The DOI matches its Crossref record; 160 of the 160 references it cites were found in a registry or an index.
Emerging technologies for the production of nanocellulose from lignocellulosic biomass
Open accessAbstract
Nanocellulose is a unique and promising natural nanomaterial and has gained significant attention due to its applications in several important areas. Thus, researchers are continuously looking for the most efficient, sustainable, economically viable, and environmentally friendly production technologies to fulfil its growing demand. Conventional production technologies, which include various physical, chemical, and physicochemical methods, are currently inadequate for this purpose and have several limitations such as long processing time, high energy consumption, low recovery of nanocellulose, and many others. To overcome these shortcomings, scientists have investigated the prospect of utilizing emerging processing technologies such as microwave irradiation, deep eutectic solvent, enzymatic processing, cold plasma, electron beam irradiation, and pulsed electric field in nanocellulose preparation. In general, studies have shown that the application of emerging technologies enhances the extraction yield and properties of nanocellulose. This article presents a review of the most recent works reported on the application of emerging technologies in nanocellulose production.
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Cites
Deep Eutectic Solvents (DESs) and Their Applications
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Isolation and characterization of nanofibers from agricultural residues – Wheat straw and soy hulls
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Recent Trends in the Pretreatment of Lignocellulosic Biomass for Value-Added Products
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Nanocellulose: Extraction and application
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Emerging technologies for the pretreatment of lignocellulosic biomass
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Metadata
- Authors
- Pradhan, D., Jaiswal, A. K., & Jaiswal, S.
- Venue
- Carbohydrate Polymers
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 285
- Pages
- 119258
- ISSN
- 0144-8617 (ISSN-L)
- OpenAlex
- W4213172043
- Cited by
- 294
- References
- 160
- FWCI
- 13.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.