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Assessment of glucose concentration is important in the diagnosis and treatment of diabetes. Since the introduction of enzymatic glucose biosensors, scientific and technological advances in nanomaterials have led to the development of new generations of glucose sensors. This field has witnessed major developments over the last decade, as the novel nanomaterials are capable of efficiently catalyzing glucose directly (i.e., act as artificial enzymes, therefore defined nanozymes) or to entrap enzymes that are able to oxidize glucose. Among other nanomaterials, metal-organic frameworks (MOFs) have recently provided a tremendous basis to construct glucose sensing devices. MOFs are large porous crystalline compounds with versatile structural and tuneable chemical properties. In addition, they possess catalytic, peroxidase-like, and electrochemical redox activity. This review comprehensively summarizes the general characteristics of MOFs, their subtypes, and MOF composites, as well as MOF-derived materials employed to construct electrochemical, optical, transistor, and microfluidic devices for the detection of glucose. They include enzymatic, nonenzymatic, wearable, and flexible sensing devices and methods. The review also outlines the design and synthesis of MOFs and the working principles of the different transductionbased glucose sensors and highlights the current challenges and future perspectives.

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This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the incylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved.