Dual-Mode SERS and Colorimetric Sensor for Lung Cancer VOC-Biomarker Detection Using Hydrogel Patches

A dual-mode flexible biosensor was developed for the detection of hexanal, a volatile organic compound (VOC) potentially serving as a biomarker for early lung cancer detection in exhaled breath. The sensor integrates surface-enhanced Raman scattering (SERS) and colorimetric detection capabilities. The sensing platform utilizes Ag nanocubes wrapped with Co-Ni layered double hydroxide ($\text{AgNCs@Co-Ni LDH}$) as the functional matrix, which provides both SERS enhancement and VOC adsorption. These materials were incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH) to facilitate the oxidation of hexanal. This process generates a colorimetric signal, producing acrizine, a blue-colored reaction product, alongside the SERS signal. The resulting $\text{MBTH-AgNCs@Co-Ni LDH/hydrogel-patch}$ demonstrated high selectivity and stability for hexanal detection via SERS, achieving a limit of $3.34 \times 10^{-13} \text{ M}$. Furthermore, a compact Convolutional Neural Network (CNN)-based multi-terminal intelligent recognition system was developed to enhance the hydrogel-patch detection through real-time colorimetric analysis.

The novelty lies in merging distinct sensing modalities—physical (SERS) and chemical (colorimetric)—into a stable, flexible hydrogel platform designed for complex biomarker analysis. The combination of nanoscale material functionality ($\text{AgNCs@Co-Ni LDH}$) with bulk hydrogel chemistry creates a system where molecular interaction translates into macroscopic, measurable signals. This moves the detection of a subtle biological marker like hexanal from highly specialized laboratory techniques to a portable, real-time format, which has significant implications for point-of-care diagnostics in oncology. The introduction of AI for pattern recognition further extends the utility by moving beyond simple signal quantification to intelligent, automated analysis of the sensor output. A key question arising is whether the stability and reproducibility of this integrated system hold across various sample matrices found in clinical settings, and how robustly the CNN handles the inherent noise associated with breath samples. Furthermore, establishing a multimodal standard for biosensors requires examining the cost-effectiveness and scalability of manufacturing complex nanocomposite hydrogels versus established diagnostic methods.