Abstract

The nitric oxide radical plays pivotal roles in physiological as well as atmospheric contexts. Although the detection of dissolved nitric oxide in vivo has been widely explored, highly sensitive (i.e., low part-per-trillion level), selective, and humidity-resistant detection of gaseous nitric oxide in air remains challenging. In the field, humidity can have dramatic effects on the accuracy and selectivity of gas sensors, confounding data, and leading to overestimation of gas concentration. Highly selective and humidity-resistant gaseous NO sensors based on laser-induced graphene were recently reported, displaying a limit of detection (LOD) of 8.3 ppb. Although highly sensitive (LOD = 590 ppq) single-wall carbon nanotube NO sensors have been reported, these sensors lack selectivity and humidity resistance. In this report, we disclose a highly sensitive (LOD = 2.34 ppt), selective, and humidity-resistant nitric oxide sensor based on a whispering-gallery mode microtoroid optical resonator. Excellent analyte selectivity was enabled via novel ferrocene-containing polymeric coatings synthesized via reversible addition-fragmentation chain-transfer polymerization. Utilizing a frequency locked optical whispering evanescent resonator system, the microtoroid's real-time resonance frequency shift response to nitric oxide was tracked with subfemtometer resolution. The lowest concentration experimentally detected was 6.4 ppt, which is the lowest reported to date. Additionally, the performance of the sensor remained consistent across different humidity environments. Lastly, the impact of the chemical composition and molecular weight of the novel ferrocene-containing polymeric coatings on sensing performance was evaluated. We anticipate that our results will have impact on a wide variety of fields where NO sensing is important such as medical diagnostics through exhaled breath, determination of planetary habitability, climate change, air quality monitoring, and treating cardiovascular and neurological disorders.

Full Text
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