Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels
The escalating global climate crisis and energy challenges have made the development of efficient radiative cooling materials increasingly urgent. This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). The model integrated multiple par...
Saved in:
| Main Authors: | Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Gels |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2310-2861/11/1/70 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Anisotropic Hygroscopic Hydrogels with Synergistic Insulation-Radiation-Evaporation for High-Power and Self-Sustained Passive Daytime Cooling
by: Xiuli Dong, et al.
Published: (2025-04-01) -
Radiative Heat Transfer Properties of Fiber–Aerogel Composites for Thermal Insulation
by: Mohanapriya Venkataraman, et al.
Published: (2025-07-01) -
Switchable radiative cooling and solar heating for sustainable thermal management
by: Yoo Myung Jin, et al.
Published: (2023-12-01) -
Microwave-Assisted Preparation of Hierarchical Porous Carbon Aerogels Derived from Food Wastes for Supercapacitors
by: Zijun Dong, et al.
Published: (2025-03-01) -
A Review of High-Temperature Resistant Silica Aerogels: Structural Evolution and Thermal Stability Optimization
by: Zhenyu Zhu, et al.
Published: (2025-05-01)