Synthetic spider silk forming highly-aligned nanoarchitectures on 2D-material surfaces
Whittall, D. R.; Kato, R.; Okimura, A.; Takizawa, R.; Motai, K.; Maeda, H.; Yamazaki, Y.; Yano, T.-a.; Takano, E.; Hayamizu, Y.
Show abstract
The synergy between silk proteins and nanomaterials can lead to novel materials with improved mechanical and electrical properties. Designed peptides have been previously utilised in the functionalisation of two-dimensional material surfaces in a self-assembly manner, including graphite, to develop highly sensitive electrical biosensors. These studies have predominantly focused on functionalising the surfaces with peptides of less than several kDa in size. In this work, we assessed the capability of a [~]35 kDa synthetic spider silk protein to serve as a candidate biomolecular scaffold on a range of two-dimensional materials: graphite, molybdenum disulphide and boron nitride. The structural properties of the synthetic spider silk protein at the 2D material surface were characterised at the nanoscale for the first time using a multi-analysis approach incorporating atomic force microscopy and fluorescence microscopy, in addition to polarised Raman and tip-enhanced Raman spectroscopy. The synthetic spider silk protein was revealed to self-assemble into stable nanowire structures of monolayer thickness. Our findings have demonstrated the feasibility of functionalising 2D materials with spider silk-based proteins and will unlock new possibilities in the development of next-generation high-performance biosensing devices.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- DNA-caged Nanoparticles via Electrostatic Self-Assembly 94%
- PEGylated surfaces for the study of DNA-protein interactions by atomic force microscopy 94%
- Adhesion force spectroscopy with nanostructured colloidal probes reveals nanotopography-dependent early mechanotransductive interactions at the cell membrane level 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.