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Perovskite solar cells have become a research hotspot in the photovoltaic field due to their excellent photoelectric performance and low-cost preparation processes. However, the environmental toxicity of traditional lead-containing perovskite materials and the optimization of device performance encounter key problems that limit their commercial applications. Numerical simulation methods provide an efficient and cost-effective approach for optimizing perovskite solar cell devices, allowing for rapid material screening and structural parameter optimization, thereby reducing experimental trial-and-error costs. Based on SCAPS-1D, this work systematically investigates the performance of solar cells with the structure FTO/SnO2/perovskite layer/Cu2O/Au by using numerical simulation. Seven different lead-free and lead-containing perovskite materials are selected as the light-absorbing layer. By the comparative analysis of their photoelectric characteristics, this work explores the influences of perovskite layer thickness, electron transport layer thickness, hole transport layer thickness, interface defect state density, and carrier concentration on device performance. Furthermore, temperature testing and J-V and QE curve analyses are conducted on the optimized perovskite solar cells. The results indicate that excessive thickness of the perovskite layer increases carrier recombination rate, thereby reducing cell efficiency. The optimized Cs2PtI6-based perovskite solar cell exhibits the best performance, with a power conversion efficiency of 27.95%, which is much higher than those of other lead-free and some lead-containing perovskite devices. Under extreme temperature conditions of 600 K, the PCE of Cs2PtI6 remains around 50% of its value at room temperature (300 K). This study reveals the influences of different perovskite materials and device parameters on photovoltaic performance through systematic numerical simulation analysis, providing a theoretical basis for designing efficient and stable perovskite solar cells. -
Keywords:
- perovskite solar cells /
- numerical simulation /
- parameter optimization /
- SCAPS
[1] Prasanna J L, Kumar A, Ravi Kumar M, Gayathri K, Santhosh C, Kumer S, Mohan E, Udayakumar S 2024 Int. J. Energy Res. 2024 3942154
Google Scholar
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Google Scholar
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Google Scholar
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图 2 钙钛矿层厚度对FTO/SnO2/钙钛矿层/Cu2O/Au性能参数的影响 (a) Voc; (b) Jsc; (c) FF; (d) PCE
Fig. 2. Effect of perovskite layer thickness on the performance parameters of FTO/SnO2/Perovskite Layer/Cu2O/Au: (a) Voc; (b) Jsc; (c) FF; (d) PCE.
图 3 L1/L2缺陷态密度对FTO/SnO2/钙钛矿层/Cu2O/Au性能参数的影响 (a) Voc; (b) Jsc; (c) FF; (d) PCE
Fig. 3. Influence of L1/L2 defect state density on the performance parameters of FTO/SnO2/Perovskite Layer/Cu2O/Au: (a) Voc; (b) Jsc; (c) FF; (d) PCE.
图 4 ND对FTO/SnO2/钙钛矿层/Cu2O/Au的性能参数的影响 (a) Voc; (b) Jsc; (c) FF; (d) PCE
Fig. 4. Effect of ND on the performance parameters of FTO/SnO2/Perovskite Layer/Cu2O/Au: (a) Voc; (b) Jsc; (c) FF; (d) PCE.
图 5 NA对FTO/SnO2/钙钛矿层/Cu2O/Au性能参数的影响 (a) Voc; (b) Jsc; (c) FF; (d) PCE
Fig. 5. Effect of NA on the performance parameters of FTO/SnO2/Perovskite Layer/Cu2O/Au: (a) Voc; (b) Jsc; (c) FF; (d) PCE.
图 6 (a)温度对FF的影响; (b) 温度对PCE的影响
Fig. 6. (a) Effect of temperature on FF; (b) effect of temperature on PCE.
图 7 不同钙钛矿材料优化后的太阳能电池的J-V特性
Fig. 7. J-V characteristics of solar cells optimized with different perovskite materials.
图 8 不同钙钛矿材料优化后的QE曲线
Fig. 8. QE curves of solar cells optimized with different perovskite materials.
表 1 模拟研究中使用的PSCs物理参数
Table 1. Physical parameters of PSCs used in simulation studies.
Parameter SnO2 Cu2O Cs2BiAgI6 CsPbI3 Cs2PtI6 CsGeI3 PeDA2MA5Pb6I19 MASnI3 FAMAPbI3 Layer thickness/nm 100 100 100 100 100 100 100 100 100 Bandgap/eV 3.3 2.17 1.6 1.694 1.37 1.6 1.6 1.35 1.53 Electron affinity/eV 4 3.2 3.9 3.95 4.3 3.52 3.98 4.17 4 Relative permittivity 9 6.6 6.5 6 4.8 18 25 6.5 9 Effective conduction band
density/(1017 cm–3)2.2 2500 100 1100 0.003 10 7.5 10 100 Effective valence band
density /(1017 cm–3)2.2 2500 100 800 1 100 18 100 50 Electron mobility/(cm2·V–1·s–1) 20 80 2 25 62.6 20 1.4 1.6 5 Hole mobility/(cm2·V–1·s–1) 10 80 2 25 62.6 20 0.3 1.6 3 Donor concentration/(1017 cm–3) 10 0 0 0.01 0.00001 1 0 0 0.2 Acceptor concentration/(1017 cm–3) 0 30 10 0 0.01 1 0 10 0.2 Density of defect state/(1014 cm–3) 1 10 1 1 1000 1 2.5 1 0.1 Refs. [1] [15] [21] [13] [22] [23] [24] [25] [26] 
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表 2 优化前后的电池性能对比
Table 2. Comparison of solar cell performance before and after optimization.
钙钛矿层材料 优化前 优化后 FF/% PCE/% FF/% PCE/% Cs2BiAgI6 80.72 11.54 86.73 23.90 CsPbI3 87.73 10.24 83.58 17.91 Cs2PtI6 78.00 16.92 80.53 27.95 CsGeI3 65.37 13.66 86.90 25.73 PeDA2MA5Pb6I19 21.99 13.58 72.00 23.52 MASnI3 59.22 20.66 74.99 26.90 FAMAPbI3 79.00 14.80 80.89 27.82
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[1] Prasanna J L, Kumar A, Ravi Kumar M, Gayathri K, Santhosh C, Kumer S, Mohan E, Udayakumar S 2024 Int. J. Energy Res. 2024 3942154
Google Scholar
[2] 张娜, 米倩玉, 邓嘉纬, 赵晓军 2024 中国软科学 2 1
Google Scholar
Zhang N, Mi Q Y, Deng J J, Zhao X J 2024 China Soft Science. 2 1
Google Scholar
[3] Soni A, Bhamu K, Sahariya J 2020 J. Alloys Compd. 817 152758
Google Scholar
[4] Maho A, Lobet M, Daem N, Piron P, Spronck G, Loicq J, Cloots R, Colson P, Henrist C, Dewalque J 2021 ACS Appl. Energy Mater. 4 1108
Google Scholar
[5] Wang Y D, Duan J L, Yang X Y, Liu L Q, Zhao L L, Tang Q W 2020 Nano Energy 69 104418
Google Scholar
[6] Brenner T M, Egger D A, Kronik L, Hodes G, Cahen D 2016 Nat. Rev. Mater. 1 15007
Google Scholar
[7] Miyata A, Mitioglu A, Plochocka P, Portugall O, Wang J, Stranks S, Snaith H, Nicholas R 2015 Nat. Phys. 11 582
Google Scholar
[8] Song Z N, McElvany C, Phillips A, Celik I, Krantz P, Watthage S, Liyanage G, Apul D, Heben M 2017 Energy Environ. Sci. 10 1297
Google Scholar
[9] Kim Y, Cho H, Heo J, Kim T, Myoung N, Lee C, Im S, Lee T 2015 Adv. Mater. 27 1248
Google Scholar
[10] Dou L T, Yang Y, You J B, Hong Z R, Chang W H, Li G, Yang Y 2014 Nat. Commun. 5 5404
Google Scholar
[11] Kojima A, Teshima K, Shira Y, Miyasaka T 2009 J. Am. Chem. So. 131 6050
Google Scholar
[12] Bhavsar K, Lapsiwala P 2021 Semicond. Phys. Quantum Electron. Optoelectron 24 341
Google Scholar
[13] Hossain M, Toki G, Alam I, Pandey R, Samajdar D, Rahman M, Islam M, Bencherif M, Madan J, Mohammed M 2023 New J. Chem. 47 4801
Google Scholar
[14] Gatti T, Menna E, Meneghetti M, Maggini M, Petrozza A, Lamberti F 2017 Nano Energy 41 84
Google Scholar
[15] Mustafa G M, Younas B, Saba S, Elqahtani Z B, Alwadaid N, Aftab S 2024 RSC advances 14 18957
Google Scholar
[16] Uddin M, Mashud M, Toki G, Pandey R, Zulfiqar M, Saidani O, Chandran K, Ouladsmane M, Hossain M 2024 J. Opt. 53 3726
Google Scholar
[17] Burgelman M, Nollet P, Degrave S 2000 Thin Solid Films 361–362 527
Google Scholar
[18] Singh A, Srivastava S, Mahapatra A, Baral J, Pradhan B 2021 Opt. Mater. 117 111193
Google Scholar
[19] Hao L S, Li T, Ma X X, Wu J, Qiao L X, Wu X F, Hou G Y, Pei H N, Wang X B, Zhang X Y 2021 Opt. Quant. Electron. 53 524
Google Scholar
[20] Rai S, Pandey B, Dwivedi D 2020 Opt. Mater. 100 109631
Google Scholar
[21] Hossain M, Arnab A, Das R, Hossain K, Rubel M, Rahman M, Bencherif H, Emetere M, Mohammed M, Pandey R 2022 RSC Adv. 12 35002
Google Scholar
[22] Amjad A, Qamar S, Zhao C C, Fatima K, Sultan M, Akhter Z 2023 RSC Adv. 13 23211
Google Scholar
[23] Ahmad W, Noman M, Jan S, Khan A 2023 Royal Soc. Open Sci. 10 221127
Google Scholar
[24] Schulte L, White W, Renna L, Ardo S 2021 Joule 5 2380
Google Scholar
[25] ASiddique A, Helal S, Haque M 2024 J. Ovonic Res. 20 187
Google Scholar
[26] Subudhi P, Punetha D 2023 Sci. Rep. 13 19485
Google Scholar
[27] Jayan K D, Sebastian V, Kurian J 2021 Solar Energy 221 99
Google Scholar
[28] Mohandes A, Moradi M, Nadgaran H 2021 Opt. Quant. Electron. 53 319
Google Scholar
[29] Salah M, Abouelatta M, Shaker A, Hassan K, Saeed A 2019 Semicond. Sci. Tech. 34 115009
Google Scholar
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