IoT-Enabled SnO₂-Based Humidity Sensor for Real-Time Monitoring in Neonatal Incubators

Area of Science:

• Materials Science
• Nanotechnology
• Biomedical Engineering

Background:

• Accurate humidity monitoring is crucial in incubator systems, especially for neonatal care.
• Existing sensors like DHT11 may lack the required precision and reliability.
• Tin Oxide (SnO₂) nanomaterials offer potential for advanced sensor development.

Purpose of the Study:

• To develop and optimize a SnO₂-based resistive humidity sensor for incubator systems.
• To evaluate the sensor's performance against commercial alternatives and ensure accuracy using machine learning.
• To integrate the sensor into an incubator system with IoT capabilities for real-time monitoring.

Main Methods:

• Hydrothermal synthesis of SnO₂ nanomaterials.
• Characterization using XRD, FESEM, J-V analysis, UV-Vis, and EIS.
• Performance testing within the 40-60% RH range and comparison with DHT11 sensor.
• Application of machine learning for error estimation and data validation.
• Integration into an incubator system with IoT data transmission.

Main Results:

• Flower-like SnO₂ nanostructures were synthesized and characterized.
• The optimized sensor achieved a response time of 18 s, recovery time of 14 s, and sensitivity of 72.4%.
• The sensor demonstrated superior performance compared to the DHT11 sensor within the target humidity range.
• Machine learning algorithms successfully validated the sensor's humidity data.
• The sensor was successfully integrated into an incubator system with real-time IoT data transmission.

Conclusions:

• Optimized SnO₂ nanomaterials are highly effective for developing precise resistive humidity sensors.
• The developed sensor meets the stringent requirements for incubator humidity monitoring, particularly in neonatal care.
• Integration with IoT platforms enables reliable, real-time humidity tracking for critical biomedical applications.