Spontaneous Graphene/Lipid Bicellar Co-assembly (NANO <sup>2</sup> -Graphene): Experiments and Computer Simulations

Using a combined experimental and computer modeling approach, we demonstrate that graphene can self-assemble with a discoidal lipid bicelle, forming a graphene incorporated nanodisc structure (NANO<sup>2</sup>-graphene) over a wide range of graphene-to-lipid ratios (from 1:2000 to 1:40 by weight). Structural characterization via dynamic light scattering (DLS), differential scanning calorimetry (DSC), and small-angle X-ray scattering (SAXS) confirms the encapsulation of graphene in the bicelles and suggests that the presence of graphene enhances the stability of the discoidal structure and prevents the usual thermally induced bicelle-to-vesicle transition. Both molecular dynamics (MD) simulations and DSC results show that bicelles are capable of encapsulating a significant graphene content and that the graphene incorporation significantly suppresses the lipid ordered phase and decreases lipid lateral diffusion. MD simulations also reveal that, in the presence of single or stacked graphene layers, lipid tails bend and orient along the graphene surface to maintain the bilayer thickness with the lipid order parameter significantly reduced and the area per lipid increased along with water contacts with lipid headgroups. These results provide important insights into the interaction between lipid molecules and graphene that should be considered in the biomedical application of graphene-containing nanomaterials and, more broadly, the effect of graphene on lipid-containing biointerfaces.

Authors

Institutions

Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-11-25
DOI
https://doi.org/10.1021/acsami.5c11189
Primary Topic
Graphene and Nanomaterials Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spontaneous Graphene/Lipid Bicellar Co-assembly (NANO 2 -Graphene): Experiments and Computer Simulations

Mu‐Ping Nieh, Donyeil Hoy, Chung‐Hao Liu, Elena E. Dormidontova et al.
ACS Applied Materials & Interfaces
Graphene and Nanomaterials Applications
article

Spontaneous Graphene/Lipid Bicellar Co-assembly (NANO 2 -Graphene): Experiments and Computer Simulations

Mu‐Ping Nieh, Donyeil Hoy, Chung‐Hao Liu, Elena E. Dormidontova, Yiyan Kuang, Alemayehu Asres
article en

Abstract

Using a combined experimental and computer modeling approach, we demonstrate that graphene can self-assemble with a discoidal lipid bicelle, forming a graphene incorporated nanodisc structure (NANO<sup>2</sup>-graphene) over a wide range of graphene-to-lipid ratios (from 1:2000 to 1:40 by weight). Structural characterization via dynamic light scattering (DLS), differential scanning calorimetry (DSC), and small-angle X-ray scattering (SAXS) confirms the encapsulation of graphene in the bicelles and suggests that the presence of graphene enhances the stability of the discoidal structure and prevents the usual thermally induced bicelle-to-vesicle transition. Both molecular dynamics (MD) simulations and DSC results show that bicelles are capable of encapsulating a significant graphene content and that the graphene incorporation significantly suppresses the lipid ordered phase and decreases lipid lateral diffusion. MD simulations also reveal that, in the presence of single or stacked graphene layers, lipid tails bend and orient along the graphene surface to maintain the bilayer thickness with the lipid order parameter significantly reduced and the area per lipid increased along with water contacts with lipid headgroups. These results provide important insights into the interaction between lipid molecules and graphene that should be considered in the biomedical application of graphene-containing nanomaterials and, more broadly, the effect of graphene on lipid-containing biointerfaces.

ACS Applied Materials & InterfacesVol. 17(49)
University of Connecticut (US)
Openalex Percentile: Top 65%
Graphene and Nanomaterials Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.