Advanced N-doped mesoporous molybdenum disulfide nanosheets and the enhanced lithium-ion storage performance

With the increasing interest in two-dimensional van der Waals materials, molybdenum disulfide (MoS2) has emerged as a promising material for electronic and energy storage devices. It suffers from poor cycling stability and low rate capability when used as an anode in lithium ion batteries. Here, N-doped MoS2 nanosheets with 2-8 atomic layers, increased interlayer distance, mesoporous structure and high surface area synthesised by a simple sol-gel method show an enhanced lithium storage performance, delivering a high reversible capacity (998.0 mA h g-1, 50 mA g-1), high rate performance (610 mA h g-1, 2 A g-1), and excellent cycling stability. The excellent lithium storage performance of the MoS2 nanosheets might be due to the better electrical and ionic conductivity and improved lithium ion diffusion which are related to their structural characteristics and high concentration N doping. The possible mechanism of the improved performance is proposed and discussed.

Inference of personal sensors in the internet of things

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Polymer architecture effect on sodium ion transport in PSTFSI-based ionomers: a molecular dynamics study

In this study, we investigated the effect of polymer architecture on the ion dynamics and local structure to understand the factors that might lead to the design of highly conductive and mechanically robust polyelectrolytes. Molecular dynamic simulations were undertaken on the sodium poly[(4-styrenesulfonyl) (trifluoromethanesulfonyl) imide] P(STFSINa) homopolymer and its copolymers with either ether or styrene spacer groups to investigate the spacer length and polarity dependence of Na-ion transport. Using a scaled charge model, we observed a continuous ion aggregate network in the homopolymer, which facilitates the fast ion dynamics despite the rigid polymer matrix. The longest spacer groups disrupt this percolating ionic network differently, with the ether group being more disruptive than the styrene group, and leading to more discrete ionic aggregates. The copolymer with the ether spacer was also found to result in an alternative Na-ion diffusion mechanism.