A Short Review on Polyethylene-based Ionomers: Synthesis, Structure, and Applications
Chem & bio engineering（2024）
Abstract
Polyethylene-based ionomers (PE ionomers) are polymers featuring polyethylene as the main chain structure with a small fraction of ionic functional groups pendant to the polyethylene backbone. Due to this combination of nonpolar covalent skeletons and polar ionic groups, PE ionomers can exhibit various properties, depending on their specific composition and structure, such as clarity, adhesivity, abrasiveness, enhanced mechanical strength, shape memory, and healable capabilities. These extraordinary properties have led to the broad applications of PE ionomers in the past decades for cosmetics packaging, coatings, blends, ion-exchange membranes, high voltage insulation materials, and adhesives and even hold great potential in the emerging fields of shape memory and healable smart materials. This review provides an in-depth overview of the latest progress in the field of PE ionomers, with a particular focus on diverse synthetic methods and structural models, as well as important related applications. The structure-property relationship is also discussed interstitially, providing ideas for the subsequent development of PE ionomers with novel structures and fresh applications.
MoreTranslated text
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined