| 초록 |
The key challenge in charting the synthesis-morphology-property relationship of polymeric and composite materials lies in the long-standing gap of nanoscopic morphology analysis. To bridge this gap, I integrated recent advancements in three fields: 3D electron tomography, quantitative morphometry, and machine learning. I studied a model system, polyamide membrane and prepared diverse nanoscale structure by varying a multitude of synthesis parameters. Quantitative morphometry extracted large datasets of 3D geometry descriptors. A machine learning was employed to rank the descriptors from the most to least informative to composite functionality. This newly created knowledge on nano-morphological properties was related back to bridge synthesis and functionality. The elucidation of the molecular underpinning of the synthesis–morphology–property relationship would enable a new prediction-based design of polymeric materials, advancing beyond previous “trial-and-error” approaches. |
