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Structural determination of Enzyme-Graphene Nanocomposite Sensor Material

Rai, Durgesh K. and Gurusaran, Manickam and Urban, Volker and Aran, Kiana and Ma, Lulu and Li, Pingzuo and Qian, Shuo and Narayanan, Tharangattu N. and Ajayan, Pulickel M. and Liepmann, Dorian and Sekar, Kanagaraj and Alvarez-Cao, Maria-Efigenia and Escuder-Rodriguez, Juan-Jose and Cerdan, Maria-Esperanza and Gonzalez-Siso, Maria-Isabel and Viswanathan, Sowmya and Paulmurugan, Ramasamy and Renugopalakrishnan, Venkatesan (2019) Structural determination of Enzyme-Graphene Nanocomposite Sensor Material. In: SCIENTIFIC REPORTS, 9 .

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Official URL: http:/dx.doi.org/10.1038/s41598-019-51882-7


State-of-the-art ultra-sensitive blood glucose-monitoring biosensors, based on glucose oxidase (GOx) covalently linked to a single layer graphene (SLG), will be a valuable next generation diagnostic tool for personal glycemic level management. We report here our observations of sensor matrix structure obtained using a multi-physics approach towards analysis of small-angle neutron scattering (SANS) on graphene- based biosensor functionalized with GOx under different pH conditions for various hierarchical GOx assemblies within SLG. We developed a methodology to separately extract the average shape of GOx molecules within the hierarchical assemblies. The modeling is able to resolve differences in the average GOx dimer structure and shows that treatment under different pH conditions lead to differences within the GOx at the dimer contact region with SLG. The coupling of different analysis methods and modeling approaches we developed in this study provides a universal approach to obtain detailed structural quantifications, for establishing robust structure-property relationships. This is an essential step to obtain an insight into the structure and function of the GOx-SLG interface for optimizing sensor performance.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to NATURE PUBLISHING GROUP
Department/Centre: Division of Interdisciplinary Research > Computational and Data Sciences
Depositing User: Id for Latest eprints
Date Deposited: 11 Dec 2019 06:23
Last Modified: 11 Dec 2019 06:23
URI: http://eprints.iisc.ac.in/id/eprint/63962

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