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

Physics > Medical Physics
[Submitted on 29 Jul 2026]
Title:Dual-Mode SERS and Colorimetric Sensor for Lung Cancer VOC-Biomarker Detection Using Hydrogel Patches
View PDFAbstract:Hexanal, a volatile organic compound (VOC), is a potential biomarker for the early detection of lung cancer. In this study, we developed a dual-mode flexible biosensor that integrates surface-enhanced Raman scattering (SERS) and colorimetric detection for the quantitative analysis of hexanal in human exhaled breath. The biosensor employs Ag nanocubes wrapped with Co-Ni layered double hydroxide (AgNCs@Co-Ni LDH) as a functional matrix, offering both superior SERS enhancement and efficient VOC adsorption properties. To achieve selective detection, AgNCs@Co-Ni LDH were incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH). The resulting MBTH-AgNCs@Co-Ni LDH/hydrogel-patch facilitate the oxidation of hexanal, enabling simultaneous colorimetric and SERS signal generation, while producing acrizine, a blue-colored reaction product. This hydrogel-based dual-mode sensing platform exhibits high selectivity, excellent stability, and precision in SERS-based hexanal detection. The detection limit for the SERS method was determined to be as low as 3.34x10-13 M. Furthermore, developed and optimized compact CNN-based multi-terminal intelligent recognition system for enhanced hydrogel-patch detection through AI-driven, portable, and real-time colorimetric analysis. Therefore, this work not only enables the effective detection of hexanal in the exhaled breath of suspected lung cancer patients, underscoring its potential for early lung cancer screening, but also establishes a foundation for the development of multimodal hydrogel biosensors for broader applications in disease diagnosis.
Current browse context:
physics.med-ph
Change to browse by:
References & Citations
Loading...
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Facts Only

* Hexanal is a volatile organic compound (VOC) that is a potential biomarker for early lung cancer detection.
* A dual-mode flexible biosensor integrating SERS and colorimetric detection was developed for quantitative analysis of hexanal in human exhaled breath.
* The biosensor functional matrix involves $\text{AgNCs@Co-Ni LDH}$.
* $\text{AgNCs@Co-Ni LDH}$ were incorporated into agarose hydrogels with 3-methyl-2-benzothiazolinone hydrazone (MBTH).
* Oxidation of hexanal by the system generates colorimetric and SERS signals, producing acrizine.
* The resulting sensor facilitated simultaneous signal generation while producing a blue-colored reaction product.
* The detection limit for the SERS method was determined to be $3.34 \times 10^{-13} \text{ M}$.
* A compact CNN-based multi-terminal intelligent recognition system was developed for enhanced hydrogel-patch detection.

Executive Summary

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.

Full Take

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.

Sentinel — Human

Confidence

This appears to be a technically dense summary of a scientific research paper detailing novel biosensor development for biomarker detection, exhibiting characteristics consistent with human academic writing.

Signals Detected

Moderate sentence length variance; technical density suggests specialized authorship.

Fluent presentation of complex, multi-step methodology without excessive hedging or overly abstract framing.

Clear progression from material (VOC biomarker) to method (biosensor) to application (AI recognition).

Specific technical details (SERS enhancement, detection limit of 3.34x10-13 M, specific chemical reactions) suggest direct reporting of research findings.

Human Indicators

The text uses highly specific scientific terminology and details regarding experimental results (detection limits, functional materials) typical of peer-reviewed or technical reporting.

The structure follows the standard pattern of a scientific abstract/summary.