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Cellulose nanofiber (CNF), a natural polymer material with a high specific surface area, is a potential candidate for gas sensors. Here, we report Au/SnO2 composites with excellent performance synthesized by a chemical precipitation method based on CNF-assisted synthesis, and a series of Au/SnO2 composites were prepared by adjusting the Au content. The sensitivity of the 1.5 wt.% Au/SnO2 sensor to a low concentration of 1 ppm NO2 was 53, nearly 17 times higher than that of pure SnO2 synthesized by CNF, under ultraviolet (UV) irradiation at a wavelength of 365 nm. The crystal structure of the composite was characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), revealing that the composites had high crystallinity and particles reaching the nanoscale. Further experimental data showed that the CNF-assisted synthesis Au/SnO2 sensors had good selectivity, repeatability, and stability. Additionally, the sensor is able to maintain its excellent gas sensing performance even under varying humidity conditions. [ABSTRACT FROM AUTHOR]

Cellulose is a common natural polymer, and the surface of the nanocellulose has a large number of hydroxyl groups, which is possible used to precipitate metal salts to obtain the precursors, and then calcine to get the corresponding metal oxides. In this paper, tin dioxide (SnO2) nanoparticles were prepared with different amounts of nanocellulose by a low-temperature hydrothermal method (60°C) followed by calcination. The obtained material was verified to be impurity-free SnO2 nanoparticles by various types of physical phase analysis and morphological characterization. The SnO2 nanoparticles obtained from 50 mg nanocellulose exhibited a significantly improved sensing performance, and the response toward SO2 at 1 ppm was 4.68. In addition, multiple performance tests were conducted on the obtained gas sensor, and it was finally found to have a good gas sensitivity and an excellent selectivity. The repeatability and stability also presented preferable results. Additionally, the sensing mechanism was discussed. The prepared SnO2 sensor is promising for the detection of SO2 due to its high sensitivity and selectivity. [ABSTRACT FROM AUTHOR]

ZnSnO3 microspheres was compounded by hydrothermal method with sodium alginate (SA) as a crystal growth modifier, and the Au nanoparticles were decorated on the surface of ZnSnO3 microspheres by simple solution method and heat treatment process. The structure and morphology of the prepared samples were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Under ultraviolet (UV) irradiation, the 0.2wt% Au/ZnSnO3@4g/L SA sensor could detect HCHO and reached a good response of 18.8 at 20 ppm, which was three times higher than the ZnSnO3 in the absence of Au. This sensor also performed an outstanding linearity within 5–20 ppm HCHO and satisfied repeatability. Moreover, the possible mechanism of the influence of Au on gas sensitivity is also proposed. [ABSTRACT FROM AUTHOR]

This work demonstrates that phenylalanine dipeptide-regulated silver can improve the gas sensing performance of a nitrogen dioxide (NO2) gas sensor. Herein, Ag/In2O3 was prepared by a hydrothermal method to detect NO2 gas, where varying amounts of phenylalanine dipeptide (FF) were introduced to regulate the growth of Ag NPs. The morphologies, microstructures, and compositions of the obtained samples were characterized in detail, and the results indicate that Ag NPs grow into nanorods with the regulation of FF. The gas sensing characteristics were systematically investigated with UV illumination (365 nm) at room temperature (25 °C). Upon exposure to NO2 gas, the optimal sensor exhibits a relatively high response value, good linearity, and satisfactory stability. In addition, the sensor can maintain good gas sensing performance under varying humidity. The reason underlying the observed enhancement in the gas sensing property is the formation of more active sites with the Ag nanorod structure. This study suggests that the introduction of polypeptides is a promising idea to improve the performance of gas sensors. [ABSTRACT FROM AUTHOR]

BackgroundIn recent years, the rapid development of mobile medical technology has provided multiple ways for the long-term management of chronic diseases, especially diabetes. As a new type of management model, smartphone apps are global, convenient, cheap, and interactive. Although apps were proved to be more effective at glycemic control, compared with traditional computer- and Web-based telemedicine technologies, how to gain a further and sustained improvement is still being explored. ObjectiveThe objective of this study was to investigate the effectiveness of an app-based interactive management model by a professional health care team on glycemic control in Chinese patients with poorly controlled diabetes. MethodsThis study was a 6-month long, single-center, prospective randomized controlled trial. A total of 276 type 1 or type 2 diabetes patients were enrolled and randomized to the control group (group A), app self-management group (group B), and app interactive management group (group C) in a 1:1:1 ratio. The primary outcome was the change in glycated hemoglobin (HbA1c) level. Missing data were handled by multiple imputation. ResultsAt months 3 and 6, all 3 groups showed significant decreases in HbA1c levels (all P.05). Multivariate line regression analyses also showed that the app interactive management group was associated with the larger reduction of HbA1c compared with groups A and B at both months 3 and 6 (all P>.05). In addition, the app interactive management group had better control of triglyceride and high-density lipoprotein cholesterol levels at both months 3 and 6 compared with baseline (both P

Accurate retinal fluid segmentation on Optical Coherence Tomography (OCT) images plays an important role in diagnosing and treating various eye diseases. The art deep models have shown promising performance on OCT image segmentation given pixel-wise annotated training data. However, the learned model will achieve poor performance on OCT images that are obtained from different devices (domains) due to the domain shift issue. This problem largely limits the real-world application of OCT image segmentation since the types of devices usually are different in each hospital. In this paper, we study the task of cross-domain OCT fluid segmentation, where we are given a labeled dataset of the source device (domain) and an unlabeled dataset of the target device (domain). The goal is to learn a model that can perform well on the target domain. To solve this problem, in this paper, we propose a novel Structure-guided Cross-Attention Network (SCAN), which leverages the retinal layer structure to facilitate domain alignment. Our SCAN is inspired by the fact that the retinal layer structure is robust to domains and can reflect regions that are important to fluid segmentation. In light of this, we build our SCAN in a multi-task manner by jointly learning the retinal structure prediction and fluid segmentation. To exploit the mutual benefit between layer structure and fluid segmentation, we further introduce a cross-attention module to measure the correlation between the layer-specific feature and the fluid-specific feature encouraging the model to concentrate on highly relative regions during domain alignment. Moreover, an adaptation difficulty map is evaluated based on the retinal structure predictions from different domains, which enforces the model focus on hard regions during structure-aware adversarial learning. Extensive experiments on the three domains of the RETOUCH dataset demonstrate the effectiveness of the proposed method and show that our approach produces state-of-the-art performance on cross-domain OCT fluid segmentation.