Fabrication of flame-retardant and water-resistant nanopapers through electrostatic complexation of phosphorylated cellulose nanofibers and chitin nanocrystals.

Biogenic, sustainable two-dimensional architectures, such as films and nanopapers, have garnered considerable interest because of their low carbon footprint, biodegradability, advanced optical/mechanical characteristics, and diverse potential applications. Here, bio-based nanopapers with tailored characteristics were engineered by the electrostatic complexation of oppositely charged colloidal phosphorylated cellulose nanofibers (P-CNFs) and deacetylated chitin nanocrystals (ChNCs). The electrostatic interaction between anionic P-CNFs and cationic ChNCs enhanced the stretchability and water stability of the nanopapers. Correspondingly, they exhibited a wet tensile strength of 17.7 MPa after 24 h of water immersion. Furthermore, the nanopapers exhibited good thermal stability and excellent self-extinguishing behavior, triggered by both phosphorous and nitrogen. These features make the nanopapers sustainable and promising structures for application in advanced fields, such as optoelectronics.

A novel hand gesture recognition method based on 2-channel sEMG.

Hand gesture recognition is getting more and more important in the area of rehabilitation and human machine interface (HMI). However, most current approaches are difficult to achieve practical application because of an excess of sensors. In this work, we proposed a method to recognize six common hand gestures and establish the optimal relationship between hand gesture and muscle by utilizing only two channels of surface electromyography (sEMG). We proposed an integrated approach to process the sEMG data including filtering, endpoint detection, feature extraction, and classifier. In this study, we used one-order digital lowpass infinite impulse response (IIR) filter with the cutoff frequency of 500 Hz to extract the envelope of the sEMG signals. The energy was utilized as a feature to detect the endpoint of motion. The short-time energy, zero-crossing rate and linear predictive coefficient (LPC) with 12 levels were chosen as the features and back propagation (BP) neural network was utilized to classify. In order to test the method, five subjects were involved in the experiment to test the hypothesis. With the proposed method, 96.41% to 99.70% recognition rate was obtained. The experimental results revealed that the proposed method is highly efficient both in sEMG data acquisition and hand motions recognition, and played a role in promoting hand rehabilitation and HMI.