journal article·2020-12-28

Functionalized CNTs as Effective Additives to Improve the Efficiency of Perovskite Solar Cells

Xiaotong Ma, Teng Zhang, Baohua Zhao, Chengben Liu, Xinmei Li, Heyuan Liu, Guangwu Yang, Yanli Chen, Lilin Jiang, Xiyou Li

ACS Applied Energy Materials· Volume 3· Issue 12
Publisher page

Abstract

High-quality and uniform perovskite films are the first step toward high-performance perovskite solar cells (PSCs). Adding additives to regulating the film formation process is proved to be an efficient way to acquire high-quality perovskite films. In this study, we use functionalized carbon nanotubes (CNTs) as effective additives to improve the power conversion efficiency (PCE) of PSCs. Three kinds of CNTs, namely, pristine CNTs, amino-group-functionalized CNTs (NH2-CNTs), and carboxyl-group-functionalized CNTs (COOH-CNTs) are selected. The PCE of PSCs doped with pristine CNTs (13.91%) drops compared with that of a control device (16.56%), while the PCE increases to 17.47% for NH2-CNT-doped PSCs and 17.75% for COOH-CNT-doped PSCs. A detailed study shows that both NH2-CNTs and COOH-CNTs can facilitate the crystallization of the perovskite films. Compared with a control film, the hole mobility of the perovskite films can be increased by 1 order of magnitude in a COOH-CNT-doped perovskite film. This contributes to better carrier transportation in PSCs. After optimizing the length and doping concentration of the COOH-CNTs, the PCE for PSCs doped with COOH-CNTs steadily improves to 18.68%. Furthermore, the stability of PSCs has also been improved with COOH-CNT-doped perovskite films. Our study provides insightful hints for using functionalized carbon-based materials to develop high-performance perovskite-based optoelectronic devices.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Perovskite Materials and Applications · Conducting polymers and applications · Solid-state spectroscopy and crystallography

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Science, China University of Petroleum (East China), Qingdao 266580, P. R. China

School of Materials Science and Engineering, China University of Petroleum (East China), Qingdao 266580, P. R. China

Institute of New Energy, China University of Petroleum (East China), Qingdao 266580, P. R. China

School of Artificial Intelligence, Hezhou University, Hezhou 542800, P. R. China

Funding

China Postdoctoral Science Foundation · Taishan Scholar Project of Shandong Province · Natural Science Foundation of Shandong Province · the Fundamental Research Funds for the Central Universities · the Tsingtao postdoc program · the Innovation Project of China University of Petroleum (East China)

References (56)

Show source-provided references
1. Best Research-Cell Efficiency Chart.pdf This Plot is Courtesy of the National Renewable Energy Laboratory, Golden, CO. Available https://www.nrel.gov/pv/cell-efficiency.html.
2. 10.1039/C6CC00268D10.1039/C6CC00268D ↗
3. 10.1038/nature1250910.1038/nature12509 ↗
4. 10.1038/nmat401410.1038/nmat4014 ↗
5. 10.1002/aenm.20150208710.1002/aenm.201502087 ↗
6. 10.1016/j.nanoen.2017.02.01910.1016/j.nanoen.2017.02.019 ↗
7. 10.1002/adfm.20160494410.1002/adfm.201604944 ↗
8. 10.1016/j.nanoen.2016.05.05210.1016/j.nanoen.2016.05.052 ↗
9. 10.1021/jacs.5b0032110.1021/jacs.5b00321 ↗
10. 10.1002/admi.20160048410.1002/admi.201600484 ↗
11. 10.1002/adma.20170107310.1002/adma.201701073 ↗
12. 10.1039/C7TA01798G10.1039/C7TA01798G ↗
13. 10.1002/adma.20160302110.1002/adma.201603021 ↗
14. 10.1002/advs.20190124110.1002/advs.201901241 ↗
15. 10.1126/science.aaa047210.1126/science.aaa0472 ↗
16. 10.1002/aenm.20160166010.1002/aenm.201601660 ↗
17. 10.1038/nenergy.2016.14210.1038/nenergy.2016.142 ↗
18. 10.1021/acs.jpclett.5b0184310.1021/acs.jpclett.5b01843 ↗
19. 10.1039/C6TA02999J10.1039/C6TA02999J ↗
20. 10.1021/acsenergylett.7b0027810.1021/acsenergylett.7b00278 ↗
21. 10.1002/adfm.20160117510.1002/adfm.201601175 ↗
22. 10.1002/adma.20160059410.1002/adma.201600594 ↗
23. 10.1002/solr.20190014610.1002/solr.201900146 ↗
24. 10.1002/adfm.20150355910.1002/adfm.201503559 ↗
25. 10.1039/C8TA11925B10.1039/C8TA11925B ↗
26. 10.1039/C7EE02272G10.1039/C7EE02272G ↗
27. 10.1002/adfm.20180198510.1002/adfm.201801985 ↗
28. 10.1021/acsami.9b2111210.1021/acsami.9b21112 ↗
29. 10.1016/j.nanoen.2017.12.04110.1016/j.nanoen.2017.12.041 ↗
30. 10.1002/adfm.20170354610.1002/adfm.201703546 ↗
31. 10.1002/aenm.20190257910.1002/aenm.201902579 ↗
32. 10.1038/ncomms808110.1038/ncomms8081 ↗
33. 10.1002/adma.20180469110.1002/adma.201804691 ↗
34. 10.1002/adma.20180141810.1002/adma.201801418 ↗
35. 10.1002/adma.20160677410.1002/adma.201606774 ↗
36. 10.1039/C6TA09687E10.1039/C6TA09687E ↗
37. 10.1039/C4DT03869J10.1039/C4DT03869J ↗
38. 10.1002/adma.20160380810.1002/adma.201603808 ↗
39. 10.1002/aenm.20190308310.1002/aenm.201903083 ↗
40. 10.1021/acsami.9b1337510.1021/acsami.9b13375 ↗
41. 10.1039/C8NR08295B10.1039/C8NR08295B ↗
42. 10.1039/C4EE01983K10.1039/C4EE01983K ↗
43. 10.1021/acs.nanolett.7b0020010.1021/acs.nanolett.7b00200 ↗
44. 10.1002/smll.20170122510.1002/smll.201701225 ↗
45. 10.1002/anie.20160952910.1002/anie.201609529 ↗
46. 10.1021/acs.jpcc.7b0327910.1021/acs.jpcc.7b03279 ↗
47. 10.1039/C5TA09249C10.1039/C5TA09249C ↗
48. 10.1038/ncomms874710.1038/ncomms8747 ↗
49. 10.1126/science.aaa576010.1126/science.aaa5760 ↗
50. 10.1021/jacs.8b1309110.1021/jacs.8b13091 ↗
51. 10.1038/s41566-019-0398-210.1038/s41566-019-0398-2 ↗
52. 10.1039/C5TA07714A10.1039/C5TA07714A ↗
53. 10.1126/science.aaa272510.1126/science.aaa2725 ↗
54. 10.1016/j.cplett.2016.09.00410.1016/j.cplett.2016.09.004 ↗
55. 10.1016/j.nanoen.2017.10.00610.1016/j.nanoen.2017.10.006 ↗
56. 10.1002/adfm.20150521510.1002/adfm.201505215 ↗