Tissue paper-based composite separator using nano-SiO2 hybrid crosslinked polymer electrolyte as coating layer for lithium ion battery with superior security and cycle stability
Source abstract
Abstract
Abstract With the development of energy-storage devices, separator is encountered by several challenges including adequate safety, higher current density and superior stability. Tissue paper, composed of packed cellulose fibers, possesses lower production cost, more easily accessibility, superior wettability and outstanding thermostability, thus being prospective as a substrate of high performance separator. To address structure collapse phenomenon occurred in conventional coating layer after long term electrolyte swelling, nano-SiO2 hybrid crosslinked network was constructed on tissue paper through chemical reactions between polymer poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and hyperbranched polyethyleneimine (PEI) in this work. The influences of crosslinking degree on physical properties and electrochemical performance were studied thoroughly. It can be found that when the crosslinking ratio of PVDF-HFP and PEI fixed at 10:1, the crosslinked composite separator displays excellent electrolyte uptake and wettability, superior ionic conductivity, better interfacial compatibility as well as higher Li+ transference number (0.56), thus offering battery with prominent rate capabilities. Besides, this crosslinked composite separator exhibits satisfying dimensional stability even treated at 250 oC, better flame retardancy, enhanced mechanical behavior, wider electrochemical window and outstanding cycle stability. Accordingly, tissue paper-based crosslinked composite separator can meet higher requirements put forward by high power lithium ion battery.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advancements in Battery Materials · Advanced Battery Materials and Technologies · Advanced Battery Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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