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Structural Effect on Proton Conduction in Two Highly Stable Disubstituted Ferrocenyl Carboxylate Frameworks.

Yin QinXinyue WangWenping XieZifeng LiGang Li
Published in: Inorganic chemistry (2020)
It is still a great challenge for people to obtain high proton conductive solid crystalline materials and accurately grasp their proton conduction mechanism. Herein, two highly stable disubstituted ferroceneyl carboxylate frameworks (DFCFs), {[HOOC(CH2)2OC]Fcc[CO(CH2)2COOH]} (DFCF 1) (Fcc = (η5-C5H4)Fe(η5-C5H4)) and [(HOOC)Fcc(COOH)] (DFCF 2) supported by intramolecular or intermolecular hydrogen bonds and π-π interactions were constructed and characterized by single crystal X-ray diffraction. Consequently, their water-assisted proton migration was researched systematically. As expected, 1 exhibited ultrahigh proton conductivity of 1.14 × 10-2 S·cm-1 at 373 K and 98% RH due to the presence of high-density free -COOH units. Unexpectedly, 2 displayed a low proton conductivity of 1.99 × 10-5 S·cm-1. On the basis of the analysis of crystal data, we believe that different arrangements of carboxyl groups lead to the different proton conductivity. Even more surprisingly, the proton conductivities of the two DFCFs are lower than those of their relevant monosubstituted ferroceneyl carboxylate frameworks (MFCFs), [FcCO(CH2)2COOH] (MFCF A) (Fc = (η5-C5H5)Fe(η5-C5H4)) (1.17 × 10-1 S·cm-1) and [FcCOOH] (MFCF B) (1.01 × 10-2 S·cm-1) under same conditions that were previously reported by us. This phenomenon indicates that the presence of a high number of free carboxyl groups in the framework does not necessarily cause high proton conductivity. We found that the arrangement of free carboxyl groups in the ferrocenyl framework plays a decisive role in proton conduction. This new discovery will provide guidance for the design of high proton conductive materials with free -COOH units.
Keyphrases
  • electron transfer
  • high density
  • small molecule
  • room temperature
  • mass spectrometry
  • high resolution
  • magnetic resonance
  • machine learning
  • high throughput
  • data analysis