Data-processing method to reduce error coefficients for membrane-based analytical systems. 1. Amperometric-based...

Area of Science:

• Analytical Chemistry
• Electrochemistry
• Sensor Technology

Background:

• Membrane-based devices are susceptible to experimental variables affecting results.
• Transient response data often introduces variability in measurements.
• Predicting equilibrium signals is crucial for reliable sensor performance.

Purpose of the Study:

• To develop and evaluate a predictive, curve-fitting method for membrane-based devices.
• To minimize the impact of experimental variables on sensor readings.
• To enhance the accuracy and efficiency of oxygen measurements using amperometric electrodes.

Main Methods:

• Utilizing multipoint data from transient response regions.
• Applying suitable models and curve-fitting techniques.
• Predicting the system's equilibrium signal from transient data for membrane-based amperometric oxygen electrodes.

Main Results:

• The predictive method yields equilibrium responses less dependent on experimental variables.
• Predicted equilibrium currents show a linear relationship with oxygen concentration.
• Compared to steady-state methods, the predictive approach offers 5-fold higher sensitivity, 17-fold shorter measurement time, and significantly reduced dependencies on membrane thickness and stirring rate.

Conclusions:

• The predictive curve-fitting method offers a robust alternative for analyzing membrane-based sensor data.
• This method enhances measurement accuracy and efficiency for oxygen determination.
• The approach demonstrates superior performance in reducing experimental variability and improving sensitivity.