Multifunctional shape-memory smart window based on femtosecond-laser-printed photothermal microwalls
Abstract Smart windows (SWs) garner significant potential in green buildings owing to their capability of on-demand tuning the solar gains. Apart from solar regulation, people always desire a type of slippery SW which can repel the surface hydrous contaminants for anti-fouling application. Unfortunately, the up-to-date slippery SWs that respond to electrical/thermal stimuli have drawbacks of inferior durability and high energy-consumption, which greatly constrain their practical usability. This article presents our current work on an ultra-robust and energy-efficient near-infrared-responsive smart window (NIR-SW) which can regulate the optical transmittance and droplet’s adhesion in synergy. Significantly, laser-printing strategy enables us to seed the shape-memory photothermal microwalls on a transparent substrate, which can promote daylighting while maintaining privacy by near-infrared (NIR) switching between being transparent and opaque. As a light manipulator, it turns transparent with NIR-activated erect microwalls like an open louver; however, it turns opaque with the pressure-fixed bent microwalls akin to a closed louver. Simultaneously, the droplets can easily slip on the surface of erect microwalls similar to a classical lotus effect; by contrast, the droplets will tightly pin on the surface of bent microwalls analogous to the prevalent rose effect. Owing to shape-memory effect, this optical/wettability regulation is thus reversible and reconfigurable in response to thealternate NIR/pressure trigger. Moreover, NIR-SW unfolds a superior longevity despite suffering from the raindrop’s impacting more than 10 000 cycles. Remarkably, such a new-type SW is competent for thermal management, anti-icing system, peep-proof screen, and programmable optics. This work renders impetus for the researchers striving for self-cleaning intelligent windows, energy-efficient greenhouse, and so forth.
Authors
- Yachao Zhang (ORCID: https://orcid.org/0000-0002-4468-6142)
- Sijia Guo
- Sizhu Wu (ORCID: https://orcid.org/0000-0002-6739-4898)
- Bingrui Liu (ORCID: https://orcid.org/0000-0002-1245-0338)
- Chenchu Zhang (ORCID: https://orcid.org/0000-0002-3433-598X)
- Zhaoxin Lao (ORCID: https://orcid.org/0000-0002-7673-0590)
- Dong Qing Wu (ORCID: https://orcid.org/0000-0003-0623-1515)
- Long Zhang (ORCID: https://orcid.org/0000-0001-7978-8774)
- Chao Chen (ORCID: https://orcid.org/0000-0002-8061-0292)
- Hao Yao
Institutions
- University of Science and Technology of China (CN)
- Anhui Agricultural University (CN)
- Hefei University of Technology (CN)
- Hefei University (CN)
Publication Details
- Journal
- International Journal of Extreme Manufacturing
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1088/2631-7990/ad8710
- Citations
- 10
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- National Natural Science Foundation of China