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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.
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Coupling liquid phases in 3D condensates and 2D membranes: Successes, challenges, and tools
This review describes the major experimental challenges researchers meet when attempting to couple phase separation between membranes and condensates. Although it is well known that phase separation in a 2D membrane could affect molecules capable of forming a 3D condensate (and vice versa), few researchers have quantified the effects to date. The scarcity of these measurements is not due to a lack of intense interest or effort in the field. Rather, it reflects significant experimental challenges in manipulating coupled membranes and condensates to yield quantitative values. These challenges transcend many molecular details, which means they impact a wide range of systems. This review highlights recent exciting successes in the field, and it lays out a comprehensive list of tools that address potential pitfalls for researchers who are considering coupling membranes with condensates.
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- PAR ID:
- 10516086
- Publisher / Repository:
- Biophysical Journal
- Date Published:
- Journal Name:
- Biophysical Journal
- Volume:
- 123
- Issue:
- 11
- ISSN:
- 0006-3495
- Page Range / eLocation ID:
- 1329 to 1341
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.bpj.2023.12.023
