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Designing artificial ion channels with strict K + /Na + selectivity toward next-generation electric-eel-mimetic ionic power generation.

Jipeng LiLinhan DuXian KongJianzhong WuDiannan LuLei JiangWei Guo
Published in: National science review (2023)
A biological potassium channel is >1000 times more permeable to K + than to Na + and exhibits a giant permeation rate of ∼10 8 ions/s. It is a great challenge to construct artificial potassium channels with such high selectivity and ion conduction rate. Herein, we unveil a long-overlooked structural feature that underpins the ultra-high K + /Na + selectivity. By carrying out massive molecular dynamics simulation for ion transport through carbonyl-oxygen-modified bi-layer graphene nanopores, we find that the twisted carbonyl rings enable strict potassium selectivity with a dynamic K + /Na + selectivity ratio of 1295 and a K + conduction rate of 3.5 × 10 7 ions/s, approaching those of the biological counterparts. Intriguingly, atomic trajectories of K + permeation events suggest a dual-ion transport mode, i.e. two like-charged potassium ions are successively captured by the nanopores in the graphene bi-layer and are interconnected by sharing one or two interlayer water molecules. The dual-ion behavior allows rapid release of the exiting potassium ion via a soft knock-on mechanism, which has previously been found only in biological ion channels. As a proof-of-concept utilization of this discovery, we propose a novel way for ionic power generation by mixing KCl and NaCl solutions through the bi-layer graphene nanopores, termed potassium-permselectivity enabled osmotic power generation (PoPee-OPG). Theoretically, the biomimetic device achieves a very high power density of >1000 W/m 2 with graphene sheets of <1% porosity. This study provides a blueprint for artificial potassium channels and thus paves the way toward next-generation electric-eel-mimetic ionic power generation.
Keyphrases
  • solid state
  • molecular dynamics simulations
  • quantum dots
  • single molecule
  • ionic liquid
  • carbon nanotubes
  • structural basis
  • mass spectrometry
  • deep learning
  • single cell
  • rare case
  • sensitive detection