Smart colorimetric temperature sensor with thermoresponsive polymers and automatic image-based identification
Metadatos
Mostrar el registro completo del ítemAutor
Lopez-Leon, Carmen; Salcedo-Abraira, Pablo; Moyano, César; Toral López, Víctor; Morales Santos, Diego Pedro; Salinas Castillo, Alfonso; Romero Maldonado, Francisco JavierEditorial
Elsevier
Materia
Colorimetric sensor Temperature sensor Thermoresponsive polymer
Fecha
2026-05-01Referencia bibliográfica
Lopez-Leon, C.; Salcedo-Abraira, P.; Moyano, C.; [et al]. (2026). Smart colorimetric temperature sensor with thermoresponsive polymers and automatic image-based identification. Sensors and Actuators: A. Physical. Volume 402, 117619. https://doi.org/10.1016/j.sna.2026.117619
Patrocinador
Centre for the Development of Industrial Technology (CDTI) (IDI-20250141); Junta de Andalucía (ProyExcel_00268 and P21_00386); MICIU/AEI/10.13039/501100011033 (JDC2022–048964-I); European Union NextGenerationEU/PRTR; Universidad de Granada / CBUAResumen
Temperature monitoring is essential across multiple sectors, including healthcare, logistics, and safety. Achieving accurate measurement under diverse conditions in a cost-effective, and sustainable manner remains a key challenge for both the scientific community and industry. In this context, we present the development and characterization of colorimetric sensors based on a polydimethylsiloxane (PDMS) matrix that incorporates commercial, food-grade, organic thermochromic pigments, thus enabling temperature sensing without electronic components. These sensors consist of six distinct modules containing pigments with different activation temperatures to enable continuous optical monitoring over a broad thermal range. The colorimetric response of each module was quantitatively analyzed as a function of temperature using image-based processing techniques. The results demonstrate good thermal reversibility and stable, reproducible behavior over multiple thermal cycles and under different ambient conditions. Once the colorimetric response of the sensor was characterized, we implemented a precise color quantification method through digital image processing to overcome the purely qualitative nature of traditional thermochromic materials. Moreover, the application uses a random forest model with a high-precision correlation between colorimetric data and temperature, thus confirming sensor reliability. Overall, the results highlight the system's potential as a scalable, electronics-free solution for temperature monitoring in diverse applications, such as smart packaging or intelligent surfaces.





