Gaussian-Fitting-Enabled High-Accuracy pH Detection for Light-Addressable Potentiometric Sensor

Area of Science:

• Semiconductor device physics
• Analytical chemistry
• Biomedical engineering

Background:

• Light-addressable potentiometric sensors (LAPS) are cost-effective semiconductor field-effect pH sensors.
• Traditional LAPS pH detection relies on normalizing voltage-photocurrent (V-I) characteristics, which is slow and susceptible to noise.

Purpose of the Study:

• To develop a novel, faster, and more robust pH measurement method for LAPS.
• To overcome the limitations of traditional V-I characteristic normalization in LAPS.

Main Methods:

• Fitting a Gaussian function to the local depletion region of the LAPS V-I characteristic.
• Utilizing robust morphological parameters of the fitted function for direct measurement.
• Optimizing measurement parameters like bias voltage range, number of points, and step value.

Main Results:

• The new method significantly reduces the impact of noise and measurement point distortions.
• Achieved high sensitivity, linearity, and specificity in pH detection.
• Demonstrated improved detection speed and strong anti-interference capabilities against light source fluctuations.

Conclusions:

• The proposed Gaussian fitting method offers a significant advancement for LAPS-based pH detection.
• This method provides a new paradigm for high-precision, rapid detection of pH and other biochemical species.
• The technique shows practical applicability with low error rates in real-world samples like sewage.