4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

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.     

Reusable and Efficient Polystyrene Immobilized Ionic Liquid Catalyst for Batch and Flow Methylation of Hydroquinone

Catalysis Letters - An environmentally benign process for synthesizing 4-methoxyphenol through methylation of hydroquinone using polystyrene immobilized Bronsted acidic ionic liquid is presented....     

Photon and vibration synergism on planar defects induced 2D-graphitic carbon nitride for ultrafast remediation of dyes and antibiotic ampicillin

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...     

Fabrication of Wear-Resistant Ti3AlC2/Al3Ti Hybrid Aluminum Composites by Friction Stir Processing

Metallurgical and Materials Transactions A - Hybrid nanocomposites have potential as wear-resistant materials. However, synthesizing these nanocomposites by conventional molten state methods result...     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

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.

     

Beneficial effects of wettability altering surfactants in oil-wet fractured reservoirs

In fractured reservoirs, an effective matrix-fracture mass transfer is required for oil recovery. Surfactants have long been considered for oil recovery enhancement, mainly in terms of their ability to reduce oil–water interfacial tension. These surfactants are effective when the fractured formations are water-wet, where capillary imbibition of surfactants from the fracture into the matrix contributes to oil recovery. However, another beneficial aspect of surfactants, namely their ability to alter wettability, remains to be explored and exploited. Surfactants capable of altering wettability can be especially beneficial in oil-wet fractured formations, where the surfactant in the fracture diffuses into the matrix and alters the wettability, enabling imbibition of even more surfactant into the matrix. This sequential process of initial diffusion followed by imbibition continues well into the matrix yielding significant enhancements in oil recovery.In order to test this hypothesis of sequential diffusion–imbibition phenomenon, Dual-Drop Dual-Crystal (DDDC) contact angle experiments have been conducted using fractured Yates dolomite reservoir fluids, two types of surfactants (nonionic and anionic) and dolomite rock substrates. A new experimental procedure was developed in which crude oil equilibrated with reservoir brine has been exposed to surfactant to simulate the matrix-fracture interactions in fractured reservoirs. This procedure enables the measurements of dynamic contact angles and oil–water interfacial tensions, in addition to providing the visual observations of the dynamic behavior of crude oil trapped in the rock matrix as it encounters the diffusing surfactant from the fractures. Both the measurements and visual observations indicate wettability alterations of the matrix surface from oil-wet to less oil-wet or intermediate wet by the surfactants. Thus this study is of practical importance to oil-wet fractured formations where surfactant-induced wettability alterations can result in significant oil recovery enhancements. In addition, this study has also identified the need to include contact angle term in the dimensionless Bond number formulations for better quantitative interpretation of rock–fluids interactions.