine learning models (Gaussian process regression) to correlate synthesis parameters (temperature, time, metal-to-ligand ratio, ion concentration) with material properties (surface area, pore volume, crystallite size, crystallinity, yield) and methylene blue (MB) removal efficiency.
  • Utilizing SHAP analysis to identify key synthesis parameters influencing specific properties.
  • Coupling Gaussian process regression with a genetic algorithm (GA) for property-specific synthesis optimization.

Main Results:

  • SHAP analysis revealed that synthesis time and metal-to-ligand ratio were dominant factors for dye removal, while temperature and time influenced surface area.
  • The optimized MIL-100(Fe) for MB removal achieved 98.3% removal efficiency, a significant improvement over the baseline.
  • The MB removal-optimized sample demonstrated higher adsorption capacity despite a lower surface area and crystallinity compared to the BET-optimized sample, highlighting that surface area alone does not govern performance.
  • Optimized samples for different targets occupied distinct regions in the descriptor space, indicating the need for application-specific tailoring.

Conclusions:

  • Machine learning-guided optimization provides a powerful framework for tailoring MOF synthesis to specific applications.
  • Optimizing MOF synthesis for a target application, such as dye removal, is more effective than maximizing generic metrics like surface area.
  • The study demonstrates that MIL-100(Fe) synthesis can be precisely controlled to achieve desired performance characteristics, moving beyond traditional optimization approaches.