Combinatorial selectivity with an array of phthalocyanines functionalized TiO2/ZnO heterojunction thin film sensors.

The development of electronic noses requires the control of the selectivity pattern of each sensor of the array. Organic chemistry offers a manifold of possibilities to this regard but in many cases the chemical sensitivity is not matched with the response of electronic sensor. The combination of organic and inorganic materials is an approach to transfer the chemical sensitivities of the sensor to the measurable electronic signals. In this paper, this approach is demonstrated with a hybrid material made of phthalocyanines and a bilayer structure of ZnO and TiO2. Results show that the whole spectrum of sensitivity of phthalocyanines results in changes of the resistance of the sensor, and even the adsorption of compounds, such as hexane, which cannot change the resistance of pure phthalocyanine layers, elicits changes of the sensor resistance. Furthermore, since phthalocyanines are optically active, the sensitivity in dark and visible light are different. Thus, operating the sensor in dark and light two different signals per sensors can be extracted. As a consequence, an array of 3 sensors made of different phthalocyanines results in a virtual array of six sensors. The sensor array shows a remarkable selectivity respect to a set of test compounds. Principal component analysis scores plot illustrates that hydrogen bond basicity and dispersion interaction are the dominant mechanisms of interaction.

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.

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.

An overview on the exponential growth of non-invasive diagnosis of diabetes mellitus from exhaled breath by nanostructured metal oxide Chemi-resistive gas sensors and µ-preconcentrator.

The characterization of acetone in exhaled breath reflects the internal metabolism of glucose in bloodstream and airways. This phenomenon provides a great potential for non-invasive diagnosis of diabetes mellitus and has inspired medical sodalities as an alternative diagnostic tool. This review discusses about the origination of acetone in breath, its correlation with blood glucose level along with the ways to collect breath sample. Furthermore, we also discuss the detection of acetone by chemical sensors with emphasis on the use of pre-concentrators on a single lab-on-chip for the diagnosis of diabetes mellitus. Finally, this review outlines the future directions for the detection of acetone from exhaled breath. The first part of the review introduces the biochemistry and prevalence of diabetes in India along with the existing techniques to estimate the concentration of acetone. The second part focuses on the semiconducting metal oxide and polymer gas sensors which discusses about tailoring the dynamic sensitivity range and selectivity towards acetone in breath. The third part elaborates on the ways to pre-concentrate the target biomarkers along with future perspectives for non-invasive diabetes diagnosis. Finally we also provide the perspectives on future challenges to make it to clinical practice. Graphical abstract .

Comparative Study on the Preparation and Gas Sensing Properties of Reduced Graphene Oxide/SnO2 Binary Nanocomposite for Detection of Acetone in Exhaled Breath.

Reduced graphene oxide/tin dioxide (RGO/SnO2) binary nanocomposite for acetone sensing performance was successfully studied and applied in exhaled breath detection. The influence of structural characteristics was explored by synthesizing the composite (RGO/SnO2) using the solvothermal method (GS-I) and the hydrothermal method (GS-II) by the chemical route and mechanical mixing, respectively. The nanocomposites characterized by X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), Fourier transform-infrared spectroscopy (FT-IR), and Brunauer-Emmett-Teller (BET) revealed that GS-I exhibited better surface area, surface energy and showed enhanced gas response than GS-II at an operating temperature of 200 °C. These sensors exhibited comparable response in humid environment as well, suitable for acetone sensing in exhaled breath that clearly distinguishes between healthy and diabetes subjects. The enhanced response at lower concentrations was attributed to the synergistic effect at the RGO/SnO2 interface. These results indicate that modification in the structural characteristics of RGO/SnO2 nanocomposite enhances the sensing property. Furthermore, it proved to be a promising material for potential application for point-of care, noninvasive diabetes detection.

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.

Several breath analysis studies have suggested a correlation between blood glucose (BG) levels and breath acetone, indicating acetone as a primary biomarker in exhaled breath for diabetes diagnosis. Herein, we have (i) fabricated and validated graphene-based chemi-resistive sensors for selective and sensitive detection of acetone, (ii) performed offline breath analysis by a static gas sensing set-up to acquire olfactory signals, and (iii) developed an LED-based portable on/off binary e-nose system for pre-screening diabetes through online analysis. The fabricated sensors showed selective detection for acetone with high sensitivity (5.66 for 1 ppm acetone vapor) and fast response and recovery times (10 s and 12 s) at low concentrations. The sensor responses of end tidal fractional breath (collected in Tedlar bags) in the fasting and postprandial conditions were compared with BG levels and glycated hemoglobin (HbA1c) levels taken at the same time in 30 volunteers (13 healthy and 17 diabetic subjects). The mean sensor responses of the diabetic subjects as obtained by offline analysis were 1.1 times higher than those of the healthy subjects. The optimal regression equation framed with the significant correlating variables for HbA1c estimation achieved an accuracy of 66.67%. The online breath analysis by on/off binary prototype exhibited an accuracy of 60.51%. Though there exists a minimal uncertainty in classification, the on/off type portable prototype is easy to operate, gives a quicker response with a refresh/recovery rate of 19 s and can be used for preliminary diagnosis, and can be used for preliminary diagnosis. This inexpensive sensor technology may revolutionize personalized medicine in the near future and greatly benefit the underprivileged.