Supplemental Information. Rich Mesostructures Derived from Natural. Woods for Solar Steam Generation

Transcription

1 JOUL, Volume 1 Supplemental Information Rich Mesostructures Derived from Natural Woods for Solar Steam Generation Chao Jia, Yiju Li, Zhi Yang, Guang Chen, Yonggang Yao, Feng Jiang, Yudi Kuang, Glenn Pastel, Hua Xie, Bao Yang, Siddhartha Das, and Liangbing Hu

2 SUPPLEMENTAL INFORMATION

3 Supplemental Figures Figure S1. Water transport along the microchannel direction of the wood to demonstrate the hydrophilicity. Note that the (A, B) poplar and (C, D) pine wood with higher porosity possess better hydrophilicity and water transport properties than (E, F) cocobolo wood. Related to Figure 1.

4 Figure S2. (A) Digital image, (B) cross section SEM image, (C) top view SEM image, (D) pore diameter distribution, (E) high magnification cross section SEM image and (F) pit diameter distribution of balsa wood. The density of balsa wood is 175 kg m -3. Balsa wood has vessels and fiber tracheids, exhibiting distinctly different lumen diameters. Scale bars: (A) 2 cm; (B) 200 μm; (C) 500 μm; (E) 10 μm. Related to Figure 2 and 3.

5 Figure S3. (A) Digital image, (B) cross section SEM image, (C) top view SEM image, (D) pore diameter distribution, (E) high magnification cross section SEM image and (F) pit diameter distribution of basswood. The density of basswood is 445 kg m -3. Basswood has vessels and fiber tracheids, exhibiting distinctly different lumen diameters. Scale bars: (A) 2 cm; (B) 200 μm; (C) 250 μm; (E) 5 μm. Related to Figure 2 and 3.

6 Figure S4. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of cocobolo wood. The density of cocobolo wood is 1118 kg m -3. Scale bars: (A) 2 cm; (B) 100 μm; (C) 100 μm; (D) 10 μm. Related to Figure 2 and 3.

7 Figure S5. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of elm wood. The density of elm wood is 654 kg m -3. Scale bars: (A) 2 cm; (B) 500 μm; (C) 500 μm; (D) 10 μm. Related to Figure 2 and 3.

8 Figure S6. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of locust wood. The density of locust wood is 687 kg m -3. Scale bars: (A) 2 cm; (B) 100 μm; (C) 500 μm; (D) 10 μm. Related to Figure 2 and 3.

9 Figure S7. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of maple wood. The density of maple wood is 620 kg m -3. Scale bars: (A) 2 cm; (B) 50 μm; (C) 250 μm; (D) 20 μm. Related to Figure 2 and 3.

10 Figure S8. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of oak wood. The density of oak wood is 646 kg m -3. Scale bars: (A) 2 cm; (B) 100 μm; (C) 500 μm; (D) 10 μm. Related to Figure 2 and 3.

11 Figure S9. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of olive wood. The density of olive wood is 851 kg m -3. Scale bars: (A) 2 cm; (B) 50 μm; (C) 100 μm; (D) 10 μm. Related to Figure 2 and 3.

12 Figure S10. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of padauk wood. The density of padauk wood is 766 kg m -3. Scale bars: (A) 5 cm; (B) 300 μm; (C) 200 μm; (D) 10 μm. Related to Figure 2 and 3.

13 Figure S11. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of poplar wood. The density of poplar wood is 518 kg m -3. Scale bars: (A) 2 cm; (B) 300 μm; (C) 400 μm; (D) 20 μm. Related to Figure 2 and 3.

14 Figure S12. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of cedar wood. The density of cedar wood is 471 kg m -3. Scale bars: (A) 2 cm; (B) 100 μm; (C) 200 μm; (D) 20 μm. Related to Figure 2 and 3.

15 Figure S13. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of pine wood. The density of pine wood is 568 kg m -3. Scale bars: (A) 2 cm; (B) 100 μm; (C) 300 μm; (D) 30 μm. Related to Figure 2 and 3.

16 Figure S14. (A) Digital image, (B) top view SEM image, (C) cross section SEM image and (D) high magnification cross section SEM image of yew wood. The density of yew wood is 674 kg m -3. Scale bars: (A) 1 cm; (B) 50 μm; (C) 200 μm; (D) 50 μm. Related to Figure 2 and 3.

17 Figure S15. (A) Density and (B) porosity of different woods. Related to Figure 3.

18 Figure S16. Schematic of thermal conductivity test system. Related to Figure 4.

19 Figure S17. (A) Infrared image of poplar L wood to show the temperature distribution at given power input. (B) The temperature distribution result obtained by ANSYS numerical simulation. Related to Figure 4.

20 Figure S18. Stress-strain curves of different (A) L woods and (B) R woods. Tensile strength and elongation at break of different (C) L woods and (D) R woods. Data are presented as the mean with error bars. Related to Figure 4.

21 Figure S19. SEM images of the original (A) poplar, (C) pine, and (E) cocobolo wood, and carbonized (B) poplar, (D) pine, and (F) cocobolo wood. Insets are the close-up of the wood microstructures. Scale bars: 100 μm; insets of (A) and (B): 50 μm; insets of (C)-(F): 20 μm. Related to Figure 5.

22 Figure S20. Transmittance spectra of poplar, pine and cocobolo wood-based SSGDs in the range of nm wavelength. Related to Figure 5.

23 Figure S21. Reflectance and absorptance spectra of (A, B) poplar, (C, D) pine and (E, F) cocobolo woodbased SSGDs at different carbonization time in the range of nm wavelength. Related to Figure 5.

24 Figure S22. The evaporation rates of the poplar, pine, and cocobolo wood-based SSGDs under dark field. Related to Figure 5.

25 Figure S23. The irradiation time dependence of surface temperature for poplar, pine and cocobolo woodbased SSGDs in water under solar illumination of 10 suns. The surface temperature increases quickly after the irradiation and the stable temperature can be reached in about 10 min. Related to Figure 5.

26 Figure S24. The solar steam generation efficiencies of the poplar, pine, and cocobolo wood-based SSGDs, and pure water under 1 sun. Related to Figure 5.

27 Figure S25. (A) E. R. of the cocobolo wood-based SSGD with different thickness under various solar intensities. (B) Relationship of E. R. and thickness (T) of the SSGDs. Red five-pointed stars denote the E. R. data we obtained at different thickness. Related to Figure 5. Supplemental Experimental Procedures Thermal conductivity measurement The schematic of Steady State Laser-Infrared Camera Thermal Conductivity Characterization System was shown in Figure S16, which incorporates a laser heat source, two standard aluminum (Al) blocks, a temperature controlled heat sink and an infrared thermal camera. The wood sample was placed between two highly conductive aluminum blocks (206 W m -1 K -1 ) with thermal interface material to minimize the thermal resistance. A continuous and stable input power was controlled and provided by a Coherent Highlight FAP-1000 (820 nm) laser. The temperature controlled water bath was used as heat sink at the bottom of lower aluminum block. A thin layer of graphite (ε=0.9) coating was applied on the exposed surfaces for accurate temperature measurement from IR camera. The steady state temperature distribution was recorded using a FLIR Merlin MID Infrared (IR) camera (Figure 4B, 4D, and S17). In this system, heat conduction through the wood sample and radiation/convection heat transfer from the system to the environment coexist. A standard Teflon sample with a calibrated thermal conductivity of 0.25 W m -1 K -1 was used to identify the overall effective average heat transfer coefficients of radiation and convection. These variables were parameterized in the 3D finite element model of the system built in ANSYS (V17.1), and adjusted to match the experimental temperature distribution results. The geometry in ANSYS was modeled as the same as the experimental conditions. The emissivity used in the ANSYS 3D simulation was identified as 0.9 due to the existence of the calibrated graphite coating on the external surface of the system. In addition, the radiation heat transfer rate can be calculated by the Stephan-Boltzmann Law according to the temperature gradient between the system surface and external environment. After the effective average coefficients were identified, the model was updated with the temperature distribution from the actual wood samples. Their thermal conductivities were parameterized until a good temperature gradient profile match between the IR camera experimental results and ANSYS numerical simulation was achieved (Figure S17, here we took poplar L wood as an example).