Colorimetry-based detection of nitric oxide from exhaled breath for quantification of oxidative stress in human body

Monitoring exhaled breath is a safe, noninvasive method for determining the health status of the human body. Most of the components in our exhaled breath can act as health biomarkers, and they help in providing information about various diseases. Nitric oxide (NO) is one such important biomarker in exhaled breath that indicates oxidative stress in our body. This work presents a simple and noninvasive quantitative analysis approach for detecting NO from exhaled breath. The sensing is based on the colorimetric assisted detection of NO by m-Cresol Purple, Bromophenol Blue, and Alizaringelb dye. The sensing performance of the dye was analyzed by ultraviolet?visible (UV?Vis) spectroscopy. The study covers various sampling conditions like the pH effect, temperature effect, concentration effect, and selective nature of the dye. The m-Cresol Purple dye exhibited a high sensitivity towards NO with a detection limit of ~0.082 ppm in the linear range of 0.002?0.5 ppm. Moreover, the dye apprehended a high degree of selectivity towards other biocompounds present in the breath, and no possible interfering cross-reaction from these species was observed. The dye offered a high sensitivity, selectivity, fast response, and stability, which benchmark its potential for NO sensing. Further, m-Cresol Purple dye is suitable for NO sensing from the exhaled breath and can assist in quantifying oxidative stress levels in the body for the possible detection of COVID-19.Acknowledgments: This work was supported by the UREP grant #UREP27-044-3-016 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Development and Fabrication of Carbon Nanotube (CNT)/CuO Nanocomposite for Volatile Organic Compounds (VOCs) Gas Sensor Application

Volatile organic compounds (VOCs) have been recognized as one of the primary trace segments of atmospheric air pollutants. The change in the level of VOCs in the surrounding environment can lead to chronic health issues, respiratory problems, nerve system disorder, and toxicity in kidneys/liver. Thus, monitoring of VOCs concentration in the surrounding environment is significant for avoiding serious health problems. Herein, copper oxide (CuO) nanoparticle and carbon nanotube (CNT) nanocomposite (NC) are presented for the efficient detection of VOCs. The scalable sol-gel method is adopted for the controlled growth of CNT/CuO NC. The structural, elemental, and morphological analysis is performed by XRD, FTIR spectroscopy, and SEM characterization, respectively. The VOCs sensor was fabricated by drop-casting the as-synthesized CNT/CuO NC on interdigitated electrodes (IDEs). The CNT/CuO sensing response is analyzed for six VOCs that include toluene, methanol, acetone, chloroform, xylene, and benzene. The CNT/CuO response towards different VOCs is investigated with respect to change in resistance of the material in the presence of test VOC and in an inert atmosphere. In comparison to other VOCs, the sensor exhibits high sensitivity toward benzene. The estimated change in relative resistance (AR) for benzene is ?0.62% for 500 ppm concentration. Moreover, the sensor apprehended a detection of benzene with a concentration as low as 5 ppm. The as-synthesized CNT/CuO NC offers high sensitivity and low detection limit, which benchmark its potential for benzene detection.This work is carried by the UREP grant # UREP27-044-3-016 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Colorimetry-Based Detection of Biomarkers in Exhaled Breath for Predicting COVID-19 Disease

Exhaled breath is the biological medium that carries relevant medical information and can be used to analyse biomarkers characteristic for detecting abnormal health status. Thus, by systematically analysing the interaction mechanism of the coronavirus with the human cell and its effect on the biological activity, it is possible to indentify the compounds whose proportion in the exhale breath is affected. One such biomarkers are hydrogen peroxide (H2O2) and nitric oxide (NO), which represents oxidative stress in the body. The present study represents the colorimetry based quantification of H2O2 and NO using KMnO4 and m-cresol purple dye, respectively. The dyes exhibited 0.01 ppm limit of detection (LOD) for H2O2 and LOD of 0.02 ppm was estimated for NO. Moreover, dyes apprehended high degree of selectivity towards other bio-compounds present in the breath. The colorimetry sensor is best suited for quantifying oxidative stress in the body, which is one of the indicator of coronavirus infection. Thus, the sensor offers rapid point-of-detection for predicting COVID-19 infection in human body