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Natural deep eutectic solvents for lignocellulosic biomass pretreatment: Recent developments, challenges and novel opportunities
Open accessAbstract
Conversion of lignocellulosic biomass to fuels and chemicals has attracted immense research and development around the world. Lowering recalcitrance of biomass in a cost-effective manner is a challenge to commercialize biomass-based technologies. Deep eutectic solvents (DESs) are new 'green' solvents that have a high potential for biomass processing because of their low cost, low toxicity, biodegradability, easy recycling and reuse. This article discusses the properties of DESs and recent advances in their application for lignocellulosic biomass processing. The effectiveness of DESs in hydrolyzing lignin-carbohydrate complexes, removing lignin/hemicellulose from biomass as well as their effect on biomass deconstruction, crystallinity and enzymatic digestibility have been discussed. Moreover, this review presents recent findings on the compatibility of natural DESs with enzymes and microorganisms.
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Metadata
- Authors
- Satlewal, A., Agrawal, R., Bhagia, S., Sangoro, J., & Ragauskas, A. J.
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 36
- Issue
- 8
- Pages
- 2032-2050
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W2892148100
- Cited by
- 552
- References
- 10
- FWCI
- 15.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 352 d)
- Funders
- Indian Oil R&D Centre
- Department of Biotechnology
- Indo-U.S. Science and Technology Forum
- UT (DE-AC05-00OR22725)
- U.S. Department of Energy (FWP ERKP752)
- Biological and Environmental Research
- National Science Foundation
- Division of Chemistry (CHE-1753282)
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.