Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity
Growth limitation caused by mutual shading and the high harvest cost hamper algal biofuel production. Here, the authors overcome these two problems by designing a semi-continuous algal cultivation system and an aggregation-based sedimentation strategy to achieve high levels production of biomass and...
Main Authors: | , , , , , , , , |
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Format: | Article |
Language: | English |
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Nature Portfolio
2022-01-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-021-27665-y |
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author | Bin Long Bart Fischer Yining Zeng Zoe Amerigian Qiang Li Henry Bryant Man Li Susie Y. Dai Joshua S. Yuan |
author_facet | Bin Long Bart Fischer Yining Zeng Zoe Amerigian Qiang Li Henry Bryant Man Li Susie Y. Dai Joshua S. Yuan |
author_sort | Bin Long |
collection | DOAJ |
description | Growth limitation caused by mutual shading and the high harvest cost hamper algal biofuel production. Here, the authors overcome these two problems by designing a semi-continuous algal cultivation system and an aggregation-based sedimentation strategy to achieve high levels production of biomass and limonene. |
first_indexed | 2024-12-24T00:43:09Z |
format | Article |
id | doaj.art-6546084f95b845f5957390adaf3c8912 |
institution | Directory Open Access Journal |
issn | 2041-1723 |
language | English |
last_indexed | 2024-12-24T00:43:09Z |
publishDate | 2022-01-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Nature Communications |
spelling | doaj.art-6546084f95b845f5957390adaf3c89122022-12-21T17:23:53ZengNature PortfolioNature Communications2041-17232022-01-0113111110.1038/s41467-021-27665-yMachine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivityBin Long0Bart Fischer1Yining Zeng2Zoe Amerigian3Qiang Li4Henry Bryant5Man Li6Susie Y. Dai7Joshua S. Yuan8Department of Plant Pathology and Microbiology, Texas A&M UniversityDepartment of Agricultural Economics, Texas A&M UniversityRenewable Resources and Enabling Sciences Center, National Renewable Energy LaboratoryDepartment of Plant Pathology and Microbiology, Texas A&M UniversityDepartment of Plant Pathology and Microbiology, Texas A&M UniversityDepartment of Agricultural Economics, Texas A&M UniversityDepartment of Plant Pathology and Microbiology, Texas A&M UniversityDepartment of Plant Pathology and Microbiology, Texas A&M UniversityDepartment of Plant Pathology and Microbiology, Texas A&M UniversityGrowth limitation caused by mutual shading and the high harvest cost hamper algal biofuel production. Here, the authors overcome these two problems by designing a semi-continuous algal cultivation system and an aggregation-based sedimentation strategy to achieve high levels production of biomass and limonene.https://doi.org/10.1038/s41467-021-27665-y |
spellingShingle | Bin Long Bart Fischer Yining Zeng Zoe Amerigian Qiang Li Henry Bryant Man Li Susie Y. Dai Joshua S. Yuan Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity Nature Communications |
title | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_full | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_fullStr | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_full_unstemmed | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_short | Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity |
title_sort | machine learning informed and synthetic biology enabled semi continuous algal cultivation to unleash renewable fuel productivity |
url | https://doi.org/10.1038/s41467-021-27665-y |
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