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Early and efficient disease diagnosis with low-cost point-of-care devices is gaining importance for personalized medicine and public health protection. Within this context, waveguide-(WG)-based optical biosensors on the silicon-nitride (Si3N4) platform represent a particularly promising option, offering highly sensitive detection of indicative biomarkers in multiplexed sensor arrays operated by light in the visible-wavelength range. However, while passive Si3N4-based photonic circuits lend themselves to highly scalable mass production, the integration of low-cost light sources remains a challenge. In this paper, we demonstrate optical biosensors that combine Si3N4 sensor circuits with hybrid on-chip organic lasers. These Si3N4-organic hybrid (SiNOH) lasers rely on a dye-doped cladding material that are deposited on top of a passive WG and that are optically pumped by an external light source. Fabrication of the devices is simple: The underlying Si3N4 WGs are structured in a single lithography step, and the organic gain medium is subsequently applied by dispensing, spin-coating, or ink-jet printing processes. A highly parallel read-out of the optical sensor signals is accomplished with a simple camera. In our proof-of-concept experiment, we demonstrate the viability of the approach by detecting different concentrations of fibrinogen in phosphate-buffered saline solutions with a sensor-length (L-)-related sensitivity of S/L = 0.16 rad nM−1 mm−1. To our knowledge, this is the first demonstration of an integrated optical circuit driven by a co-integrated low-cost organic light source. We expect that the versatility of the device concept, the simple operation principle, and the compatibility with cost-efficient mass production will make the concept a highly attractive option for applications in biophotonics and point-of-care diagnostics.

The ubiquity of ever-connected smartphones has lead to new sensing paradigms that promise environmental monitoring in unprecedented temporal and spatial resolution. Everyday people may use low-cost sensors to collect environmental data. However, measurement errors increase over time, especially with low-cost air quality sensors. Therefore, regular calibration is important. On a larger scale and in participatory sensing, this needs be done in-situ. Since for this step, personal sensor data, time and location need to be exchanged, privacy implications arise. This paper presents a novel privacy-preserving multi-hop sensor calibration scheme, that combines Private Proximity Testing and an anonymizing MIX network with cross-sensor calibration based on rendezvous. Our evaluation with simulated ozone measurements and real-world taxicab mobility traces shows that our scheme provides privacy protection while maintaining competitive overall data quality in dense participatory sensing networks.

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