ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Accelerated Selective Li+ Transports Assisted by Microcrack-Free Anionic Network Polymer Membranes for Long Cyclable Lithium Metal Batteries.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Accelerated Selective Li+ Transports Assisted by Microcrack-Free Anionic Network Polymer Membranes for Long Cyclable Lithium Metal Batteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Rechargeable Li metal batteries have the potential to meet the demands of high-energy density batteries for electric vehicles and grid-energy storage system applications. Achieving this goal, however, requires resolving not only safety concerns and a shortened battery cycle life arising from a combination of undesirable lithium dendrite and solid-electrolyte interphase formations. Here, a series of microcrack-free anionic network polymer membranes formed by a facile one-step click reaction are reported, displaying a high cation conductivity of 3.1 × 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.