The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Thermal Processing of Silicones for Green, Scalable, and Healable Superhydrophobic Coatings
Abstract
The thermal degradation of silicones is exploited and engineered to make super-hydrophobic coatings that are scalable, healable, and ecofriendly for various outdoor applications. The coatings can be generated and regenerated at the rate of 1 m(2) min(-1) using premixed flames, adhere to a variety of substrates, and tolerate foot traffic (>1000 steps) after moderate wear and healing.
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
Solvent Compatibility of Poly(dimethylsiloxane)-Based Microfluidic Devices
Lee, J. N., Park, C., & Whitesides, G. M. (2003) · Analytical Chemistry · doi:10.1021/ac0346712 · Cited by 2,643
Cited by
Robust superhydrophobicity: mechanisms and strategies
Zhang, W., Wang, D., Sun, Z., Song, J., & Deng, X. (2021) · Chemical Society Reviews · doi:10.1039/d0cs00751j · Cited by 686
Bioinspired Designs of Superhydrophobic and Superhydrophilic Materials
Si, Y., Dong, Z., & Jiang, L. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00504 · Cited by 507
Superhydrophobic Coatings from Ecofriendly Materials and Processes: A Review
Bayer, I. S. (2020) · Advanced Materials Interfaces · doi:10.1002/admi.202000095 · Cited by 346
Guo, K.-Y., Wu, Q., Mao, M., Chen, H., Zhang, G.-D., Zhao, L., Gao, J.-F., Song, P., & Tang, L.-C. (2020) · Composites Part B: Engineering · doi:10.1016/j.compositesb.2020.108017 · Cited by 281
Chen, F.-F., Zhu, Y.-J., Xiong, Z.-C., Dong, L.-Y., Chen, F., Lu, B.-Q., & Yang, R.-L. (2017) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b09484 · Cited by 73
Metadata
- Authors
- Tian, X., Shaw, S., Lind, K. R., & Cademartiri, L.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2016-03-23
- Volume
- 28
- Issue
- 19
- Pages
- 3677-3682
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2310529669
- Cited by
- 193
- References
- 1
- FWCI
- 10.83
- 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: Thermal Processing of Silicones for Green, Scalable, and Healable Superhydrophobic Coatings. https://app.cochat.ai/papers/10.1002/adma.201506446
- The paper
- Tian, X., Shaw, S., Lind, K. R., & Cademartiri, L. (2016). Thermal Processing of Silicones for Green, Scalable, and Healable Superhydrophobic Coatings. Advanced Materials. https://doi.org/10.1002/adma.201506446
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.