JOURNAL ARTICLE
Biofabrication of rhamnolipid biosurfactant for nanoparticle stabilization and chitosan immobilized lipase: A green detergent additive.
Published In: Journal of Surfactants & Detergents, 2025, v. 28, n. 3. P. 571 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sharma, Priyanka; Debnath, Mousumi 3 of 3
Abstract
Biosurfactants prevent agglomeration of nanoparticles by reducing the surface tension and offer stability over time by forming a stable layer on the surface of nanoparticles. The current study focuses on the biosynthesis of silver nanoparticles (SNP) coated with a rhamnolipid biosurfactant (BS) and its use as an additive in fabric cleaning detergent. Rhamnolipids were extracted from a Pseudomonas aeruginosa strain, Pa84, that was isolated from a halophilic environment, Sambhar Salt Lake, Rajasthan, India. The reduction of silver ions was achieved by the rhamnolipid‐coated SNP (BS‐SNP). BS, SNP, and BS‐SNP demonstrated antibacterial efficacy against a variety of microorganisms. Lipase, present in the crude biosurfactant, was immobilized on modified chitosan microbeads (Ch‐BS‐SNP) and used for washing fabrics. The conjugate was found to be effective as a laundry detergent additive. The immobilized lipase showed high relative activity ranging from 66% to 110% and performed better than free lipase or standards. Our results highlight a potential claim for a commercially viable laundry detergent additive. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Surfactants & Detergents. 2025/05, Vol. 28, Issue 3, p571
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:1097-3958
- DOI:10.1002/jsde.12824
- Accession Number:185103381
- Copyright Statement:Copyright of Journal of Surfactants & Detergents is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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