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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.
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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.
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.
Harvesting all sources of available clean energy is an essential strategy to contribute to healing current dependence on non-sustainable energy sources. Recently, triboelectric nanogenerators (TENGs) have gained visibility as new mechanical energy harvester offering a valid alternative to batteries, being particularly suitable for portable devices. Here, the increased capacitance of a few-layer graphene-based electrode is obtained by incorporating nitrogen-doped graphene (N_graphene), enabling a 3_fold enhancement in TENGs power output. The dependence of TENGs performance on the electronic properties of different N_graphene types, varying in the doping concentration and in the relative content of N-pyridinic and N-graphitic sites is investigated. These sites have different electron affinities, and synergistically contribute to the variation of the capacitive and resistive properties of N-graphene and consequently, TENG performance. It is demonstrated that the power enhancement of the TENG occurs when the N_graphene, an n-semiconductor, is interfaced between the positive triboelectric material and the electrode, while a deterioration of the electrical performance is observed when it is placed at the interface with the negative triboelectric material. This behavior is explained in terms of the dependence of N_graphene quantum capacitance on the electrode chemical potential which shifts according to the opposite polarization induced at the two electrodes upon triboelectrification.
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies. Here we present a case study, based on a data set of conformers of amino acids and dipeptides, of how machine-learning techniques can help addressing these issues. We will exploit a recently developed strategy to define a metric between structures, and use it as the basis of both clustering and dimensionality reduction techniques showing how these can help reveal structure-property relations, identify outliers and inconsistent structures, and rationalise how perturbations (e.g. binding of ions to the molecule) affect the stability of different conformers.
While the emerging evidence indicates that the pathogenesis of Parkinsons disease (PD) is strongly correlated to the accumulation of alpha-synuclein ({alpha}-syn) aggregates, there has been no clinical success in anti-aggregation agents for the disease to date. Here we show that graphene quantum dots (GQDs) exhibit anti-amyloid activity via direct interaction with {alpha}-syn. Employing biophysical, biochemical, and cell-based assays as well as molecular dynamics (MD) simulation, we find that GQDs have notable potency in not only inhibiting fibrillization of {alpha}-syn but also disaggregating mature fibrils in a time-dependent manner. Remarkably, GQDs rescue neuronal death and synaptic loss, reduce Lewy body (LB)/Lewy neurite (LN) formation, ameliorate mitochondrial dysfunctions, and prevent neuron-to-neuron transmission of {alpha}-syn pathology induced by {alpha}-syn preformed fibrils (PFFs) in neurons. In addition, in vivo administration of GQDs protects against {alpha}-syn PFFs-induced loss of dopamine neurons, LB/LN pathology, and behavioural deficits through the penetration of the blood-brain barrier (BBB). The finding that GQDs function as an anti-aggregation agent provides a promising novel therapeutic target for the treatment of PD and related {alpha}-synucleinopathies.
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