Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters

Xiaojie Xu

doi:10.1021/acsomega.0c01438

journal article Jun 16, 2020

Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters

Xiaojie Xu

ACS Omega Vol. 5 No. 25 pp. 15344-15352 · American Chemical Society (ACS)

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References

45

[1]

10.1039/c4ra03291h

[2]

10.1016/j.solener.2004.03.031

[3]

10.3390/molecules20011319

[4]

Recent Advances in Heterogeneous Photocatalytic CO2 Conversion to Solar Fuels

Kan Li, Bosi Peng, Tianyou Peng

ACS Catalysis 10.1021/acscatal.6b02089

[5]

10.1039/c5cs00352k

[6]

10.1039/b412803f

[7]

10.1063/1.4915903

[8]

10.1109/jphot.2019.2950049

[9]

10.3390/catal7110317

[10]

10.1016/j.apcata.2013.10.006

[11]

10.1007/s10853-010-5113-0

[12]

10.2465/ganko1941.77.117

[13]

10.1016/j.jallcom.2015.03.035

[14]

10.1007/s11082-016-0435-z

[15]

10.1007/s10971-015-3661-0

[16]

10.1007/s11356-014-3356-z

[17]

10.1016/j.apsusc.2014.10.045

[18]

10.1016/j.apsusc.2015.01.069

[19]

10.1016/j.catcom.2007.12.014

[20]

10.1016/j.apcatb.2011.12.039

[21]

10.1016/j.apcata.2014.12.053

[22]

10.1016/j.jphotochem.2015.07.014

[23]

10.1016/j.jallcom.2014.12.065

[24]

10.1007/s10971-012-2924-2

[25]

10.1039/c1jm10407a

[26]

Schwartz, J.; Koch, C. C.; Zhang, Y.; Liu, X. Formation of Bismuth Strontium Calcium Copper Oxide Superconductors. U.S. Patent US9773962B2 (2017). https://patents.google.com/patent/US9773962B2/en (accessed May 19, 2020).

[27]

10.1088/0953-2048/29/9/095012

[28]

10.1088/0953-2048/29/12/125005

[29]

10.1088/0953-2048/27/5/055016

[30]

10.1016/j.apcata.2005.02.025

[31]

10.1016/s0021-9517(03)00034-4

[32]

10.1021/cm990172z

[33]

10.1111/j.1551-2916.2005.00151.x

[34]

10.1021/jp0312924

[35]

10.1016/j.matdes.2015.12.022

[36]

10.1364/ao.50.000379

[37]

10.1021/jp710283p

[38]

10.1016/j.apsusc.2011.09.085

[39]

10.1016/j.jpcs.2010.08.021

[40]

10.1088/2053-1591/ab61b2

[41]

Ong, S. Materials Project. https://www.materialsproject.org/wiki/index.php/GGA%2BU_calculations#cite_note-1 (2016; accessed May 19, 2020).

[42]

10.1103/physrevb.73.195107

[43]

10.1016/j.commatsci.2020.109583

[44]

10.1007/s00339-020-03503-8

[45]

10.1016/j.physleta.2020.126500

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Details

Published: Jun 16, 2020
Vol/Issue: 5(25)
Pages: 15344-15352
License: View

Authors

X

Xiaojie Xu

North Carolina State University, Raleigh, North Carolina 27695, United States

Cite This Article

Xiaojie Xu (2020). Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters. ACS Omega, 5(25), 15344-15352. https://doi.org/10.1021/acsomega.0c01438

Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters

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