Researchers from the Skolkovo Institute of Science and Technology (Skoltech) in Moscow, Russia,along with colleagues from the Harbin Institute of Technology and Jiangsu University in China, have developed an empirical model capable of predicting the behavior of carbon nanotube-based sensors across a broad temperature range of –170°C to +90°C.

Carbon nanotubes are widely used in sensor technologies due to their exceptional strength, high electrical conductivity and ability to alter their electrical properties in response to external factors, including temperature, mechanical stress and chemical influences. These characteristics make it possible to develop materials capable of detecting changes in the condition of structures and equipment.

However, the high sensitivity of these materials complicates signal analysis. Because a sensor may respond to multiple factors simultaneously, isolating the exact cause of a signal shift is notoriously difficult. The new model makes it possible to account for the influence of temperature and distinguish it from the effects of other external factors, improving the accuracy of data interpretation.

During the study, the researchers examined a range of materials based on single-walled carbon nanotubes, from fibers to hierarchical three-phase nanocomposites. Despite the differences in their structure, all of the systems investigated demonstrated similar temperature-dependent electrical conductivity.
The team discovered that at cryogenic temperatures, charge transport occurs primarily through a hopping mechanism between localized states. As temperatures rise, the dominant mechanism shifts to metallic charge-carrier scattering.

According to the researchers, this identified pattern provides deeper insights into the physical mechanisms governing nanomaterial performance. It also paves the way for developing more reliable sensor systems capable of operating under extreme temperature fluctuations.

The newly developed model could help engineers more accurately account for temperature effects when designing smart materials and distinguish temperature-related changes from other signal variations. In the future, such technologies could be applied in the aerospace sector, as well as in other industries where continuous monitoring of the condition of structures and equipment is required under demanding operating conditions.