High-Precision Nonenzymatic Electrochemical Glucose Sensing Based on CNTs/CuO Nanocomposite

The measurement of blood glucose levels is essential for diagnosing and managing diabetes. Enzymatic and nonenzymatic approaches using electrochemical biosensors are used to measure serum or plasma glucose accurately. Current research aims to develop and improve noninvasive methods of detecting glucose in sweat that are accurate, sensitive, and stable. The carbon nanotube (CNT)-copper oxide (CuO) nanocomposite (NC) improved direct electron transport to the electrode surface in this study. The complex precipitation method was used to make this NC. X-ray diffraction (XRD) and scanning electron microscopy were used to investigate the crystal structure and morphology of the prepared catalyst. Using cyclic voltammetry and amperometry, the electrocatalytic activity of the as-prepared catalyst was evaluated. The electrocatalytic activity in artificial sweat solution was examined at various scan rates and at various glucose concentrations. The detection limit of the CNT-CuO NC catalyst was 3.90 µM, with a sensitivity of 15.3 mA cm−2 µM−1 in a linear range of 5–100 µM. Furthermore, this NC demonstrated a high degree of selectivity for various bio-compounds found in sweat, with no interfering cross-reactions from these species. The CNT-CuO NC, as produced, has good sensitivity, rapid reaction time (2 s), and stability, indicating its potential for glucose sensing.This publication was supported by Qatar University internal (Grant No. QUCG-CAM-21/22-1). The findings herein are solely the responsibility of the authors.Scopu

A Smart Colorimetric Platform for Detection of Methanol, Ethanol and Formic Acid

Carbon dioxide (CO2 ) is a greenhouse gas in the atmosphere and scientists are working on converting it to useful products, thereby reducing its quantity in the atmosphere. For converting CO2, different approaches are used, and among them, electrochemistry is found to be the most common and more efficient technique. Current methods for detecting the products of electrochemical CO2 conversion are time-consuming and complex. To combat this, a simple, cost-effective colorimetric method has been developed to detect methanol, ethanol, and formic acid, which are formed electrochemically from CO2 . In the present work, the highly efficient sensitive dyes were successfully established to detect these three compounds under optimized conditions. These dyes demonstrated excellent selectivity and showed no cross-reaction with other products generated in the CO2 conversion system. In the analysis using these three compounds, this strategy shows good specificity and limit of detection (LOD, ~0.03-0.06 ppm). A cost-effective and sensitive Internet of Things (IoT) colorimetric sensor prototype was developed to implement these dyes systems for practical and real-time application. Employing the dyes as sensing elements, the prototype exhibits unique red, green, and blue (RGB) values upon exposure to test solutions with a short response time of 2 s. Detection of these compounds via this new approach has been proven effective by comparing them with nuclear magnetic resonance (NMR). This novel approach can replace heavy-duty instruments such as high-pressure liquid chromatography (HPLC), gas chromatography (G.C.), and NMR due to its extraordinary selectivity and rapidity.Funding: This research was funded by Qatar National Research Fund (a member of the Qatar Foundation) grant number NPRP11S-1221-170116 and the APC was funded by Qatar National Research Fund.Scopu

Aluminium doped ZnO nanostructures for efficient photodegradation of indigo carmine and azo carmine G in solar irradiation

Aluminium doped zinc oxide (AZO) nanomaterials (AlxZn1-xO) with x fraction varying as 0.02 and 0.04 were synthesized using the auto-combustion method using glycine as a fuel. The synthesized catalysts were characterized with X-ray diffraction (XRD), UV–Visible Spectroscopy (UV–Vis), Raman spectroscopy, Photoluminescence (PL) spectroscopy, and High Resolution Transmission Electron Microscopy (HR-TEM). XRD results showed that synthesized materials possessed good crystallinity, while UV–VIS was employed to find the band gaps of synthesized materials. Raman was used to determine the vibrational modes in the synthesized nanoparticles, while TEM analysis was performed to study the morphology of the samples. Industrial effluents such as indigo carmine and azo carmine G were used to test the photodegradation ability of synthesised catalysts. Parameters such as the effect of catalyst loading, dye concentration and pH were studied. The reduction in crystallite size, band gap and increased lattice strain for the 4% AZO was the primary reason for the degradation in visible irradiation, degrading 97 and 99% equimolar concentrations of indigo carmine and azo carmine G in 140 min. The Al doped ZnO was found to be effective in faster degradation of dyes as compared to pure ZnO in presence of natural sunlight.This work was supported by an NPRP grant from the Qatar National Research Fund under NPRP12S-0131–190030

A paper-based colourimetric sensor for sodium sulfite detection in beverages

Sulfite is a common food additive that prevents oxidation from damaging food nutrients, and it has long been used in the food industry as a bleaching agent. It can harm the human body if taken wrongly or excessively. In this study, three dyes (cresol red, chlorophenol red, and bromocresol green) were explored to analyze the presence of sodium sulfite (SS) in an inexpensive, disposable paper sensor with a lower visible detection limit of 0.05 M. This visual paper sensor detects sodium sulfite with high selectivity and sensitivity at room temperature. An IoT-based sensor was also developed to practically apply the developed method, which is rapid and low-cost and can replace heavy-duty instruments. Both these sensors can substantially impact scenarios such as food quality monitoring and detecting sodium sulfite in medicinal items. Graphical Abstract: [Figure not available: see fulltext.

Self-sanitizing reusable glove via 3D-printing and common mold making method

In health care and public health practice, it is critical to settings control practices that are critical to reducing the transmission of infections through cross-contamination. To provide protection from cross-contamination, use and throw gloves are routinely used. However, single-time use and inconsistent sanitization of used gloves remain a large problem and elevate the risk of catching viruses, germs, pathogens, and contaminants. The study reports reusable self-sanitizing gloves via 3D-printing and common hand molding methods. The major contribution is frequent self-sanitization of gloves without any manual intervention. The elastomeric material is used for fabricating gloves and continuous channels are embedded within the elastomeric material that runs through the entire glove surface, covering the front, back, and fingers. Elastomeric material allows the engagement of fingers for gripping objects. While the embedded channel is provided with uniformly spaced openings to eject the sanitizing solution. The glove surface is textured with a porous morphology that acts as mini and micro reservoirs for sterilizing solution ejected through embedded channel opening. The embedded channel is connected to a sanitizing solution storage tank. The incorporation of sanitizing solution storage tank enables its usage over a longer period. This uniquely constructed design of the gloves even assists in the effective sterilization of infected surface that comes in contact with the gloves. The gloves can be customized to improve comfortability by fabricating them from the 3D-printed mound developed based on the palm size of the user. The developed technology can be used by individuals working in hospitals, the transport sector, delivery units, schools, offices, industries, etc. We strongly believe that this technology will be highly useful in minimizing the risk of getting infected through cross-contamination and will help in maintaining hygienic as well as safe surroundings.This work was supported by the RRC-2-063-133 grant from the Qatar National Research Fund (a member of Qatar Foundation). Open Access funding was provided by the Qatar National Library

Review of Progress and Prospects in Research on Enzymatic and Non- Enzymatic Biofuel Cells; Specific Emphasis on 2D Nanomaterials

Energy generation from renewable sources and effective management are two critical challenges for sustainable development. Biofuel Cells (BFCs) provide an elegant solution by combining these two tasks. BFCs are defined by the catalyst used in the fuel cell and can directly generate electricity from biological substances. Various nontoxic chemical fuels, such as glucose, lactate, urate, alcohol, amines, starch, and fructose, can be used in BFCs and have specific components to oxide fuels. Widely available fuel sources and moderate operational conditions make them promise in renewable energy generation, remote device power sources, etc. Enzymatic biofuel cells (EBFCs) use enzymes as a catalyst to oxidize the fuel rather than precious metals. The shortcoming of the EBFCs system leads to integrated miniaturization issues, lower power density, poor operational stability, lower voltage output, lower energy density, inadequate durability, instability in the long-term application, and incomplete fuel oxidation. This necessitates the development of non-enzymatic biofuel cells (NEBFCs). The review paper extensively studies NEBFCs and its various synthetic strategies and catalytic characteristics. This paper reviews the use of nanocomposites as biocatalysts in biofuel cells and the principle of biofuel cells as well as their construction elements. This review briefly presents recent technologies developed to improve the biocatalytic properties, biocompatibility, biodegradability, implantability, and mechanical flexibility of BFCs.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under UREP grant #UREP28-052-2-020. The statements made herein are solely the responsibility of the authors

Electrochemical Nonenzymatic Acetone Sensing: A Novel Approach of Biosensor Platform Based on CNT/CuO Nanosystems

A promising approach for noninvasive medical diagnosis may be to measure the VOCs produced by metabolic changes or pathological disorders in human sweat, such as measuring the acetone levels in the presence of diabetes. Acetone is a by-product of fat catabolism and serves as an indicator of ketosis and diabetes. The measurement of acetone may be used instead of glucose monitoring. Current research aims to develop and improve noninvasive methods of detecting acetone in sweat that are accurate, sensitive, and stable. The carbon nanotubes (CNTs)–copper oxide (CuO) nanocomposite (NC) improves direct electron transport to the electrode surface in this study. The complex-precipitation method is used to make this NC. X-ray diffraction (XRD) and scanning electron microscopy (SEM) are used to investigate the crystal structure and morphology of the prepared catalyst. Using cyclic voltammetry (CV) and amperometry, the electrocatalytic activity of the as-prepared catalyst is evaluated. The electrocatalytic activity in artificial sweat solution is examined at various scan rates and acetone concentrations. The detection limit of the CNTs-CuO NC catalyst is 0.05 mm, with a sensitivity of 16.1 mA cm−2 µm−1 in a linear range of 1–50 mm. Furthermore, this NC demonstrates a high degree of selectivity for various biocompounds found in sweat, with no interfering cross-reactions from these species. The CNT-CuO NC, as produced, has good sensitivity, rapid reaction time (2 s), and stability, indicating its potential for acetone sensing.This publication was supported by Qatar University internal grant No. QUCG-CAM-21/22-1. The findings herein are solely the responsibility of the authors.Scopu

High-Precision Nonenzymatic Electrochemical Glucose Sensing Based on CNTs/CuO Nanocomposite

The measurement of blood glucose levels is essential for diagnosing and managing diabetes. Enzymatic and nonenzymatic approaches using electrochemical biosensors are used to measure serum or plasma glucose accurately. Current research aims to develop and improve noninvasive methods of detecting glucose in sweat that are accurate, sensitive, and stable. The carbon nanotube (CNT)-copper oxide (CuO) nanocomposite (NC) improved direct electron transport to the electrode surface in this study. The complex precipitation method was used to make this NC. X-ray diffraction (XRD) and scanning electron microscopy were used to investigate the crystal structure and morphology of the prepared catalyst. Using cyclic voltammetry and amperometry, the electrocatalytic activity of the as-prepared catalyst was evaluated. The electrocatalytic activity in artificial sweat solution was examined at various scan rates and at various glucose concentrations. The detection limit of the CNT-CuO NC catalyst was 3.90 µM, with a sensitivity of 15.3 mA cm−2 µM−1 in a linear range of 5–100 µM. Furthermore, this NC demonstrated a high degree of selectivity for various bio-compounds found in sweat, with no interfering cross-reactions from these species. The CNT-CuO NC, as produced, has good sensitivity, rapid reaction time (2 s), and stability, indicating its potential for glucose sensing.This publication was supported by Qatar University internal (Grant No. QUCG-CAM-21/22-1). The findings herein are solely the responsibility of the authors.Scopu

A Path towards Timely VAP Diagnosis: Proof-of-Concept Study on Pyocyanin Sensing with Cu-Mg Doped Graphene Oxide

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO (GO - Cu - Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.This paper was supported by an International Research Collaboration Co-Fund (IRCC) grant of Qatar University under grant no. IRCC-2022-569. This work was additionally supported by the Qatar National Research Fund under grant no. MME03-1226-210042. The findings achieved and statements made herein are solely the responsibility of the authors.Scopu

Research Trends in Smart Cost-Effective Water Quality Monitoring and Modeling: Special Focus on Artificial Intelligence

Numerous conventional methods are available for analyzing various water quality parameters to determine the water quality index. However, ongoing surveillance is necessary for large bodies of water. A water quality monitoring system supports a robust surface and groundwater ecosystem. Various tactics are used to improve aquatic habitats: identification of the precise chemical pollutants released into the aquatic environment; advancements in assessing ecological effects; and working on ways to enhance water quality through informing the public, communities, businesses, etc. In order to save the marine ecosystem and those who entirely depend on these enormous bodies of water, it is also crucial to continuously handle many data sets of water quality metrics. To predict the water quality index, this review paper provides an overview of water quality monitoring, the modeling and numerous sensors employed, and various artificial intelligence approaches. Various water quality models were proposed to assess pH, a few components, and alkalinity. Additionally, handling raw information for surface and groundwater quality metrics was studied using artificial intelligence techniques like neural networks