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Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a

Bin Pan1, Majed S Madani1,2, Allison P Forsberg3

  • 1Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, California 90089-1211, United States.

ACS Nano
|September 5, 2024
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This summary is machine-generated.

Machine learning analyzes UV-vis data to determine platinum nanoparticle (Pt NP) concentrations from flow chemistry. This method reveals how different ionic liquid solvents impact Pt NP yields, aiding sustainable synthesis development.

Area of Science:

  • Nanomaterials Synthesis
  • Chemical Engineering
  • Machine Learning Applications

Background:

  • Colloidal platinum nanoparticles (Pt NPs) have diverse technological applications.
  • Sustainable synthesis of Pt NPs via polyol reduction in ionic liquids (ILs) is promising but lacks kinetic data.
  • Direct in-line analysis of Pt NPs using UV-vis spectrophotometry is challenging due to their optical properties.

Purpose of the Study:

  • To develop a machine learning (ML)-based approach for analyzing in-line UV-vis spectrophotometry data to quantify Pt NP concentrations.
  • To investigate the kinetics of Pt NP synthesis in two structurally similar ionic liquid solvents using a flow reactor.
  • To understand how subtle differences in IL molecular structures influence Pt NP reaction yields.

Main Methods:

Keywords:
flow chemistryionic liquidsmachine learningplatinum nanoparticlespolyol reductionreaction kineticsspectrophotometry

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  • Utilized a benchtop flow reactor for in-line UV-vis spectrophotometry.
  • Developed and applied a machine learning model to interpret UV-vis data and determine Pt NP concentrations.
  • Investigated Pt NP yield as a function of residence time in 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf) IL solvents.

Main Results:

  • The ML approach accurately determined Pt NP concentrations with prediction errors as low as 4%, validated by particle size analysis.
  • Significantly different Pt NP yields were observed between the two IL solvents, despite their structural similarity.
  • Demonstrated the feasibility of using ML-interpreted in-line analysis to study solvent effects on NP synthesis kinetics.

Conclusions:

  • The ML-based in-line analysis enables kinetic investigation of Pt NP synthesis in ILs, overcoming limitations of direct UV-vis spectrophotometry.
  • Subtle structural variations in ionic liquids can lead to substantial differences in polyol reduction yields for Pt NPs.
  • This generalizable approach can provide insights into various reaction outcomes influenced by solvent effects, including yield, reaction order, and rate coefficients.