Dual-mode sweat urea sensor based on Ti3C2Tx MXene/CuO nanocomposite: Colorimetric-electrochemical detection optimized by machine learning and genetic algorithms

• Published in: Sensors and actuators. B, Chemical Vol. 447; p. 138882
• Main Authors: Laochai, Thidarut, Nishihara, Hirotomo, Qin, Jiaqian, Okhawilai, Manunya, Wang, Joseph, Rodthongkum, Nadnudda, Sukmas, Wiwittawin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2026
• Subjects: Dual-mode sensor; Genetic algorithm; Machine learning; Sweat urea; Ti3C2Tx MXene/CuO nanocomposites; Sweat urea; Ti3C2Tx MXene/CuO nanocomposites; Dual-mode sensor; Genetic algorithm; Machine learning
• ISSN: 0925-4005
• DOI: 10.1016/j.snb.2025.138882

This study presents a high-performance dual-mode wearable sensor for real-time, non-invasive sweat urea monitoring, integrating Ti3C2Tx MXene/CuO nanocomposites with machine learning and genetic algorithm (ML-GA) optimization. The sensor combines two complementary modalities: (1) a urease-based colorimetric assay on nanocomposite-modified cotton for semi-quantitative detection (10–80 mM, R² = 0.9855), and (2) a non-enzymatic electrochemical system using Ti3C2Tx MXene/CuO/PEDOT:PSS-modified electrodes for precise quantification (0.5–60 mM, R² = 0.9706). The nanocomposite’s high surface area, conductivity, and catalytic activity enhance both sensing mechanisms, supported by DFT calculations showing strong interfacial interactions and urea adsorption. An ML framework evaluated > 20 regression models, identifying Random Forest Regression as most accurate (R² = 0.9650 for colorimetric; 0.8900 for electrochemical). GA optimization fine-tuned material ratios and parameters. On-body validation showed strong sweat-to-blood urea correlation (p < 0.01), confirming clinical relevance. This ML-GA-enhanced dual-mode sensor offers a reliable, user-friendly tool for personalized health monitoring and early renal disorder detection. [Display omitted] •A dual-mode sensor combining colorimetric and electrochemical detection of sweat urea is developed.•Ti3C2Tx MXene/CuO enhance conductivity, mechanical and catalytic properties for enhanced sensor performance.•This sensor accurately detects physiological sweat urea at cut-off levels for early kidney dysfunction.•Machine learning and genetic algorithm optimization align predictions with experiments, enhancing sensor precision.