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

CuO-ZnO p-n junctions for accurate prediction of multiple volatile organic compounds aided by machine learning algorithms

Detection and quantification of multiple volatile organic compounds (VOCs) are emerging as critical requirements for several niche applications including healthcare. It is desirable to get multiple gases identified rapidly and using minimum number of sensors. Heterojunctions of metal oxides are still among the top-picks for efficient VOC sensing because they unfold exciting sensing characteristics in addition to enhanced response. This work reports the synthesis of nanostructures of CuO, ZnO, and three CuO-ZnO p-n junctions having different weight percentages (1-0.5, 1-1, and 0.5-1) of CuO and ZnO, using a facile one-pot hydrothermal method. The nanomaterials were characterized using X-ray diffraction, field emission scanning electron microscopy, and UV-Visible spectroscopy. Resistive sensors were fabricated of all five nanomaterials and were tested for 25-200 ppm of four VOCs - isopropanol, methanol, acetonitrile, and toluene. The CuO and CuO-ZnO (1-0.5) sensors showed the highest response for isopropanol (7.5-65.3% and 19-122%, respectively) at 250 °C, CuO-ZnO (1-1) and CuO-ZnO (0.5-1) exhibited the highest responses for methanol (9-60%) and isopropanol (15-120%), respectively at 350 °C, and the intrinsic ZnO showed maximum response to toluene (29-76%) at 400 °C. All the sensing layers were observed to exhibit finite responses to the other three VOCs so, an attempt to classify and quantify the four VOCs accurately was made using support vector machine (SVM) and multiple linear regression (MLR) algorithms. The response and response times of two sensors were observed to be sufficient as inputs to the machine learning algorithms for classifying and quantifying all the four VOCs. The combinations of (CuO-ZnO (1-0.5) & (1-1) and CuO-ZnO (1-1) & (0.5-1) demonstrated the highest classification accuracy of 98.13% with SVM. The combination of CuO-ZnO (1-0.5) & (1-1) demonstrated the best quantification of the four VOCs using MLR.

TiO2 nanoflower photocatalysts: Synthesis, modifications and applications in wastewater treatment for removal of emerging organic pollutants

Titanium dioxide (TiO₂) has been considered as one of the most promising photocatalysts nanomaterials and is being used in a variety of fields of energy and environment under sunlight irradiation via photocatalysis. Highly efficient photocatalytic materials require the design of the proper structure with excellent morphology, interfacial structures, optical and surface properties, etc. Which are the key points to realize effective light-harvesting for photocatalytic applications. Hierarchical TiO₂ based nanoflower structures (i.e., 3D nanostructures) possess such characteristics and have attracted much attention in recent years. The uniqueness of TiO₂ nanoflowers (NFs) with a coarse texture and arranged structures demonstrates higher photocatalytic activity. This review deals with the hydrothermal synthesis of 3D TiO₂ NFs and effect of shape/size as well as various key synthesis parameters to improve their optoelectronic and photocatalytic properties. Furthermore, to improve their photocatalytic properties, various strategies such as doping engineering and heterojunction/nanocomposite formation with other functional nanomaterials have been discussed followed by their potential applications in photocatalytic degradation of various emerging pollutants discharged into the wastewater from various sources. Importance of such 3D nanoarchitecutres and future research in other fields of current interest in environments are discussed.

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.