| 학회 |
한국고분자학회 |
| 학술대회 |
2022년 봄 (04/06 ~ 04/08, 대전컨벤션센터) |
| 권호 |
47권 1호 |
| 발표분야 |
보다 정밀한, 보다 정확한 고분자 소재 분석 기술 |
| 제목 |
Informed Morphogenesis of Polymeric Materials Enabled by 3D Nanoscale Imaging-Analysis Platform |
| 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 prepared diverse nanoscale structure by varying a multitude of synthesis parameters. Quantitative morphometry extracted large datasets of 3D geometry descriptors (total 49). 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. |
| 저자 |
안효성 |
| 소속 |
전남대 |
| 키워드 |
electron tomography; machine-learning; polyamide membranes
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| E-Mail |
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