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4. Optical wood with switchable solar transmittance for all-round thermal management

Author

Gao, He, Li, Ying, Xie, Yanjun, Liang, Daxin, Li, Jian, Wang, Yonggui, Xiao, Zefang, Wang, Haigang, Gan, Wentao, Pattelli, Lorenzo, and Xu, Hongbo

Subjects
Physics - Optics, Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

Technologies enabling passive daytime radiative cooling and daylight harvesting are highly relevant for energy-efficient buildings. Despite recent progress demonstrated with passively cooling polymer coatings, however, it remains challenging to combine also a passive heat gain mechanism into a single substrate for all-round thermal management. Herein, we developed an optical wood (OW) with switchable transmittance of solar irradiation enabled by the hierarchically porous structure, ultralow absorption in solar spectrum and high infrared absorption of cellulose nanofibers. After delignification, the OW shows a high solar reflectance (94.9%) in the visible and high broadband emissivity (0.93) in the infrared region (2.5-25 $\mu$m). Owing to the exceptional mass transport of its aligned cellulose nanofibers, OW can quickly switch to a new highly transparent state following phenylethanol impregnation. The solar transmittance of optical wood (OW-II state) can reach 68.4% from 250 to 2500 nm. The switchable OW exhibits efficient radiative cooling to 4.5 {\deg}C below ambient temperature in summer (81.4 W m$^{-2}$ cooling power), and daylight heating to 5.6 {\deg}C above the temperature of natural wood in winter (heating power 229.5 W m$^{-2}$), suggesting its promising role as a low-cost and sustainable solution to all-season thermal management applications., Comment: accepted version of the manuscript published on Composites Part B: Engineering

Published
2023
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41. Y-net: 3D intracranial artery segmentation using a convolutional autoencoder

Author

Chen, Li, Xie, Yanjun, Sun, Jie, Balu, Niranjan, Mossa-Basha, Mahmud, Pimentel, Kristi, Hatsukami, Thomas S., Hwang, Jenq-Neng, and Yuan, Chun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this report, an optimized CAE model (Y-net) was trained to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. The trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation., Comment: 5 pages, 4 figures, an improved version after accepted by IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA, November 13 - 16, 2017

Published
2017

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