Highly efficient dissolution and reinforcement mechanism of robust and transparent cellulose films for smart packaging.

The packaging of fresh foods increasingly focuses on renewable and eco-friendly cellulose films, but their low dissolution efficiency and weak mechanical strength greatly limit their wide application, which also cannot be used for smart packaging. Here, a highly efficient synergistic chloride-salt dissolution method was proposed to fabricate robust, transparent, and smart cellulose films. Cellulose films with appropriate Ca2+ concentration exhibited robust mechanical strength, better thermal stability, high transparency and crystallinity. The metal chelation of Ca2+ with cellulose chains could induce cellulose chain arrangement during the cellulose regeneration process. Particularly, compared to pure cellulose films, the tensile strength and elongation at break of cellulose films with suitable Ca2+ were increased by 167 % and 200 %, respectively. Moreover, optimal cellulose films can be used to reflect the quality of the fruit by detecting changes in ethanol gas. Hence, a novel strategy is presented to fabricate robust and transparent cellulose films with great potential application for smart packaging.

Athermal panoramic annular lens design with a thermal analysis method.

An athermal 360∘×(30∘-100∘) and F/3.5 panoramic annular lens (PAL) system is designed. Through the optical mechanical thermal analysis results based on finite element analysis (FEA), it is expected that the system will have excellent performance under extreme temperature fluctuations. Simulation shows that the system is thermally insensitive in the temperature range from -40∘ C to 60°C, consistently providing great imaging capability with the modulation transfer function (MTF) value at 133 lp/mm greater than 0.3. The proposed design and analysis workflow contains an evaluation of thermal optical performance with a higher accuracy, thus having significance in future athermal optical design. We expect the designed PAL system to have a broad application prospect in the field of outdoor applications, including automatic navigation of vehicles and all-weather surveillance systems.

Robust and versatile superhydrophobic cellulose-based composite film with superior UV shielding and heat-barrier performances for sustainable packaging.

Replacing single-use plastic delivery bags (SPDBs) with cellulose-based materials is an effective strategy to reduce environmental pollution. However, the inherent hydrophilicity and ultralow mechanical strength of cellulose materials limit its development. In this study, zinc oxide (ZnO)-cellulose composite films were successfully prepared through "two-step strategy" of lotus leaves structure simulation, including deposition of micro-nano ZnO particles and stearic acid (STA) modification. Well-dispersed micro-nano ZnO particles with stick-like structure were anchored in the ZnO-cellulose composite film prepared at 90 °C (CF-90). Due to the special structural design and strong interaction between the cellulose and micro-nano ZnO particles, the CF-90 showed higher mechanical property (a 47.8 % improvement in the tensile strength). Impressively, CF-90 also exhibited great UV shielding properties with larger UPF value of 1603.98 and superhigh heat-barrier performance. Moreover, CF-90 obtained excellent superhydrophobicity with a water contact angle of 163.6° by further modification. Consequently, the versatile cellulose-based material bringing a dawn on application of sustainable packaging materials for express delivery industry.

Homogeneous wet-spinning construction of skin-core structured PANI/cellulose conductive fibers for gas sensing and e-textile applications.

Fiber-based wearable electronic textiles have broad applications, but non-degradable substrates may contribute to electronic waste. The application of cellulose-based composite fibers as e-textiles is hindered by the lack of fast and effective preparation methods. Here, we fabricated polyaniline (PANI)/cellulose fibers (PC) with a unique skin-core structure through a wet-spinning homogeneous blended system. The conductive network formation was enabled at a mere 1 wt% PANI. Notably, PC15 (15 wt% PANI) shows higher electrical conductivity of 21.50 mS cm-1. Further, PC15 exhibits excellent ammonia sensing performance with a sensitivity of 2.49 %/ppm and a low limit of detection (LOD) of 0.6 ppm. Cellulose-based composite fibers in this work demonstrate good gas sensing and anti-static properties as potential devices for smart e-textiles.

General design algorithm for stray light suppression of a panoramic annular system.

In this work, a universal algorithm for designing a panoramic annular lens (PAL) system free from stray light is proposed. The impact of a given stray light path to the optical system could be estimated without running a full stray light analysis process, which allows designers to eliminate troublesome stray light paths by optimizing lens parameters at an early stage of optical design. A 360° ×(40°-100°) PAL system is designed and implemented to verify the proposed method. Simulation shows that the point source transmittance (PST) decreases by 2 orders of magnitude at a specific field-of-view (FoV) range after optimizing the system. Experimental results show perfect consistency with the simulation predictions, which indicate that two types of stray light are totally eliminated in the demonstrated system. This stray light analysis and suppression method provides a promising approach for the research and development of ultra-wide angle high performance optical systems.

Chitosan Oligosaccharide Modified Bovine Serum Albumin Nanoparticles for Improving Oral Bioavailability of Naringenin.

INTRODUCTION: With the rapid development of nanotechnology, the research and development of nano-drugs have become one of the development directions of drug innovation. The encapsulation of the nanoparticles can change the biological distribution of the drug in vivo and improve the bioavailability of the drug in vivo. Naringenin is poorly soluble in water and has a low bioavailability, thus limiting its clinical application. The main purpose of this study was to develop a nano-sized preparation that could improve the oral bioavailability of naringenin. METHODS: Chitosan oligosaccharide modified naringenin-loaded bovine serum albumin nanoparticles (BSA-COS@Nar NPs) were prepared by emulsification solvent evaporation and electrostatic interaction. The nanoparticles were characterized by HPLC, laser particle size analyzer, transmission electron microscope and X-ray diffraction analysis. The release in vitro was investigated, and the behavior of nanoparticles in rats was also studied. The caco-2 cell model was established in vitro to investigate the cytotoxicity and cellular uptake of nanoparticles. RESULTS: BSA-COS@Nar NPs were successfully prepared, and the first-order release model was confirmed in vitro release. In vivo pharmacokinetic results indicated that the area under the drug concentration-time curve (AUC) of BSA-COS@Nar NPs was 2.37 times more than free naringenin. Cytotoxicity and cellular uptake results showed that BSA-COS@Nar NPs had no significant cytotoxic effect on Caco-2 cells and promoted cellular uptake of the drug. CONCLUSION: BSA-COS@Nar NPs could improve the in vivo bioavailability of naringenin.

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving.

Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

Research on water meter reading recognition based on deep learning.

At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.