A comparison of online and offline measurement of exhaled breath for diabetes pre-screening by graphene-based sensor; from powder processing to clinical monitoring prototype

The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

Socio-economic demands and challenges for non-invasive disease diagnosis through a portable breathalyzer by the incorporation of 2D nanosheets and SMO nanocomposites

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials. The trace level selective detection of volatile organic compounds (VOCs) in breath facilitates the study of physiological disorder and real-time health monitoring. This review focuses on advancements in chemiresistive gas sensor technology for biomarker detection associated with different diseases. Emphasis is placed on selective biomarker detection by semiconducting metal oxide (SMO) nanostructures, 2-dimensional nanomaterials (2DMs) and nanocomposites through various optimization strategies and sensing mechanisms. Their synergistic properties for incorporation in a portable breathalyzer have been elucidated. Furthermore, the socio-economic demands of a breathalyzer in terms of recent establishment of startups globally and challenges of a breathalyzer are critically reviewed. This initiative is aimed at highlighting the challenges and scope for improvement to realize a high performance chemiresistive gas sensor for non-invasive disease diagnosis.

Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials.

Recent Progress in Graphene Derivatives/Metal Oxides Binary Nanocomposites Based Chemi-resistive Sensors for Disease Diagnosis by Breath Analysis

Background::

The scientific and clinical interest of breath analysis for non-invasive disease diagnosis has been focused by the scientific community over the past decade. This was due to the exhalation of prominent volatile organic compounds (VOCs) corresponding to the metabolic activities in the body and their concentration variation. To identify these biomarkers, various analytical techniques have been used in the past and the threshold concentration was established between a healthy and diseased state. Subsequently, various nanomaterials-based gas sensors were explored for their demand in quantifying these biomarkers for real-time, low cost and portable breathalyzers along with the essential sensor performances.

Methods::

We focus on the classification of graphene derivatives and their composites’ gas sensing efficiency for the application in the development of breathalyzers. The review begins with the feasibility of the application of nanomaterial gas sensors for healthcare applications. Then, we systematically report the gas sensing performance of various graphene derivatives/semiconductor metal oxides (SMO) binary nanocomposites and their optimizing strategies in selective detection of biomarkers specific to diseases. Finally, we provide insights on the challenges, opportunity and future research directions for the development of breathalyzers using other graphene derivatives/SMO binary nanocomposites.

Results::

On the basis of these analyses, graphene and its derivatives/metal oxides based binary nanocomposites have been a choice for gas sensing material owing to their high electrical conductivity and extraordinary thickness-dependent physicochemical properties. Moreover, the presence of oxygen vacancies in SMO does not only alter the conductivity but also accelerates the carrier transport rate and influence the adsorption behavior of target analyte on the sensing materials. Hence researchers are exploring the search of ultrathin graphene and metal oxide counterpart for high sensing performances.

Conclusion::

Their impressive properties compared to their bulk counterpart have been uncovered towards sensitive and selective detection of biomarkers for its use in portable breathalyzers.

Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis

The correlation between blood glucose and breath acetone suggested by several studies has spurred the research community to develop an electronic (e-nose) for diabetes diagnosis. Herein, we have validated the in-house graphene based sensors with known acetone concentration. The sensor performances such as sensitivity, selectivity and stability (SSS) suggested their potential use in acquiring breath print. The 10% higher mean saturation voltage for 30 diabetic subjects ensured a discrimination accuracy of 65% with a positive correlation (r = 0.88) between biochemically measured and non-invasively estimated (glycated haemoglobin) HbA1c. For the improvement of classification rate, thirteen features associated with the adsorption kinetics were extracted from the breathprint from each of the three sensors. The features given as an input to the Naïve Bayes classification model fetched an accuracy of 68.33%. Elimination of redundant features by distinction index and one-R feature ranking algorithm results in Naïve Bayes algorithm with improved performances. The success rate has improved to 70% using the subset of features ranked by one-R algorithm. These results indicated the use of feature ranking algorithms and prediction models for the improvement in accuracy of our in-house fabricated graphene based sensors.