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Polymeric membranes have become essential for energy-efficient gas separations such as natural gas sweetening, hydrogen separation, and carbon dioxide capture. Polymeric membranes face challenges like permeability-selectivity tradeoffs, plasticization, and physical aging, limiting their broader applicability. Machine learning (ML) techniques are increasingly used to address these challenges. This review covers current ML applications in polymeric gas separation membrane design, focusing on three key components: polymer data, representation methods, and ML algorithms. Exploring diverse polymer datasets related to gas separation, encompassing experimental, computational, and synthetic data, forms the foundation of ML applications. Various polymer representation methods are discussed, ranging from traditional descriptors and fingerprints to deep learning-based embeddings. Furthermore, we examine diverse ML algorithms applied to gas separation polymers. It provides insights into fundamental concepts such as supervised and unsupervised learning, emphasizing their applications in the context of polymer membranes. The review also extends to advanced ML techniques, including data-centric and model-centric methods, aimed at addressing challenges unique to polymer membranes, focusing on accurate screening and inverse design. 

Millions of tons of plastics enter the oceans yearly, and they can be fragmented by ultraviolet and mechanical means into nanoplastics. Here, we report the direct observation of nanoplastics in global ocean water leveraging a unique shrinking surface bubble deposition (SSBD) technique. SSBD involves optically heating plasmonic nanoparticles to form a surface bubble and leveraging the Marangoni flow to concentrate suspended nanoplastics onto the surface, allowing direct visualization using electron microscopy. With the plasmonic nanoparticles co-deposited in SSBD, the surface-enhanced Raman spectroscopy effect is enabled for direct chemical identification of trace amounts of nanoplastics. In the water samples from two oceans, we observed nanoplastics made of nylon, polystyrene, and polyethylene terephthalate—all common in daily consumables. The plastic particles have diverse morphologies, such as nanofibers, nanoflakes, and ball-stick nanostructures. These nanoplastics may profoundly affect marine organisms, and our results can provide critical information for appropriately designing their toxicity studies.