Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPerformance-Enhanced Non-Enzymatic Glucose Sensor Based on Graphene-Heterostructure
372
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of Graphene (G)/Platinum Oxide (PtO)/n-Silicon (Si) heterostructure. The sensor was tested with different glucose concentrations and interfering solutions to investigate its sensitivity and selectivity. Different structures of the device were studied by adjusting the platinum oxide film thickness to investigate its catalytic activity. It was found that the film thickness plays a significant role in the efficiency of glucose oxidation and hence in overall device sensitivity. 0.8-2 uA output current was obtained in the case of 4-10 mM with a sensitivity of 0.2 uA/mM.cm2. Besides, results have shown that 0.8 uA and 15 uA were obtained by testing 4 mM glucose on two different PtO thicknesses, 30 nm, and 50 nm, respectively. The sensitivity of the device was enhanced by 150% (i.e., up to 30 uA/mM.cm2) by increasing the PtO layer thickness. This was attributed to both the increase of the number of active sites for glucose oxidation as well as the increase in the graphene layer thickness, which leads to enhanced charge carriers concentration and mobility. Moreover, theoretical investigations were conducted using the Density Function Theory (DFT) to understand the detection method and the origins of selectivity better. The working principle of the sensors puts it in a competitive position with other non-enzymatic glucose sensors. DFT calculations provided a qualitative explanation of the charge distribution across the graphene sheet within a system of a platinum substrate with D-glucose molecules above. The proposed G/PtO/n-Si heterostructure has proven to satisfy these factors, which opens the door for further developments of more reliable non-enzymatic glucometers for continuous glucose monitoring systems.
rate research
Read More
A novel mediator-free, non-enzymatic electrochemical sensor, based on a graphene-Schottky junction, was fabricated for glucose detection. The sensor offers a promising alternative to the conventional enzyme-catalyzed electrochemical continuous glucose monitoring systems (CGM), as it overcomes many of the drawbacks attributed to the enzymatic nature; namely, irreversibility, drift, and interference with body fluids, which affect their accuracy, reliability and longevity. Enhanced performance of the sensors is demonstrated through the band interaction at the graphene-Schottky junction, which yields stronger forward/reverse currents in response to 50 {mu}L glucose drop. Under optimized conditions, the linear response of the sensor to glucose concentration was valid in the range from 0 to 15 mmol/L with a detection limit of 0.5 mmol/L. The results indicated that the proposed sensor provided a highly sensitive, more facile method with good reproducibility for continuous glucose detection.
Porous, atomically thin graphene membranes have interesting properties for filtration and sieving applications because they can accommodate small pore sizes, while maintaining high permeability. These membranes are therefore receiving much attention for novel gas and water purification applications. Here we show that the atomic thickness and high resonance frequency of porous graphene membranes enables an effusion based gas sensing method that distinguishes gases based on their molecular mass. Graphene membranes are used to pump gases through nanopores using optothermal forces. By monitoring the time delay between the actuation force and the membrane mechanical motion, the permeation time-constants of various gases are shown to be significantly different. The measured linear relation between the effusion time constant and the square root of the molecular mass provides a method for sensing gases based on their molecular mass. The presented microscopic effusion based gas sensor can provide a small, low-power alternative for large, high-power, mass-spectrometry and optical spectrometry based gas sensing methods.
Diodes made of heterostructures of the 2D material graphene and conventional 3D materials are reviewed in this manuscript. Several applications in high frequency electronics and optoelectronics are highlighted. In particular, advantages of metal-insulator-graphene (MIG) diodes over conventional metal-insulator-metal diodes are discussed with respect to relevant figures-of-merit. The MIG concept is extended to 1D diodes. Several experimentally implemented radio frequency circuit applications with MIG diodes as active elements are presented. Furthermore, graphene-silicon Schottky diodes as well as MIG diodes are reviewed in terms of their potential for photodetection. Here, graphene-based diodes have the potential to outperform conventional photodetectors in several key figures-of-merit, such as overall responsivity or dark current levels. Obviously, advantages in some areas may come at the cost of disadvantages in others, so that 2D/3D diodes need to be tailored in application-specific ways.
In this work we study thermoelectric properties of graphene nanoribbons with side-attached organic molecules. By adopting a single-band tight binding Hamiltonian and the Greens function formalism, we calculated the transmission and Seebeck coefficients for different hybrid systems. The corresponding thermopower profiles exhibit a series of sharp peaks at the eigenenergies of the isolated molecule. We study the effects of the temperature on the thermoelectric response, and we consider random configurations of molecule distributions, in different disorder regimes. The main characteristics of the thermopower are not destroyed under temperature and disorder, indicating the robustness of the system as a proposed molecular thermo-sensor device.
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
