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Optimizing multi-step polymer film manufacturing involves complex non-linear factor interactions and time-varying process-history variations. To address these challenges under strict proprietary constraints, this work proposes a pragmatic, intelligent modeling framework integrating machine learning (ML) using a small dataset which are designed by Design of Experiments (DoE) approach.
1 Introduction Scientific documents usually contain large amounts of domain knowledge and information. In the chemistry domain, molecule structure plays an important role in conveying composition and spatial arrangement information [1]. Chemical structure diagrams, where atoms are represented by chemical symbols and bonds typically represented by line segments, are widely used in chemistry documents to describe molecule structures.
We describe a physical mechanism capable of achieving simultaneous mixing and focused enrichment in hydrothermal pore microenvironments. Microscale chaotic advection established in response to a temperature gradient paradoxically promotes bulk homogenization of molecular species, while at the same time transporting species to discrete targeted locations on the bounding sidewalls where they become highly enriched.
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