Passive Small Direct Alcohol Fuel Cells for Low-Power Portable Applications: Assessment Based on Innovative Increments since 2018

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Research Progress in Enzyme Biofuel Cells Modified Using Nanomaterials and Their Implementation as Self-Powered Sensors

Enzyme biofuel cells (EBFCs) can convert chemical or biochemical energy in fuel into electrical energy, and therefore have received widespread attention. EBFCs have advantages that traditional fuel cells cannot match, such as a wide range of fuel sources, environmental friendliness, and mild reaction conditions. At present, research on EBFCs mainly focuses on two aspects: one is the use of nanomaterials with excellent properties to construct high-performance EBFCs, and the other is self-powered sensors based on EBFCs. This article reviews the applied nanomaterials based on the working principle of EBFCs, analyzes the design ideas of self-powered sensors based on enzyme biofuel cells, and looks forward to their future research directions and application prospects. This article also points out the key properties of nanomaterials in EBFCs, such as electronic conductivity, biocompatibility, and catalytic activity. And the research on EBFCs is classified according to different research goals, such as improving battery efficiency, expanding the fuel range, and achieving self-powered sensors.

Novel Cu and Pd-Cu Catalysts Supported on Multi-Walled Carbon Nanotubes for Steam Reforming and Decomposition of Methanol

In this study, multi-walled carbon nanotubes (MWCNTs) were prepared by chemical vapour deposition (CVD) using acetylene as a carbon source over an iron catalyst. As-prepared MWCNTs were used to support modern mono-copper, palladium, and bimetallic palladium-copper catalysts, and their feasibility for hydrogen production was tested during steam reforming of methanol (SRM) and methanol decomposition (DM). The structural characteristics of the MWCNTs were evaluated using the SEM and XRD methods. The physicochemical properties of the monometallic and bimetallic catalysts were analysed using the TPR and XRD methods. The promotion effect of palladium on methanol conversion rate and H2 productivity in the case of the copper catalysts was demonstrated. The enhanced activity of the Cu/MWCNTs after palladium promotion was due to the formation of Pd-Cu alloy compound.