Flow chemistry, in-situ analytics, and machine learning for nanoparticle synthesis.

Bin Pan*, Allison P. Forsberg*, Ricki Chairil, Majed S. Madani, Susan E. Habas, Frederick G. Baddour, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

A mild continuous-flow synthesis of α-MoC₁₋ₓ nanoparticles from Mo(CO)₆, coupled with in-line spectroscopic monitoring and machine learning analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron model deconvolutes complex, nonlinear UV-vis spectra to reveal a two-step pathway—precursor conversion to an amorphous intermediate followed by intraparticle crystallization—with the first step being rate limiting. Ex situ SAXS and XRD validate the predicted concentration profiles, paving the way for self-driving, flow-based platforms for nanoparticle synthesis.

Bin Pan*, Majed S. Madani*, Allison P. Forsberg, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

Machine learning-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations in ionic liquid solvents using flow chemistry.

Majed S. Madani, Bin Pan, Allison P. Forsberg, Brendan Ward-O'Brien, Richard L. Brutchey, and Noah Malmstadt

Real-time characterization of Pt nanoparticle nucleation and growth mechanisms in ionic liquid solvents using synchrotron-based in situ small-angle X-ray scattering (SAXS) integrated with a continuous flow synthesis platform.