Raw Data Library
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
Green Science
​
​
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User GuideGreen Science

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Large-Scale Self-Assembly of anisotropic graphene oxide films via blade Coating: Sustainable design and Stimuli-Responsive performance for biomimicry

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
English
2023

Large-Scale Self-Assembly of anisotropic graphene oxide films via blade Coating: Sustainable design and Stimuli-Responsive performance for biomimicry

0 Datasets

0 Files

English
2023
Materials & Design
Vol 233
DOI: 10.1016/j.matdes.2023.112205

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Konstantin ‘kostya’  Novoselov
Konstantin ‘kostya’ Novoselov

The University of Manchester

Verified
Musen Chen
Qian Wang
Maxim M. Trubyanov
+6 more

Abstract

Sustainable structural design, utilizing material to imitate natural biological systems, presents both promise and challenges. By avoiding interfacial problems encountered in composite counterparts, such designs offer self-adaptive materials for smart housing and green architecture, etc. In this study, we demonstrate the feasibility of large-scale self-assembly of graphene oxide (GO) flakes into anisotropic films through a simple blade coating technique. Through the application of blade coating to a highly concentrated nematic GO suspension, we successfully fabricate GO films with morphological gradient and patterning. Additionally, we propose a statistical analysis method utilizing scanning electron microscopy (SEM) images for the characterization of materials with macroscopic surface morphology. Furthermore, we explore the application of these GO films as low-dimensional soft actuators, revealing their outstanding stimuli-responsive performance and self-adaptation to environment. Such robust and flexible films can be used as integral building elements in the bioinspired design of sustainable smart housing facilitating remote robotization and sensing capabilities.

How to cite this publication

Musen Chen, Qian Wang, Maxim M. Trubyanov, Kou Yang, Aleksandr S. Aglikov, Qi Ge, Ekaterina V. Skorb, Konstantin ‘kostya’ Novoselov, Daria V. Andreeva (2023). Large-Scale Self-Assembly of anisotropic graphene oxide films via blade Coating: Sustainable design and Stimuli-Responsive performance for biomimicry. Materials & Design, 233, pp. 112205-112205, DOI: 10.1016/j.matdes.2023.112205.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2023

Authors

9

Datasets

0

Total Files

0

Language

English

Journal

Materials & Design

DOI

10.1016/j.matdes.2023.112205

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access