Machine vision-enabled surface temperature mapping based on thermo-responsive cholesteric liquid crystal elastomer arrays
Combining thermo-responsive cholesteric liquid crystal elastomers (CLCE) with machine vision enables precise temperature mapping. Two machine learning models detect temperature values and distributions for robust material-based monitoring.
Authors
- Nour F. Attia (ORCID: https://orcid.org/0000-0002-0713-1738)
- Shu Xiao (ORCID: https://orcid.org/0000-0002-7211-7211)
- Jianji Wang (ORCID: https://orcid.org/0000-0003-2417-4630)
- Saihua Jiang (ORCID: https://orcid.org/0000-0003-0777-6787)
- Jiaqi Cheng (ORCID: https://orcid.org/0000-0001-9745-8090)
- Mingzhu Liu (ORCID: https://orcid.org/0000-0003-2973-5361)
- Haotian Zhao
Institutions
- Zhejiang International Studies University (CN)
- Longdong University (CN)
- National Institute of Standards (EG)
- Beihang University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1039/d4ta07959k
- Citations
- 11
- Primary Topic
- Advanced Materials and Mechanics
- Type
- article
- Field-Weighted Citation Impact
- 2.34
Funders
- National Natural Science Foundation of China
- Beihang University
- Natural Science Foundation of Guangdong Province
- Fundamental Research Funds for the Central Universities