at Google ScholarTitle data
Künneth, Christopher ; Lalonde, Jessica ; Marrone, Babetta L. ; Iverson, Carl N. ; Ramprasad, Rampi ; Pilania, Ghanshyam:
Bioplastic design using multitask deep neural networks.
In: Communications Materials.
Vol. 3
(2022)
Issue 1
.
- 96.
ISSN 2662-4443
DOI: https://doi.org/10.1038/s43246-022-00319-2
Abstract in another language
Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Engineering Science > Juniorprofessur Computational Materials Science > Juniorprofessur Computational Materials Science - Juniorprof. Dr. Christopher Künneth Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Juniorprofessur Computational Materials Science |
| Result of work at the UBT: | No |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 05 May 2023 08:48 |
| Last Modified: | 05 May 2023 08:48 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76169 |