Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology. 相似文献
Journal of Materials Science - Hybrid oxidation methodologies (HOMs) and active site enrichment of 2D nanocatalyst through defects induction are ubiquitously used for generating adequate reactive... 相似文献
Metallurgical and Materials Transactions A - Hybrid nanocomposites have potential as wear-resistant materials. However, synthesizing these nanocomposites by conventional molten state methods result... 相似文献
The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar+ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 1016 ions/cm2 is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm2 and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm?2. 相似文献
Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.
Neural Computing and Applications - We propose “Deep Autoencoders for Feature Learning in Recommender Systems,” a novel discriminative model based on the incorporation of features from... 相似文献