Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors

This paper reports a new mechanism for particulate matter detection and identification. Three types of carbon particles are synthesized with different functional groups to mimic the real particulates in atmospheric aerosol. After exposing polymer-based organic devices in organic field effect transistor (OFET) architectures to the particle mist, the sensitivity and selectivity of the detection of different types of particles are shown by the current changes extracted from the transfer curves. The results indicate that the sensitivity of the devices is related to the structure and functional groups of the organic semiconducting layers, as well as the morphology. The predominant response is simulated by a model that yielded values of charge carrier density increase and charge carriers delivered per unit mass of particles. The research points out that polymer semiconductor devices have the ability to selectively detect particles with multiple functional groups, which reveals a future direction for selective detection of particulate matter. The paper illustrates a unique approach for detecting carbon particles, which have the potential to selectively detect particulate matter in aerosols. The sensing mechanisms behind the selectivity and sensitivity, and how to optimize the sensing responses and distinctions are also discussed. Polymer semiconductors can be promising materials for accurate and efficient particulate detection.image

Automated summary

This paper reports a new mechanism for detecting and identifying particulate matter, using synthesized carbon particles with different functional groups to mimic real particles in atmospheric aerosols. The research highlights the ability of polymer semiconductor devices to selectively detect particles with multiple functional groups, revealing a future direction for selective detection of particulate matter.