DOE OSTI · 3367189
Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning
Abstract
Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Pan, Bin [University of Southern California, Los Angeles, CA (United States)] (ORCID:0000000343037519), Forsberg, Allison P. [University of Southern California, Los Angeles, CA (United States)], Chairil, Ricki [University of Southern California, Los Angeles, CA (United States)], Madani, Majed S. [King Abdulaziz University, Jeddah (Saudi Arabia)], Habas, Susan E. [National Laboratory of the Rockies (NLR), Golden, CO (United States)] (ORCID:0000000238938454), Baddour, Frederick G. [National Laboratory of the Rockies (NLR), Golden, CO (United States)] (ORCID:0000000258375804), Brutchey, Richard L. [University of Southern California, Los Angeles, CA (United States)], Malmstadt, Noah [University of Southern California, Los Angeles, CA (United States)]. 2026-06-05. Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning. https://doi.org/10.1002/smll.74006
Cite the original work for its findings. Save a collection to share your selection of sources.