Abstract Biopolymers, a class of fascinating polymers from biomass provide sustainability, biodegradability, availability, biocompatibility, and unique properties. A ubiquitous feature of biopolymers is their hierarchical structure, with the presence of well‐organized structures from the nanoscale to macroscopic dimensions. This structural organization endows biopolymers with toughness, defect resistance, and bucking adaptability. To retain these inherent structural features, nano‐structural assemblies isolated from biomass have been applied as building blocks to construct new biopolymer‐based materials. This top‐down processing strategy is distinct from the more traditional molecular‐level bottom‐up design and assembly approach for new materials. In this review, the hierarchical structures of several representative biopolymers (cellulose, chitin, silk, collagen) are introduced with a focus on these nanoscale building blocks, as well as highlighting the similarities and differences in the respective chemistries and structures. Recent progress in production strategies of these natural building blocks are summarized, covering methods and treatments used for isolations. Finally, approaches and emerging applications of biopolymer‐based materials using these natural nano‐ and meso‐scale building blocks are demonstrated in areas of biomedicine, electronics, environmental, packaging, sensing, foods, and cosmetics.
'Yusuli' is breeded with female parent 'Dangshan suli'× male parent 'Zhuzuili'.It has length oval fruit,yellow-white peel and light ware surface.The average fruit weights 348 g.Its flesh is white,succulent and tasted sweet.The soluble solids content of fruit is 11%-13%.Furthermore,It is extremely resistant to storage.It ripens during late September in Jinzhong,Shanxi Province.
Exploring the crop production water footprint and their driving factors is of significant importance for management of agricultural water resources. However, how do we effectively assess the total agricultural water consumption and explore the significance of their driving factors, i.e., population, economy, and agricultural production conditions, using a backpropagation neural network (BPNN)? It is still ambiguous. Water consumption for crops during the growing season is explicitly explored by way of water footprint indicators (green water footprint, WFPg, and blue water footprint, WFPb). This study provides new insights into the factors driving the changes in crop production water footprint in Taiyuan City over the period of 2005–2021. Simulations of crop evapotranspiration using the CROPWAT model were quantified. The results showed that Taiyuan City has a low crop yield level below the average level of China, with the highest crop yield in maize. The crop production water footprint in Taiyuan City showed a non-linearly decreasing trend over time. The average annual crop production water footprint was 187.09 × 103 m3/kg in Taiyuan City, with the blue water footprint and green water footprint accounting for 63.32% and 36.68%, respectively. The crop production water footprint in the west and north of Taiyuan City was significantly higher than those in other areas, accounting for 42.92% of the total crop production water footprint. Oilseed crops contributed most to the total crop production water footprint, accounting for 47.11%. The GDP and total sown area of crops were more important for the changes in WFPb. Agricultural machinery power and agriculture-to-non-agriculture ratio were more important for the changes in WFPg. Agricultural machinery power and GDP were more important for the changes in IWFP. In-depth analysis of the factors driving the changes in crop production water footprint is dramatically important for agricultural decision makers to mitigate water resource pressure in Taiyuan City.
A simple sensor for highly sensitive determination of NHDC based on a SWNTs-modified glassy carbon electrode was established. Compared with a bare GCE, the proposed electrode significantly improved the response of NHDC and finally it was applied in beverage analysis.
Electrical safety is always an important aspect of medical equipment in hospitals. With the development of clinical engineering, electrical safety testing has become a routine procedure for the clinical engineering units in hospitals. Among the various safety standard parameters for medical equipment, the leakage current is most important. This study measured different leakage currents. An intelligent digital tester was applied to test the safety quality of medical instruments automatically. This tester was based on a chip-computer (the Intel 8031). This tester was designed to be able to be set up either in normal mode or in single-failure mode for automatic testing by electric relays. All the results, which are free from manual errors, are displayed by means of an LCD unit and are printed by a micro-printer. The highly accurate output is also an advantage of this instrument, which is based on a precise signal-detecting electrical circuit and an adaptive filter. This tester can be easily used in clinical units following the Chinese standard GB9701 as well as the IEC standard 601.
To address the issues of inadequate fault diagnosis accuracy and suboptimal generalisation performance of rolling bearings in the presence of significant noise and varying operational conditions, a fault diagnosis approach utilising a dual-stream interactive convolutional neural network (DSICNN) is presented. To fully leverage the fault characteristics in vibration signals, both time-domain and frequency-domain signals are concurrently employed as inputs to the neural network. Then, dual attention mechanisms are presented, among which the Dynamic Weighted Channel Attention Mechanism (DWCAM) dynamically calibrates the channel weights of different inputs based on the importance of different channels, and the Aggregated Spatial Self Attention Mechanism (ASSAM) assigns greater weights to important regions while enhancing feature expression ability. Meanwhile, an arctangent linear unit function (AT-LU) is constructed to improve the problem of information loss contained in the signal when the linear rectifier unit has negative input. Finally, the signals are input into DSICNN, and the features are extracted more fully by interacting with two information streams. The model is trained and evaluated against other fault diagnosis models. The experimental results indicate that the suggested method exhibits superior classification performance, generalisation capability and robustness in the presence of significant noise and varying operating conditions.
Wood and lignocellulosic-based material components are explored in this review as functional additives and reinforcements in composites for extrusion-based additive manufacturing (AM) or 3D printing. The motivation for using these sustainable alternatives in 3D printing includes enhancing material properties of the resulting printed parts, while providing a green alternative to carbon or glass filled polymer matrices, all at reduced material costs. Previous review articles on this topic have focused only on introducing the use of natural fillers with material extrusion AM and discussion of their subsequent material properties. This review not only discusses the present state of materials extrusion AM using natural filler-based composites but will also fill in the knowledge gap regarding state-of-the-art applications of these materials. Emphasis will also be placed on addressing the challenges associated with 3D printing using these materials, including use with large-scale manufacturing, while providing insight to overcome these issues in the future.
This article introduced the hardware electrical circuit of the USB test platform,which is based on the CH372 interface chip and takes the STC89LE58AD as the core.The article also analyzed both the communication protocol between this platform and the PC machine and the programming.
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