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AI-enabled, enzyme-integrated photonic crystal sensor for real-time noninvasive visual uric acid detection
Aojue Ke1, Haibin Chen1, Chunhao Li1
1School of Chemistry and Chemical Engineering, Guangdong Provincial Key Lab of Green Chemical Product Technology, South China University of Technology, Guangzhou 510640, PR China.
Colloids and Surfaces. B, Biointerfaces
|May 23, 2026
Summary
This study introduces a novel photonic crystal sensor for detecting uric acid (UA), offering improved stability and selectivity for metabolic disorder diagnosis. The sensor uses structural color changes for easy visual detection and smartphone analysis.
Area of Science:
- Biomedical Engineering
- Materials Science
- Analytical Chemistry
Background:
- Uric acid (UA) is a key biomarker for metabolic disorders like gout and hyperuricemia.
- Current visual UA sensors lack stability, selectivity, and clear colorimetric resolution.
- Noninvasive optical sensors offer advantages for UA analysis.
Purpose of the Study:
- To develop a stable, selective, and visually clear optical sensor for uric acid detection.
- To transduce enzymatic recognition of UA into measurable structural color shifts.
- To create a smartphone-based platform for quantitative UA analysis using AI.
Main Methods:
- Fabrication of a photonic crystal (PC) sensor using a polystyrene@graphene oxide (PS@GO) template and enzyme-incorporated hydrogel.
- Integration of enzymatic UA recognition with structural color changes in the PC.
- Development of an AI-enabled machine learning strategy for image capture and quantitative analysis.
Main Results:
- The UA-PC sensor demonstrated a rapid response time of approximately 20 minutes.
- Achieved high sensitivity with a detection limit of 5 μM for UA.
- Exhibited excellent anti-interference performance against common urinary components.
- Enabled intuitive visual readout through robust structural color shifts.
Conclusions:
- The developed UA-PC sensor overcomes limitations of conventional sensors in stability, selectivity, and visual clarity.
- The AI-powered smartphone platform facilitates quantitative UA analysis for home-based monitoring.
- This work presents a proof-of-concept for a visual sensing platform for daily health screening and self-monitoring.
