Preparation and Characterization of Chinese Leek Extract Incorporated Cellulose Composite Films.

This study aimed to prepare microcrystalline cellulose (MCC) films with good mechanical properties via plasticization using a Chinese leek (CL, Allium tuberosum) extract. The microstructure, crystal structure, mechanical properties, barrier ability, and thermal properties of the films were investigated. The chemical structure analysis of CL extract showed the existence of cellulose, lignin, and low-molecular-weight substances, such as polysaccharides, pectins, and waxes, which could act as plasticizers to enhance the properties of MCC:CL biocomposite films. The results of scanning electron microscopy and atomic force microscopy analyses indicated the good compatibility between MCC and CL extract. When the volume ratio of MCC:CL was 7:3, the MCC:CL biocomposite film exhibited the best comprehensive performance in terms of water vapor permeability (2.11 × 10-10 g/m·s·Pa), elongation at break (13.2 ± 1.8%), and tensile strength (24.7 ± 2.5 MPa). The results of a UV absorption analysis demonstrated that the addition of CL extract improved the UV-shielding performance of the films. Therefore, this work not only proposes a facile method to prepare MCC films with excellent mechanical properties via plasticization using CL extract but also broadens the potential applications of MCC films in the packaging area.

Construction of EVA/chitosan based PEG-PCL micelles nanocomposite films with controlled release of iprodione and its application in pre-harvest treatment of grapes.

A novel nanocomposite poly(ethylene-co-vinyl acetate) (EVA) film with controlled in vitro release of iprodione (ID) was prepared. Chitosan (CS) was used as the reinforcement which enhances the water and oxygen permeability of films. ID loaded poly(ethylene glycol)-poly(ε-caprolactone) (PEG-PCL) (IPP) micelles were used as the drug carrier which endows the films with antifungal and controlled release ability. IPP micelles with spherical shape and uniform size were obtained, and the maximum encapsulation efficacy (EE) was 91.17 ± 5.03% by well controlling the feeding amount of ID. Incorporation CS could improve the oxygen and moisture permeability of films, and the maximum oxygen permeability (OP) and water vapor transmission rate (WVTR) were 477.84 ± 13.03 cc/(m2·d·0.1 MPa) and 8.60 ± 0.25 g m-2 d-1, respectively. After loading IPP micelles, the films showed an improved antifungal ability and temperature-sensitive drug release behavior, and were found to enhance the quality of grapes by pre-harvest spraying.

Wavelength detection of model-sharing fiber Bragg grating sensor networks using long short-term memory neural network.

In this paper, an effective wavelength detection approach based on long short-term memory (LSTM) network is proposed for fiber Bragg grating (FBG) sensor networks. The FBG sensor network utilizes a model-sharing mechanism, where the whole spectral wavelength is divided into several shareable regions and spectral overlap is allowed in each region. LSTM, a representative recurrent neural network in deep learning, is applied to learn the features directly from the spectra of FBGs and build the wavelength detection model. By feeding the spectra sequentially into the well-trained model, the Bragg wavelengths of FBGs can be quickly determined under overlap. The obtained LSTM model can be repeatedly used without re-training to improve the multiplexing capability. The results demonstrate that the LSTM-based method can realize high-accuracy and high-speed wavelength detection in the spectral overlapping situations. The proposed approach offers a flexible tool to enhance the sensing capacity of FBG sensor networks.