期刊
ACS OMEGA
卷 4, 期 14, 页码 15912-15922出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.9b01978
关键词
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资金
- Australian Research Council [IH140100018]
- ARC Australian Laureate Fellowship [FL170100006]
- Australian Research Council [IH140100018] Funding Source: Australian Research Council
In materials science, the investigation of a large and complex experimental space is time-consuming and thus may induce bias to exclude potential solutions where little to no knowledge is available. This work presents the development of a highly hydrophobic material from an amphiphilic polymer through a novel, adaptive artificial intelligence approach. The hydrophobicity arises from the random packing of short polymer fibers into paper, a highly entropic, multistep process. Using Bayesian optimization, the algorithm is able to efficiently navigate the parameter space without bias, including areas which a human experimenter would not address. This resulted in additional knowledge gain, which can then be applied to the fabrication process, resulting in a highly hydrophobic material (static water contact angle 135 degrees) from an amphiphilic polymer (contact angle of 90 degrees) through a simple and scalable filtration-based method. This presents a potential pathway for surface modification using the short polymer fibers to create fluorine-free hydrophobic surfaces on a larger scale.
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