Search Results (4)

Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R² while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications. Full article

Bacterial infections in marine fishes are linked to mass mortality issues; hence, rapid detection of an infection can contribute to achieving a faster diagnosis using point-of-care testing. There has been substantial interest in identifying diagnostic biomarkers that can be detected in major organs to predict bacterial infections. Aspartate was identified as an important biomarker for bacterial infection diagnosis in olive flounder (Paralichthys olivaceus) fish. To determine aspartate levels, an amperometric biosensor was designed based on bi-enzymes, namely, glutamate oxidase (GluOx) and aspartate transaminase (AST), which were physisorbed on copolymer reduced graphene oxide (P-rGO), referred to as enzyme nanosheets (GluOx-ASTENs). The GluOx-ASTENs were drop casted onto a Prussian blue electrodeposited screen-printed carbon electrode (PB/SPCE). The proposed biosensor was optimized by operating variables including the enzyme loading amount, coreactant (α-ketoglutarate) concentration, and pH. Under optimal conditions, the biosensor displayed the maximum current responses within 10 s at the low applied potential of −0.10 V vs. the internal Ag/AgCl reference. The biosensor exhibited a linear response from 1.0 to 2.0 mM of aspartate concentrations with a sensitivity of 0.8 µA mM⁻¹ cm⁻² and a lower detection limit of approximately 500 µM. Moreover, the biosensor possessed high reproducibility, good selectivity, and efficient storage stability. Full article

Microelectromechanical system (MEMS)-based piezoelectric energy harvesting (PEH) devices can convert the mechanical vibrations of their surrounding environment into electrical energy for low-power sensors. This electrical energy is amplified when the operation resonant frequency of the PEH device matches with the vibration frequency of its surrounding environment. We present the electromechanical modeling of two MEMS-based PEH devices to transform the mechanical vibrations of domestic washing machines into electrical energy. These devices have resonant structures with a T shape, which are formed by an array of multilayer beams and a ultraviolet (UV)-resin seismic mass. The first layer is a substrate of polyethylene terephthalate (PET), the second and fourth layers are Al and Pt electrodes, and the third layer is piezoelectric material. Two different types of piezoelectric materials (ZnO and PZT-5A) are considered in the designs of PEH devices. The mechanical behavior of each PEH device is obtained using analytical models based on the Rayleigh–Ritz and Macaulay methods, as well as the Euler–Bernoulli beam theory. In addition, finite element method (FEM) models are developed to predict the electromechanical response of the PEH devices. The results of the mechanical behavior of these devices obtained with the analytical models agree well with those of the FEM models. The PEH devices of ZnO and PZT-5A can generate up to 1.97 and 1.35 µW with voltages of 545.32 and 45.10 mV, and load resistances of 151.12 and 1.5 kΩ, respectively. These PEH devices could supply power to internet of things (IoT) sensors of domestic washing machines. Full article

Piezoelectric vibration energy harvesting (pVEH) microdevices can convert the mechanical vibrations to electrical voltages. In the future, these microdevices can provide an alternative to replace the electrochemical batteries, which cause contamination due to their toxic materials. We present the electromechanical modeling of a pVEH microdevice with a novel resonant structure for air conditioning vents at office buildings. This electromechanical modeling includes different multilayers and cross-sections of the microdevice resonator as well as the air damping. This microdevice uses a flexible substrate and it does not include toxics materials. The microdevice has a resonant structure formed by multilayer beams and U-shape proof mass of UV-resin (730 μm thickness). The multilayer beams contain flexible substrates (160 μm thickness) of polyethylene terephthalate (PET), two aluminum electrodes (100 nm thickness), and a ZnO layer (2 μm thickness). An analytical model is developed to predict the first bending resonant frequency and deflections of the microdevice. This model considers the Rayleigh and Macaulay methods, and the Euler-Bernoulli beam theory. In addition, the electromechanical behavior of the microdevice is determined through the finite element method (FEM) models. In these FEM models, the output power of the microdevice is obtained using different sinusoidal accelerations. The microdevice has a resonant frequency of 60.3 Hz, a maximum deflection of 2.485 mm considering an acceleration of 1.5 m/s², an output voltage of 2.854 V and generated power of 37.45 μW with a load resistance of 217.5 kΩ. An array of pVEH microdevices connected in series could be used to convert the displacements of air conditioning vents at office buildings into voltages for electronic devices and sensors. Full article