Biomining of MoS2 with Peptide-based Smart Biomaterials

Biomining of valuable metals using a target specific approach promises increased purification yields and decreased cost. Target specificity can be implemented with proteins/peptides, the biological molecules, responsible from various structural and functional pathways in living organisms by virtue o...

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Veröffentlicht in:Scientific reports 2018-02, Vol.8 (1), p.1-12, Article 3374
Hauptverfasser: Cetinel, Sibel, Shen, Wei-Zheng, Aminpour, Maral, Bhomkar, Prasanna, Wang, Feng, Borujeny, Elham Rafie, Sharma, Kumakshi, Nayebi, Niloofar, Montemagno, Carlo
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Sprache:eng
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Zusammenfassung:Biomining of valuable metals using a target specific approach promises increased purification yields and decreased cost. Target specificity can be implemented with proteins/peptides, the biological molecules, responsible from various structural and functional pathways in living organisms by virtue of their specific recognition abilities towards both organic and inorganic materials. Phage display libraries are used to identify peptide biomolecules capable of specifically recognizing and binding organic/inorganic materials of interest with high affinities. Using combinatorial approaches, these molecular recognition elements can be converted into smart hybrid biomaterials and harnessed for biotechnological applications. Herein, we used a commercially available phage-display library to identify peptides with specific binding affinity to molybdenite (MoS 2 ) and used them to decorate magnetic NPs. These peptide-coupled NPs could capture MoS 2 under a variety of environmental conditions. The same batch of NPs could be re-used multiple times to harvest MoS 2 , clearly suggesting that this hybrid material was robust and recyclable. The advantages of this smart hybrid biomaterial with respect to its MoS 2 -binding specificity, robust performance under environmentally challenging conditions and its recyclability suggests its potential application in harvesting MoS 2 from tailing ponds and downstream mining processes.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-21692-4