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Advances in lignocellulosic feedstocks for bioenergy and bioproducts
by
Jiang, Xiao
, Lan, Kai
, Wang, Jack P.
, Sulis, Daniel B.
, Sederoff, Heike
, Lavoine, Nathalie
, Barrangou, Rodolphe
, Cofre-Vega, Carlos
, Marques, Barbara M.
in
13
/ 631/208/2491
/ 631/449/711
/ 631/61/447
/ 631/61/54
/ Alternative energy sources
/ Artificial intelligence
/ Biodiesel fuels
/ Biofuels
/ Biomass
/ Biomass energy
/ Biomaterials
/ Biomedical materials
/ Bioprocessing
/ Biotechnology
/ Biotechnology - methods
/ Carbon
/ Cellulose
/ Climate Change
/ CRISPR
/ Editing
/ Efficiency
/ Emissions
/ Energy consumption
/ Energy conversion efficiency
/ Environmental impact
/ Food
/ Fossil fuels
/ Gene Editing
/ Genome editing
/ Humanities and Social Sciences
/ Industrial applications
/ Learning algorithms
/ Lignin
/ Lignin - chemistry
/ Lignin - metabolism
/ Lignocellulose
/ Machine Learning
/ multidisciplinary
/ Nanomaterials
/ Quantum dots
/ Raw materials
/ Renewable energy
/ Renewable resources
/ Review
/ Review Article
/ Science
/ Science (multidisciplinary)
/ Sustainable energy
/ Textiles
2025
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Advances in lignocellulosic feedstocks for bioenergy and bioproducts
by
Jiang, Xiao
, Lan, Kai
, Wang, Jack P.
, Sulis, Daniel B.
, Sederoff, Heike
, Lavoine, Nathalie
, Barrangou, Rodolphe
, Cofre-Vega, Carlos
, Marques, Barbara M.
in
13
/ 631/208/2491
/ 631/449/711
/ 631/61/447
/ 631/61/54
/ Alternative energy sources
/ Artificial intelligence
/ Biodiesel fuels
/ Biofuels
/ Biomass
/ Biomass energy
/ Biomaterials
/ Biomedical materials
/ Bioprocessing
/ Biotechnology
/ Biotechnology - methods
/ Carbon
/ Cellulose
/ Climate Change
/ CRISPR
/ Editing
/ Efficiency
/ Emissions
/ Energy consumption
/ Energy conversion efficiency
/ Environmental impact
/ Food
/ Fossil fuels
/ Gene Editing
/ Genome editing
/ Humanities and Social Sciences
/ Industrial applications
/ Learning algorithms
/ Lignin
/ Lignin - chemistry
/ Lignin - metabolism
/ Lignocellulose
/ Machine Learning
/ multidisciplinary
/ Nanomaterials
/ Quantum dots
/ Raw materials
/ Renewable energy
/ Renewable resources
/ Review
/ Review Article
/ Science
/ Science (multidisciplinary)
/ Sustainable energy
/ Textiles
2025
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Advances in lignocellulosic feedstocks for bioenergy and bioproducts
by
Jiang, Xiao
, Lan, Kai
, Wang, Jack P.
, Sulis, Daniel B.
, Sederoff, Heike
, Lavoine, Nathalie
, Barrangou, Rodolphe
, Cofre-Vega, Carlos
, Marques, Barbara M.
in
13
/ 631/208/2491
/ 631/449/711
/ 631/61/447
/ 631/61/54
/ Alternative energy sources
/ Artificial intelligence
/ Biodiesel fuels
/ Biofuels
/ Biomass
/ Biomass energy
/ Biomaterials
/ Biomedical materials
/ Bioprocessing
/ Biotechnology
/ Biotechnology - methods
/ Carbon
/ Cellulose
/ Climate Change
/ CRISPR
/ Editing
/ Efficiency
/ Emissions
/ Energy consumption
/ Energy conversion efficiency
/ Environmental impact
/ Food
/ Fossil fuels
/ Gene Editing
/ Genome editing
/ Humanities and Social Sciences
/ Industrial applications
/ Learning algorithms
/ Lignin
/ Lignin - chemistry
/ Lignin - metabolism
/ Lignocellulose
/ Machine Learning
/ multidisciplinary
/ Nanomaterials
/ Quantum dots
/ Raw materials
/ Renewable energy
/ Renewable resources
/ Review
/ Review Article
/ Science
/ Science (multidisciplinary)
/ Sustainable energy
/ Textiles
2025
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Advances in lignocellulosic feedstocks for bioenergy and bioproducts
Journal Article
Advances in lignocellulosic feedstocks for bioenergy and bioproducts
2025
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Overview
Lignocellulose, an abundant renewable resource, presents a promising alternative for sustainable energy and industrial applications. However, large-scale adoption of lignocellulosic feedstocks faces considerable obstacles, including scalability, bioprocessing efficiency, and resilience to climate change. This Review examines current efforts and future opportunities for leveraging lignocellulosic feedstocks in bio-based energy and products, with a focus on enhancing conversion efficiency and scalability. It also explores emerging biotechnologies such as CRISPR-based genome editing informed by machine learning, aimed at improving feedstock traits and reducing the environmental impact of fossil fuel dependence.
Lignocellulose is a promising feedstock to produce bioenergy and biomaterials. Here, the authors review current efforts, including genome editing informed by machine learning, for lignocellulosic feedstock-based bioenergy and biomaterials production and provide outlook for improving feedstock traits.
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