Electrochemical Breath Sensor for Real-Time Detection of Disease-Related VOCs Using Room-Temperature Ionic Liquid Interfaces

Abstract:

Volatile organic compounds (VOCs) in exhaled breath serve as powerful non-invasive biomarkers for early-stage diagnosis and monitoring of chronic diseases. While roomtemperature ionic liquid (RTIL)-based electrochemical sensors have demonstrated promise for VOC detection, they remain largely confined to rigid, lab-scale devices, limiting real-world application. This project proposes the development of a flexible, wearable electrochemical sensor platform integrated into a face mask, initially targeting acetone as a biomarker for diabetes. Major innovations include (1) fabrication on flexible polymeric electrodes, (2) a custom low-power potentiostat for wearable deployment, and (3) an AI-enhanced data analysis system to compensate for environmental variability and improve selectivity. The system is designed for short-duration use, such as morning breath analysis, with the potential to expand to other disease-relevant VOCs like ammonia or nitric oxide. This research aims to answer the research question: “Can we enable selective, user-adaptive sensors for diseaserelated VOCs, by integrating a flexible electrochemical sensing platform with AI-driven analysis?”

 

Introduction:

Breathomics, the study of volatile organic compounds (VOCs) in exhaled air, offers a noninvasive route for real-time disease detection. Endogenous VOCs like acetone, ammonia, and nitric oxide reflect metabolic dysfunctions associated with diabetes, kidney disease, and inflammation, respectively (Sharma et al., 2023). However, the transition from research-grade instrumentation (e.g., GC-MS) to user-friendly, wearable diagnostics remains an unsolved challenge.

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Traditional gas sensors suffer from limitations: metal oxide semiconductors (MOS) require high temperatures, while chemiresistive sensors show poor selectivity. Electrochemical platforms based on RTILs and nanomaterials have shown selectivity and ppb-level sensitivity at room temperature (Gong et al., 2024). Still, these are generally rigid, lab-confined, and lack user-adaptivity or embedded intelligence. This project proposes a flexible electrochemical integrated sensing system enhanced by machine learning (ML). The system will be initially applied to acetone detection and quantification in breath samples for diabetes monitoring.

 

Literature Review:

Breath analysis offers a non-invasive and real-time approach to disease detection through volatile organic compounds(VOCs) that are associated with pathological conditions, including diabets, kidney disease, and cancer. Recent studies confirm that breath acetone concentrations rise significantly (>1.8 ppm) in diabetic patients compared to healthy individuals (<0.8 ppm) (Sett et al., 2022). Nanomaterial-enhanced RTIL sensors using platforms like ZIF-8, UIO-66- NH2@MoS2, and rGO-metalloporphyrins have demonstrated excellent acetone and ammonia sensitivity in the ppb range (Lee et al., 2021 and Velumani et al., 2022). However, most systems are not wearable, require external lab instrumentation, and suffer from interferences and cross-sensitivity, defining a significant limitation given the high moisture content of exhaled breath (Velumani et al., 2022).

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AI integration, particularly with support vector machines (SVM) and neural networks (ANN) has shown high performance in VOC discrimination, achieving >90% accuracy in distinguishing disease-related breath signatures (Tizhoosh et al., 2018). However, such systems are rarely embedded in flexible, low-power formats suitable for daily health monitoring. Hence, there is a pressing need for a wearable, AI-assisted electrochemical VOC sensor that balances comfort, specificity, and real-time operation, addressing the gap between lab research and point-of-care use.

 

Methodology:​ 

The project will be executed in two phases (P), divided into multiple activities (A), as described below:

P1 - Flexible Sensor Design

A1.1. Fabrication and characterization. In a first step, commercially available screen-printed electrodes (SPEs) on flexible polymeric substrates will be used. However, in-house fabrication of the screen-printed electrodes is also envisaged, using easily disposable, biodegradable polymeric substrates such as paper or polylactic acid sheets. The SPEs will be functionalized with nanostructures (based on MoS2, ZnO or rGO) and will be characterized morphologically and elementally by scanning-electron microscopy and energy dispersive Xray mapping. Electrochemical characterization will be evaluated by cyclic voltammetry and electrochemical impedance spectroscopy, using RTILs such as [BMIM][BF₄]. Baseline measurements will be performed in normal atmosphere taking into consideration various conditions regarding temperature, humidity and air flow (static versus stimulated flow, using and adjustable-speed fan).

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A1.2. Acetone detection. The performance of the optimal sensors resulting from A1.1. will be further evaluated towards acetone detection and quantification in an enclosed chamber saturated with acetone, using various concentrations as well as various environmental conditions. The accuracy of the fabricated sensors will be assessed by comparison with measurements obtained from a commercial photoionization detector (PID). The selectivity of the sensors towards acetone will be assed in a mixed gas atmosphere using VOCs that can be present in the human breath, such as ethanol, ammonia, acetylene, among others. A1.3. Real-sample analysis. The sensor architecture exhibiting the lowest limit of detection will be further evaluated using breath samples from healthy and diabetic volunteers. The detection accuracy of the fabricated sensor will be assessed by comparison with a PID sensor.

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P2 – Integrated System

A2.1. Circuit Development. A miniaturized, low power potentiostat will be designed and fabricated on a flexible polyimide substrate, suitable for integration within a wearable system such as a face mask. Temperature and humidity sensors will be embedded within the same flexible module for real-time environmental monitoring. A face mask prototype will be developed by 3D printing with biocompatible polymers.

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A2.3. Data Acquisition and Processing. An AI algorithm will be trained on the measurements resulting from A1.1. and A1.2., in order to: account for measurement variations resulting from environmental factors, and be able to perform baseline correction and signal normalization. Data from the acetone and environmental sensors will be parsed to a microcontroller unit, into which the AI algorithm will be also deployed. The capability of the system to perform VOC pattern recognition and adaptive calibration will be evaluated using real samples.

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Final Remarks:

This project aims to develop a portable integrated electrochemical sensing platform and reach a technology readiness level of 4. By initially targeting acetone for diabetes, and designing the system to be modular for other analytes, the platform holds promise for scalable, non-invasive diagnostics in everyday healthcare. However, while this platform is dedicated to biological markers, such a device could be further targeted towards the rapid detection of pollutants or chemical warfare compounds.

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