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Despite their extraordinary mechanosensitivities, most channel-like crackbased strain sensors are limited by their poor levels of stretchability and linearity. This work presents a simple yet efficient way of modulating the cracking structure of thin metal films on elastomers to facilitate the development of high-performance wearable strain sensors. A net-shaped crack structure based on a thin platinum (Pt) film can be produced by coating an elastomer surface with M13 bacteriophages (phages) and consequently engineering the surface strain upon stretching. This process produces a Pt-on-phage (PoP) strain sensor that simultaneously exhibits high levels of stretchability (24%), sensitivity (maximum gauge factor ≈ 845.6 for 20−24%), and linearity (R 2 ≈ 0.988 up to 20%). In addition, the sensor performance can be further modulated by either changing the phage coating volume or adding a silver nanowire coating to the PoP sensor film. The balanced strain-sensing performance, combined with fast response times and high levels of mechanical flexibility and operational stability, enables the devices to detect a wide range of human motions in real time after being attached to various body parts. Furthermore, PoP-based strain sensors can be usefully extended to detect more complex multidimensional strains through further strain engineering on a cross-patterned PoP film.

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.