PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 30, 2026Nature Communications4 citationsOpen Access

π-π Stacking origin of irreversible dispersibility of graphene oxide

YGYue GaoYWYa WangYLYangchao Liao

Key Points

  • To investigate the role of π-π stacking in the irreversible dispersibility of graphene oxide.
  • Conduct experiments and simulations to analyze the interaction of functional groups in GO.
  • Examine the effects of interlayer water exclusion on π-π stacking.
  • Design selective gelation paths for graphene-based hydrogels.
  • Identified that irreversible dispersibility in GO is linked to interlayer π-π stacking.
  • Exclusion of interlayer water enhances π-π stacking interactions.
  • Proposed a mechanism that informs new methods for producing graphene-based materials.

Abstract

Graphene oxide (GO) has become an increasingly important industrial chemical and useful two-dimensional material. The complexity in both functional groups and heterostructure of GO offers its rich chemistry yet complicates its stability, dispersibility and processing property. Interactions with functional groups have been pioneered to explain these confusing properties of GO. However, the critical role of structural heterogeneity keeps elusive. Here, we report that the irreversible dispersibility of GO solid origins from the interlayer π-π stacking and the accessibility between conjugated domains. Experiments and simulations reveal that the exclusion of interlayer water leads to irreversible π-π stacking. This insight into the π-π stacking mechanism informs the design of selective gelation paths, enabling the scalable, continuous production of highly conductive graphene-based hydrogel for neural probes. Our work unveils a general mechanism for confusing dispersibility of GO and opens supramolecular interactions modulating methods for assembled structures and materials.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/69ca12d4883daed6ee0951e3https://doi.org/10.1038/s41467-026-71003-z
Ask AI
Helpful
Bookmark
Share
View Full Paper