Self-healing organic coatings based on microcapsules – A patent-based review

2021 ◽  
Vol 04 ◽  
Author(s):  
Diego Moreira Schlemper ◽  
Sérgio Henrique Pezzin

: Self-healing coatings are intended to increase long-term durability and reliability and can be enabled by the presence of microcapsules containing a self-healing agent capable of interacting with the matrix and regenerating the system. This review article provides an overview of the state-of-the-art, focusing on the patents published in the field of microcapsule-based self-healing organic coatings, since the early 2000’s. A discussion about coatings for corrosion protection and the different self-healing approaches and mechanisms are also addressed, as well as future challenges and expectations for this kind of coatings.

Bone ◽  
2018 ◽  
Vol 106 ◽  
pp. 28-29 ◽  
Author(s):  
A.E. Litwic ◽  
C. Parsons ◽  
M.H. Edwards ◽  
D. Jagannath ◽  
C. Cooper ◽  
...  

2021 ◽  
pp. 105678952110112
Author(s):  
Kaihang Han ◽  
Jiann-Wen Woody Ju ◽  
Yinghui Zhu ◽  
Hao Zhang ◽  
Tien-Shu Chang ◽  
...  

The cementitious composites with microencapsulated healing agents have become a class of hotspots in the field of construction materials, and they have very broad application prospects and research values. The in-depth study on multi-scale mechanical behaviors of microencapsulated self-healing cementitious composites is critical to quantitatively account for the mechanical response during the damage-healing process. This paper proposes a three-dimensional evolutionary micromechanical model to quantitatively explain the self-healing effects of microencapsulated healing agents on the damage induced by microcracks. By virtue of the proposed 3 D micromechanical model, the evolutionary domains of microcrack growth (DMG) and corresponding compliances of the initial, extended and repaired phases are obtained. Moreover, the elaborate studies are conducted to inspect the effects of various system parameters involving the healing efficiency, fracture toughness and preloading-induced damage degrees on the compliances and stress-strain relations. The results indicate that relatively significant healing efficiency, preloading-induced damage degree and the fracture toughness of polymerized healing agent with the matrix will lead to a higher compressive strength and stiffness. However, the specimen will break owing to the nucleated microcracks rather than the repaired kinked microcracks. Further, excessive higher values of healing efficiency, preloading-induced damage degree and the fracture toughness of polymerized healing agent with the matrix will not affect the compressive strength of the cementitious composites. Therefore, a stronger matrix is required. To achieve the desired healing effects, the specific parameters of both the matrix and microcapsules should be selected prudently.


2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.


2020 ◽  
Vol 34 (06) ◽  
pp. 10352-10360
Author(s):  
Jing Bi ◽  
Vikas Dhiman ◽  
Tianyou Xiao ◽  
Chenliang Xu

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.


2021 ◽  
Vol 4 ◽  
Author(s):  
Tiina Laamanen ◽  
Veera Norros ◽  
Sanna Suikkanen ◽  
Mikko Tolkkinen ◽  
Kristiina Vuorio ◽  
...  

Environmental DNA (eDNA) and other molecular based approaches are revolutionizing the field of biomonitoring. These approaches undergo rapid modifications, and it is crucial to develop the best practices by sharing the newest information and knowledge. In our ongoing project we: assess the state-of-the-art of eDNA methods at Finnish Environment Institute SYKE; identify concrete next steps towards the long-term aim of implementing eDNA methods into environmental and biomonitoring; promote information exchange on eDNA methods and advance future research efforts both within SYKE and with our national and international partners. assess the state-of-the-art of eDNA methods at Finnish Environment Institute SYKE; identify concrete next steps towards the long-term aim of implementing eDNA methods into environmental and biomonitoring; promote information exchange on eDNA methods and advance future research efforts both within SYKE and with our national and international partners. Scientific background Well-functioning and intact natural ecosystems are essential for human well-being, provide a variety of ecosystem services and contain a high diversity of organisms. However, human activities such as eutrophication, pollution, land-use or invasive species, are threatening the state and functioning of ecosystems from local to global scale (e.g. Benateau et al. 2019; Reid et al. 2018; Vörösmarty et al. 2010). New molecular techniques in the field and in the laboratory have enabled sampling and identification of much of terrestrial, marine and freshwater biodiversity. These include environmental DNA (eDNA, e.g. Valentini et al. 2016) and bulk-sample DNA metabarcoding approaches (e.g. Elbrecht et al. 2017) and targeted RNA-based methods (e.g. Mäki and Tiirola 2018). The eDNA technique uses DNA that is released from organisms into their environment, from which a signal of organisms’ presence in the system can be obtained. For example, in aquatic ecosystems, eDNA is typically extracted from sediment or filtered water samples (e.g. Deiner et al. 2016), and this approach is distinguished from bulk DNA metabarcoding, where organisms are directly identified from e.g. complete biological monitoring samples (e.g. Elbrecht et al. 2017). Despite the demonstrated potential of environmental and bulk-sample DNA metabarcoding approaches in recent years, there are still significant bottlenecks to their routine use that need to be addressed (e.g. Pawlowski et al. 2020). Methods and implementati on The project is divided into three work packages: WP1 Gathering existing knowledge, identifying knowledge gaps and proposing best practices, WP2 Roadmap to implementation and WP3 eDNA monitoring pilot. Please see more details in the Fig. 1


The Analyst ◽  
2018 ◽  
Vol 143 (8) ◽  
pp. 1735-1757 ◽  
Author(s):  
Matthew J. Baker ◽  
Hugh J. Byrne ◽  
John Chalmers ◽  
Peter Gardner ◽  
Royston Goodacre ◽  
...  

This review examines the state-of-the-art of clinical applications of infrared absorption and Raman spectroscopy, outstanding challenges, and progress towards translation.


2019 ◽  
Vol 810 ◽  
pp. 119-124
Author(s):  
Wataru Nakao ◽  
Taira Hayakawa ◽  
Tesuro Yanaseko ◽  
Shingo Ozaki

The availability of TiC healing agent has been evaluated in low temperature self-healing behavior of Al2O3 based self-healing ceramics. For this purpose, some technical issues to actualize the advanced fiber-reinforced self-healing ceramics containing TiC based interlayer as healing agent were discussed. Especially, the mechanical matching between the matrix and the interlayer was focused. Moreover, the self-healing behavior of the advanced shFRC containing the optimized TiC based healing agent was investigated. As a result, 30 vol% TiC-70 vol% Al2O3 interlayer was confirmed to be the optimized healing agent in the self-healing ceramics, and the self-healing ceramics was found to enable to attain the perfect healing at 600°C within 10 min. And we succeeded in prototype production of fiber-reinforced self-healing ceramics for low pressure turbine blade.


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