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Immobilized ligninolytic enzymes: An innovative and environmental responsive technology to tackle dye-based industrial pollutants – A review

Bilal, Muhammad, Asgher, Muhammad, Parra-Saldivar, Roberto, Hu, Hongbo, Wang, Wei, Zhang, Xuehong, Iqbal, Hafiz M.N.·2017·Science of The Total Environment

Abstract

In the twenty-first century, chemical and associated industries quest a transition prototype from traditional chemical-based concepts to a greener, sustainable and environmentally-friendlier catalytic alternative, both at the laboratory and industrial scale. In this context, bio-based catalysis offers numerous benefits along with potential biotechnological and environmental applications. The bio-based catalytic processes are energy efficient than conventional methodologies under moderate processing, generating no and negligible secondary waste pollution. Thanks to key scientific advances, now, solid-phase biocatalysts can be economically tailored on a large scale. Nevertheless, it is mandatory to recover and reprocess the enzyme for their commercial feasibility, and immobilization engineering can efficiently accomplish this challenge. The first part of the present review work briefly outlines the immobilization of lignin-modifying enzymes (LMEs) including lignin peroxidase (LiP), manganese peroxidase (MnP) and laccase of white-rot fungi (WRF). Whereas, in the second part, a particular emphasis has been given on the recent achievements of carrier-immobilized LMEs for the degradation, decolorization, or detoxification of industrial dyes and dye-based industrial wastewater effluents.

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Metadata

Authors
Bilal, M., Asgher, M., Parra-Saldivar, R., Hu, H., Wang, W., Zhang, X., & Iqbal, H. M. N.
Venue
Science of The Total Environment
Publisher
Elsevier BV
Published
2017-01
Volume
576
Pages
646-659
ISSN
0048-9697
Cited by
382
References
3
Language
en
Funders
  • Tecnologico de Monterrey, Mexico
  • State Key Laboratory of Microbial Metabolism
  • School of Life Sciences and Biotechnology, Shanghai Jiao Tong University

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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