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Bioplastic design using multitask deep neural networks

Environmental Studies and Forestry

Bioplastic design using multitask deep neural networks

C. Kuenneth, J. Lalonde, et al.

Explore how groundbreaking research by Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone, Carl N. Iverson, Rampi Ramprasad, and Ghanshyam Pilania develops multitask deep neural network predictors that identify promising biodegradable alternatives to non-degradable plastics. This innovative approach could transform our reliance on petroleum-based commodities.

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Playback language: English
Abstract
Non-degradable plastic waste is a major environmental problem. Bio-synthesized and biodegradable alternatives, such as polyhydroxyalkanoates (PHAs), offer a potential solution. This study develops multitask deep neural network property predictors using experimental data for nearly 23,000 PHA homo- and copolymer chemistries. These predictors identified 14 PHA-based bioplastics from a search space of almost 1.4 million candidates as potential replacements for seven petroleum-based commodity plastics (accounting for 75% of global plastic production). Potential synthesis routes for these promising materials are also discussed.
Publisher
Communications Materials
Published On
Dec 03, 2022
Authors
Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone, Carl N. Iverson, Rampi Ramprasad, Ghanshyam Pilania
Tags
biodegradable plastics
polyhydroxyalkanoates
deep neural networks
environmental sustainability
material science
bioplastics

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