Real-time breath analysis for COPD risk assessment in smokers using a ZnO/SnO₂ heterojunction sensor integrated with support vector machine

Chronic Obstructive Pulmonary Disease (COPD) is a progressively debilitating and life-threatening respiratory condition that has long been attributed to exposure to airborne toxic substances like carbon monoxide (CO), a reliable biomarker of oxidative stress, especially in smoking individuals. To address the need for a real-time and non-invasive method that can detect this life-threatening condition, a wearable diagnostic device is developed in this work based on a Z/S heterostructure thin-film gas sensor that can detect exhaled CO at a temperature as low as 37 °C. This gas sensor was prepared using a hydrothermal synthesis route and characterized by various techniques like X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and Energy-dispersive spectroscopy (EDS), and packed in a face mask design that enables continuous sampling of breath. Of all the sensing devices prepared with varying concentrations of the active components ZnO (Z), SnO₂ (S), and the combination of both (Z/S), the Z/S structure showed maximum sensitivity with a sensitivity of 264.29% at 12 parts per million (ppm), a response time of 14 seconds, and a recovery time of 3 seconds. The incorporation of a polymer layer of poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) improved the charge carrier mobility through p-n junction formation. A customized gas sensing laboratory chamber enabled accurate calibrations of CO sensing. To derive actual CO levels from the resistance changes in the sensor, a power-law model was used. To classify patient groups further as current smokers, ex-smokers, and non-smokers accurately, a support vector machine (SVM) classification with a training accuracy of 94.2% and testing accuracy of 81.7% was used in this work. By integrating nanostructured gas sensors with wearable technology design concepts and ML diagnostics in a seamless Internet of Things (IoT) network, this scientific development will allow early diagnosis of the life-threatening condition of COPD in a completely non-invasive and non-destructive fashion with high portability. This developed device has unending possibilities in taking up roles in individualized breath medical care and represents a significant milestone in translating on-body disease detection using sensor technology.