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Autogenous Production and Stabilization of Highly Loaded Sub-Nanometric Particles within Multishell Hollow Metal-Organic Frameworks and Their Utilization for High Performance in Li-O2 Batteries.

Won Ho ChoiByeong Cheul MoonDong Gyu ParkJae Won ChoiKeon-Han KimJae-Sun ShinMin Gyu KimKyung Min ChoiJeung Ku Kang
Published in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2020)
Sub-nanometric particles (SNPs) of atomic cluster sizes have shown great promise in many fields such as full atom-to-atom utilization, but their precise production and stabilization at high mass loadings remain a great challenge. As a solution to overcome this challenge, a strategy allowing synthesis and preservation of SNPs at high mass loadings within multishell hollow metal-organic frameworks (MOFs) is demonstrated. First, alternating water-decomposable and water-stable MOFs are stacked in succession to build multilayer MOFs. Next, using controlled hydrogen bonding affinity, isolated water molecules are selectively sieved through the hydrophobic nanocages of water-stable MOFs and transferred one by one to water-decomposable MOFs. The transmission of water molecules via controlled hydrogen bonding affinity through the water-stable MOF layers is a key step to realize SNPs from various types of alternating water-decomposable and water-stable layers. This process transforms multilayer MOFs into SNP-embedded multishell hollow MOFs. Additionally, the multishell stabilizes SNPs by π-backbonding allowing high conductivity to be achieved via the hopping mechanism, and hollow interspaces minimize transport resistance. These features, as demonstrated using SNP-embedded multishell hollow MOFs with up to five shells, lead to high electrochemical performances including high volumetric capacities and low overpotentials in Li-O2 batteries.
Keyphrases
  • metal organic framework
  • genome wide
  • gold nanoparticles
  • drug delivery
  • dna methylation
  • gene expression
  • microbial community
  • machine learning
  • molecular dynamics
  • cancer therapy
  • high resolution
  • deep learning