Optimization of Gas Sensors Based on Advanced Nanomaterials through Split-Plot Designs and GLMMs

Area of Science:

• Materials Science
• Chemical Engineering
• Statistical Modeling

Background:

• Perovskite materials offer high sensitivity for detecting hazardous gases such as nitrogen dioxide (NO2) and carbon monoxide (CO).
• Accurate detection of these gases is crucial for industrial safety and human health.
• Existing gas sensing technologies require optimization for improved performance and efficiency.

Purpose of the Study:

• To plan and model a split-plot experiment for optimizing perovskite-based gas sensing materials.
• To develop a dual-response modeling approach for simultaneous estimation of location and dispersion models.
• To conduct robust process optimization for minimizing working temperature while maximizing gas detection sensitivity.

Main Methods:

• Utilized a split-plot experimental design tailored for two target gases (NO2 and CO).
• Applied a dual-response modeling approach, estimating both location and dispersion models.
• Implemented robust process optimization techniques, focusing on minimizing operational temperature.

Main Results:

• Achieved satisfactory estimates for process variables and reliable diagnostic valuations through dual-response modeling.
• Successfully optimized perovskite gas sensing materials for both NO2 and CO detection.
• Demonstrated significant improvements in gas sensing performance compared to previous studies, particularly at minimized working temperatures.

Conclusions:

• The proposed split-plot design and dual-response modeling effectively enhance perovskite gas sensor performance.
• Optimized sensor materials show improved detection capabilities for hazardous gases at lower working temperatures.
• This approach offers a robust framework for developing advanced gas sensing solutions for industrial and environmental monitoring.