Extended switching endurance of phase change memory through nano-confined cell structure.

Phase change memory has been regarded as a promising candidate for storage class memory application. However, the high switching current and limited switching endurance remain a critical challenge. In this work a switching endurance beyond 1.

Brain-inspired semantic data augmentation for multi-style images.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model.

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening.

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults.

Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency.

Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE.

Nanoscale Phase Change Material Array by Sub-Resolution Assist Feature for Storage Class Memory Application.

High density phase change memory array requires both minimized critical dimension (CD) and maximized process window for the phase change material layer. High in-wafer uniformity of the nanoscale patterning of chalcogenides material is challenging given the optical proximity effect (OPE) in the lithography process and the micro-loading effect in the etching process. In this study, we demonstrate an approach to fabricate high density phase change material arrays with half-pitch down to around 70 nm by the co-optimization of lithography and plasma etching process.

Polyaniline/reduced graphene oxide foams as metal-free cathodes for stable lithium-oxygen batteries.

Lithium-oxygen batteries (LOBs) are considered as next-generation energy storage devices owing to their high-energy densities, yet they generally suffer from low actual specific capacity and poor cycle performance. To solve these issues, a range of electrocatalysts have been introduced in the cathode to reduce the overpotential during charge/discharge cycles and minimize unwanted side reactions. Due to relative high costs and limited reserves of noble metals and their compounds, it is important to develop low-cost and efficient metal-free electrocatalysts.

Graphitic carbon nitride as polysulfide anchor and barrier for improved lithium-sulfur batteries.

The poor conductivity of sulfur and the shuttle effect of soluble polysulfides have considerably hindered the practical application of lithium-sulfur (Li-S) batteries. Here, we have fabricated a three-dimensional graphitic carbon nitride/reduced graphene oxide (GCN@rGO) network as the sulfur host in Li-S batteries, where the bifunctional GCN strongly binds polysulfides through a chemical interaction and catalyzes the redox reactions of polysulfides. Additionally, GCN coating is also applied to different membranes and when these GCN-coated-membranes (GCMs) are used as separators, they are found to effectively act as the polysulfide barrier to suppress the diffusion of polysulfide intermediates to the Li anode and thus ameliorate the shuttle effect.