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Engineering topics

Wu, Liheng

Publications and source records attributed to Wu, Liheng.

Selective Catalytic Behavior Induced by Crystal–Phase Transformation in Well–Defined Bimetallic Pt–Sn Nanocrystals

The Pt-Sn bimetallic system is a much studied and commercially used catalyst for propane dehydrogenation. The traditionally prepared catalyst, however, suffers from inhomogeneity and phase separation of the active Pt–Sn phase. Colloidal chemistry offers a route for the synthesis of Pt–Sn bimetallic nanoparticles (NPs) in a systematic, well-defined, tailored fashion over conventional methods. Here, the successful synthesis of well-defined ≈2 nm Pt, PtSn, and Pt 3 Sn nanocrystals with distinct crystallographic phases is reported; hexagonal close packing (hcp) PtSn and fcc Pt 3 Sn show different activity and stability depending on the hydrogen-rich or poor environment in the feed. Moreover, face centred cubic (fcc) Pt 3 Sn/Al 2 O 3 , which exhibited the highest stability compared to hcp PtSn, shows a unique phase transformation from an fcc phase to an L1 2 -ordered superlattice. Contrary to PtSn, H 2 cofeeding has no effect on the Pt 3 Sn deactivation rate. Finally, the results reveal structural dependency of the probe reaction, propane dehydrogenation, and provide a fundamental understanding of the structure–performance relationship on emerging bimetallic systems.

36 MATERIALS SCIENCE↗

Utilization of machine learning to accelerate colloidal synthesis and discovery

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗