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

Jie Tan¹, Zigeng Huang¹, Bin Sun¹

¹School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610097, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

A new Gaussian fitting method enhances light-addressable potentiometric sensor (LAPS) pH detection. This approach improves speed and accuracy by reducing noise influence and measurement points for reliable results.

Keywords:

Gaussian function depletion region high detection performance light-addressable potentiometric sensor pH detection

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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.