Bio-waste derived dialdehyde cellulose ethers as supports for alpha-chymotrypsin immobilization
- Authors
- Kumari, Sapana; Chauhan, Ghanshyam S.; Ahn, Jou-Hyeon; Reddy, N. S.
- Issue Date
- Apr-2016
- Publisher
- Elsevier BV
- Keywords
- alpha-Chymotrypsin immobilization; Reusability and storage stability; Artificial neural network model
- Citation
- International Journal of Biological Macromolecules, v.85, pp 227 - 237
- Pages
- 11
- Indexed
- SCI
SCIE
SCOPUS
- Journal Title
- International Journal of Biological Macromolecules
- Volume
- 85
- Start Page
- 227
- End Page
- 237
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/15591
- DOI
- 10.1016/j.ijbiomac.2015.12.063
- ISSN
- 0141-8130
1879-0003
- Abstract
- Enzyme immobilization is an important technique to enhance stability, storability and reusability of enzymes. In the present work, pine needles, a forest bio-waste, were used as a feedstock of cellulose to synthesize new materials as supports for immobilization of alpha-chymotrypsin (CT) enzyme. The extracted cellulose from pine needles was etherified with different alkyl bromides (RBr) and etherified products were further modified to dialdehyde via oxidation with NaIO4 to get the desired products, dialdehyde cellulose ethers (R-O-cell-CH=O). CT was then covalently immobilized onto as-synthesized dialdehyde cellulose ethers via Schiff-base formation, i.e., imine linkage. The synthesized products and enzyme immobilization were confirmed by different characterization techniques and the activity assay of the free and the immobilized CT was carried out using standard protocol with variation of different parameters such as temperature, pH and substrate concentration. The storage stability and reusability of the immobilized CT were also investigated. CT activity was also studied in simulated physiological conditions in the artificial gastric fluid and artificial intestinal fluid. Artificial neural network (ANN) model was employed to correlate the relationship with% relative activity and time, temperature and pH affecting enzyme activity. A good correlation of experimental data was predicted by ANN model. (C) 2015 Elsevier B.V. All rights reserved.
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Collections - 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles

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