Speaker
Description
The development of high-performance Terahertz–Infrared (THz–IR) sensors is essential for next-generation non-destructive inspection. Photo-thermoelectric (PTE) sensors utilizing carbon nanotubes (CNTs) are promising due to their broadband response. However, achieving peak sensitivity requires matching the material's properties to the specific eigenfrequency of the inspection target. The reciprocal of the NEP was used as the primary sensitivity index to identify the optimal material configuration for each target wavelength. This study investigates the optimal CNT design focusing on ionic doping concentrations and semiconducting-to-metallic ratios to minimize the Noise Equivalent Power (NEP). We fabricated CNT thin films with varying semiconducting ratios (0%–90%) and controlled ionic doping levels. To determine the NEP, we systematically measured the Seebeck coefficient, electrical resistance, and transmittance across a broad wavelength range. Experimental results revealed that these three variables behave independently under doping For CNTs with a 50% semiconducting ratio, the Seebeck coefficient increased at low doping concentrations but decreased at higher concentrations. In 90% semiconducting CNTs, resistance decreased as dopant concentration increased. Notably, higher semiconducting ratios led to a more pronounced rate of decrease in resistance. For 100% semiconducting CNTs, doping caused a slight decrease in absorptance in the near-infrared (NIR) region, while significantly enhancing it in the far-infrared (FIR) region. By integrating these divergent behaviors into the NEP calculation, we successfully identified the optimal CNT parameters for specific wavelengths. These results provide a design roadmap for tailoring CNT-based PTE sensors to achieve maximum sensitivity in targeted THz–IR sensing applications. Details of the measurement data and NEP optimization will be presented on the poster.
| I am the presenting author | Yes |
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