The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A superhydrophobic/electrothermal synergistically anti-icing strategy based on graphene composite
Abstract
Abstract Surface icing tends to cause serious problems such as flash over and the following blackout accident. Although electrothermal system is the most widely used method, how to solve the re-freeze problem as the melted ice tend to stay on the surface is still a challenge. Here, we introduced a superhydrophobic/electrothermal synergistically anti-icing strategy based on graphene composite. The superhydrophobicity together with the high electrothermal efficiency let the graphene surface dry and clean in the simulated “glaze ice” condition. Although ice accretion would be formed on the surface when the DC voltage was off, the ice could be rapidly removed within 70 s after applying the voltage of 50 V. To enhance the durability of superhydrophobic surface, the hierarchical structure was constructed by the tri-scale nature of inorganic fillers (graphene, carbon nanotubes and silica nanoparticles). Then the hierarchical structure was partially embedded into the substrate by a dissolution and resolidification method. The coupling effect of partially-embedded structure and hierarchical structure led to the superior robustness, which could withstand sandpaper abrasion (500 g load, 8.00 m), the attack of various corrosive liquids, and low/high temperature treatment without losing superhydrophobicity. More remarkably, this graphene superhydrophobic composite retained deicing property even after 30 icing/deicing cycles.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Graphene: Status and Prospects
Geim, A. K. (2009) · Science · doi:10.1126/science.1158877 · Cited by 13,942
Thermal Conductance of an Individual Single-Wall Carbon Nanotube above Room Temperature
Pop, E., Mann, D., Wang, Q., Goodson, K., & Dai, H. (2006) · Nano Letters · doi:10.1021/nl052145f · Cited by 1,852
Robust self-cleaning surfaces that function when exposed to either air or oil
Lu, Y., Sathasivam, S., Song, J., Crick, C. R., Carmalt, C. J., & Parkin, I. P. (2015) · Science · doi:10.1126/science.aaa0946 · Cited by 1,808
Mechanically Durable Superhydrophobic Surfaces
Verho, T., Bower, C., Andrew, P., Franssila, S., Ikkala, O., & Ras, R. H. A. (2011) · Advanced Materials · doi:10.1002/adma.201003129 · Cited by 1,110
Moving superhydrophobic surfaces toward real-world applications
Tian, X., Verho, T., & Ras, R. H. A. (2016) · Science · doi:10.1126/science.aaf2073 · Cited by 824
Graphene-based electronic sensors
He, Q., Wu, S., Yin, Z., & Zhang, H. (2012) · Chemical Science · doi:10.1039/c2sc20205k · Cited by 745
Superhydrophobic Surfaces: Are They Really Ice-Repellent?
Kulinich, S. A., Farhadi, S., Nose, K., & Du, X. W. (2011) · Langmuir · doi:10.1021/la104277q · Cited by 737
Robust Anti‐Icing Performance of a Flexible Superhydrophobic Surface
Wang, L., Gong, Q., Zhan, S., et al. (2016) · Advanced Materials · doi:10.1002/adma.201602480 · Cited by 635
Du, J., Zhao, L., Zeng, Y., Zhang, L., Li, F., Liu, P., & Liu, C. (2011) · Carbon · doi:10.1016/j.carbon.2010.11.013 · Cited by 428
Superhydrophobic SiC/CNTs Coatings with Photothermal Deicing and Passive Anti-Icing Properties
Jiang, G., Chen, L., Zhang, S., & Huang, H. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b11201 · Cited by 345
Cited by
Tian, Y., Xu, Y., Zhu, Z., Liu, Y., Xie, J., Zhang, B., Zhang, H., & Zhang, Q. (2022) · Colloids and Surfaces A: Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2022.129586 · Cited by 56
Metadata
- Authors
- Wang, P., Yao, T., Li, Z., Wei, W., Xie, Q., Duan, W., & Han, H.
- Venue
- Composites Science and Technology
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 198
- Pages
- 108307
- ISSN
- 0266-3538 (ISSN-L)
- OpenAlex
- W3036892668
- Cited by
- 281
- References
- 30
- FWCI
- 31.53
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A superhydrophobic/electrothermal synergistically anti-icing strategy based on graphene composite. https://app.cochat.ai/papers/10.1016/j.compscitech.2020.108307
- The paper
- Wang, P., Yao, T., Li, Z., Wei, W., Xie, Q., Duan, W., & Han, H. (2020). A superhydrophobic/electrothermal synergistically anti-icing strategy based on graphene composite. Composites Science and Technology. https://doi.org/10.1016/j.compscitech.2020.108307
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.