Abstract
Single-atom catalysts (SACs) offer significant potential across various applications, yet our understanding of their formation mechanism remains limited. Notably, the pyrolysis of zeolitic imidazolate frameworks (ZIFs) stands as a pivotal avenue for SAC synthesis, of which the mechanism can be assessed through infrared (IR) spectroscopy. However, the prevailing analysis techniques still rely on manual interpretation. Here, we report a machine learning (ML)-driven analysis of the IR spectroscopy to unravel the pyrolysis process of Pt-doped ZIF-67 to synthesize Pt-Co3O4 SAC. Demonstrating a total Pearson correlation exceeding 0.7 with experimental data, the algorithm provides correlation coefficients for the selected structures, thereby confirming crucial structural changes with time and temperature, including the decomposition of ZIF and formation of Pt-O bonds. These findings reveal and confirm the formation mechanism of SACs. As demonstrated, the integration of ML algorithms, theoretical simulations, and experimental spectral analysis introduces an approach to deciphering experimental characterization data, implying its potential for broader adoption. © 2023 American Chemical Society.
| Original language | English |
|---|---|
| Pages (from-to) | 11058-11062 |
| Number of pages | 5 |
| Journal | Journal of Physical Chemistry Letters |
| Volume | 14 |
| Issue number | 49 |
| Online published | 4 Dec 2023 |
| DOIs | |
| Publication status | Published - 14 Dec 2023 |
Funding
The authors acknowledge financial support by the Australian Research Council (DE240100661, DP230102027, and FT190100636). Computations were undertaken with resources from Phoenix High Performance Computing, which is supported by The University of Adelaide. Haobo Li, Zhen Zhang, and Xinyu Li acknowledge the financial support from the Center for Augmented Reasoning, Australian Institute for Machine Learning.