Russian and Chinese scientists develop model to predict smart sensor performance at extreme temperatures

Researchers from the Skolkovo Institute of Science and Technology (Skoltech), together with colleagues from the Harbin Institute of Technology and Jiangsu University, have developed an empirical model capable of describing and predicting the behaviour of carbon nanotube-based sensors across a wide temperature range, from –170°C to +90°C. This was reported by the press service of the Russian university, a TV BRICS partner.

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 such materials makes signal analysis more challenging, as a sensor may respond to several factors simultaneously, making it difficult to determine the exact cause of a change in its response. 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 fibres to hierarchical three-phase nanocomposites. Despite differences in their structure, all of the systems investigated demonstrated similar temperature-dependent electrical conductivity.

The researchers found that at low, cryogenic temperatures, charge transport occurs primarily through a hopping mechanism between localised states. As the temperature increases, the dominant mechanism shifts to one characteristic of metallic charge-carrier scattering.

According to the researchers, the identified pattern provides a deeper understanding of the physical mechanisms governing nanomaterial performance and supports the development of more reliable sensor systems capable of operating under significant 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.