The performance of an organic solar cell is strongly influenced by the structure of the photosensitizer. In this work, the open-circuit voltage (V_{OC}) and conversion efficiency (η) of a series of coumarin dyes are quantitatively related to the structure of nine coumarin derivatives. The Quantitative Structure Property Relationship (QSPR) is performed using the statistical method of multiple linear regression. In addition, descriptors determined from the ground state at the Cam_B3lyp/6-31G(d, p) level of theory and from the 2D structure of the molecules are mathematically related to the photovoltaic properties. These VOC and η models are accredited with very good statistical indicators (R^{2} = 0.906 and 0.918; Q_{cv}^{2}= 0.845 and 0.849; S= 0.045 and 0.112; F = 14.524 and 16.846). These statistical indicators confirm the robustness and performance of the models developed. The results show that Voc improves with decreasing surface tension (ts) and increasing number of cycles (cycle). As for the conversion efficiency of light radiation into electrical energy, it is optimal when the light harvesting efficiency (LHE_{th}) and the excited state lifetime (τ_{th}) are high. In conclusion, these models have good predictive capabilities and can be used to predict and explain the open-circuit voltage and efficiency of coumarin derivatives that belong to the same field of application.
Published in | Modern Chemistry (Volume 12, Issue 2) |
DOI | 10.11648/j.mc.20241202.12 |
Page(s) | 33-46 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Solar Cell, Coumarin, DFT, QSPR
Colorant | ${\mathit{\lambda}}_{\mathit{max}}^{\mathit{abs}}$(nm) | J_{sc} (mA cm^{-2}) | V_{OC} (V) | ff | η(%) |
---|---|---|---|---|---|
C343 | 442 | 4.100 | 0.410 | 0.560 | 0.900 |
NKX-2311 | 504 | 15.200 | 0.550 | 0.620 | 5.200 |
NKX-2388 | 493 | 12.900 | 0.500 | 0.640 | 4.100 |
NKX-2398 | 451 | 11.100 | 0.510 | 0.600 | 3.400 |
NKX-2586 | 506 | 15.100 | 0.470 | 0.500 | 3.500 |
NKX-2593 | 510 | 14.700 | 0.670 | 0.730 | 7.200 |
NKX-2677 | 511 | 14.300 | 0.730 | 0.740 | 7.700 |
NKX-2753 | 492 | 16.100 | 0.600 | 0.690 | 6.700 |
NKX-2807 | 566 | 14.300 | 0.510 | 0.730 | 5.300 |
CODE | Descriptors | Photovoltac property | ||||
---|---|---|---|---|---|---|
Colorant | ts | cycle | LHEth | τth | pɳexp | Vocexp |
C343 | 73.900 | 4 | 0.832 | 0.049 | 0.046 | 0.410 |
NKX-2388 | 64.300 | 4 | 0.888 | 0.136 | -0.613 | 0.500 |
NKX-2586 | 61.900 | 4 | 0.992 | 0.078 | -0.544 | 0.470 |
NKX-2677 | 74.200 | 6 | 0.98 | 0.107 | -0.886 | 0.730 |
NKX-2753 | 59.000 | 5 | 0.986 | 0.085 | -0.826 | 0.600 |
NKX-2807 | 73.000 | 5 | 0.985 | 0.09 | -0.724 | 0.510 |
NKX-2311 | 62.900 | 4 | 0.972 | 0.095 | -0.716 | 0.550 |
NKX-2398 | 58.500 | 4 | 0.806 | 0.155 | -0.531 | 0.510 |
NKX-2593 | 68.200 | 5 | 0.986 | 0.096 | -0.857 | 0.670 |
Variables | LHE_{th} | τ_{th} | ts | cycle |
---|---|---|---|---|
LHE_{th} | 1 | 0.188 | -0.305 | 0.566 |
τ_{th} | 0.188 | 1 | -0.189 | 0.216 |
ts | -0.305 | -0.189 | 1 | 0.345 |
cycle | 0.566 | 0.216 | 0.345 | 1 |
QSPR model | Voc | ɳ_{exp} |
---|---|---|
Indicators statistical | Value | |
Number of compounds N | 9 | |
Correlation coefficient of the regression line R^{2} | 0.906 | 0.918 |
Prediction correlation coefficient$\mathrm{}{Q}_{\mathit{cv}}^{2}$ | 0.845 | 0.849 |
Standard Deviation | 0.045 | 0.112 |
Validation of Fischer F | 14.524 | 16.846 |
Confidence level α | 95% |
QSPR-V_{OC} model | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Iterations | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
${\mathrm{R}}_{\mathrm{r}}^{2}$ | 0,841 | 0,842 | 0,535 | 0,012 | 0,092 | 0,027 | 0,027 | 0,101 | 0,101 | 0,101 |
QSPR- ɳ_{exp} model | ||||||||||
Iterations | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
${R}_{r}^{2}$ | 0,852 | 0,804 | 0,840 | 0,321 | 0,276 | 0,711 | 0,790 | 0,472 | 0,461 | 0,251 |
Colorants | Voc_{exp} | Voc_{th} | Voc_{th}/Voc_{exp} |
---|---|---|---|
Training set | |||
C343 | 0.410 | 0.415 | 1.013 |
NKX-2388 | 0.500 | 0.459 | 0.918 |
NKX-2586 | 0.470 | 0.470 | 1.000 |
NKX-2677 | 0.730 | 0.694 | 0.951 |
NKX-2753 | 0.600 | 0.623 | 1.038 |
NKX-2807 | 0.510 | 0.559 | 1.097 |
Validation set | |||
NKX-2311 | 0.550 | 0.465 | 0.846 |
NKX-2398 | 0.510 | 0.485 | 0.951 |
NKX-2593 | 0.670 | 0.581 | 0.867 |
Tropsha criteria | ${\mathit{R}}_{\mathit{Test}}^{\mathit{2}}$ | ${\mathit{Q}}_{\mathit{cv\; Test}}^{\mathit{2}}$ | $\left|{\mathit{R}}_{\mathit{Test}}^{\mathit{2}}\mathit{-}{\mathit{R}}_{\mathit{0}}^{\mathit{2}}\right|$ | $\frac{\left|{\mathit{R}}_{\mathit{Test}}^{\mathit{2}}\mathit{-}{\mathit{R}}_{\mathit{0}}^{\mathit{2}}\right|}{{\mathit{R}}_{\mathit{Test}}^{\mathit{2}}}$ | $\frac{\left|{\mathit{R}}_{\mathit{Test}}^{\mathit{2}}\mathit{-}{\mathit{R}}_{\mathit{0}}^{\mathit{\text{'}}\mathit{2}}\right|}{{\mathit{R}}_{\mathit{Test}}^{\mathit{2}}}$ | k | k’ |
---|---|---|---|---|---|---|---|
QSPR-Voc model | 0.845 | 0.845 | 0 | 0 | 0.007 | 1.131 | 0.886 |
QSPR-ɳ model | 0.849 | 0.849 | 0 | 0 | 0.172 | 0.916 | 1.088 |
Colorants | pɳ exp | pɳ_{th} | pn_{th}/pn_{exp} |
---|---|---|---|
Training set | |||
C343 | 0.046 | 0.045 | 0.986 |
NKX-2311 | -0.716 | -0.702 | 0.980 |
NKX-2388 | -0.613 | -0.667 | 1.089 |
NKX-2398 | -0.532 | -0.507 | 0.953 |
NKX-2586 | -0.544 | -0.666 | 1.225 |
NKX-2753 | -0.826 | -0.688 | 0.833 |
Validation set | |||
NKX-2593 | -0.857 | -0.755 | 0.880 |
NKX-2677 | -0.887 | -0.801 | 0.903 |
NKX-2807 | -0.724 | -0.715 | 0.987 |
DSSC | Dye-Sensitized Solar Cells |
QSPR | Quantitative Structure- Property Relationship |
DFT | Density Functional Theory |
TD-DFT | Time-Dependent DFT |
SLR | Simple Linear Regression |
MLR | Multiple Linear Regression |
PLS | Partial Least Squares |
LOO | Leave-One Out |
LMO | Leave-Many Out |
V_{OC} | Open-Circuit Voltage |
[1] | B. O’Regan et M. Graetzel, «A Low-Cost, High-Efficiency Solar Cell Based on Dye Sensitized Colloidal TiO_{2} Films» Nature, vol. 353, p. 737 – 740, 1991. |
[2] | A. I. E. Duarte, A. L. L. Cruz, A. Marquina, J. A. A. Martínez, A. G. Juárez et C. Z. Islas, «One-step method to simultaneously grow TiO_{2} compact and porous layers for DSSC photoelectrodes» Appl Nanosci, vol. 14, p. 819–826, 2024. |
[3] | A. Saraswathi, N. Shobanadevi, M. Muthupriya, M. B. M. Yusuf et T. A. Sheeba, «Synergistic Incorporation of 2D Graphitic Carbon Nitride into Bimetal Oxide Photoanodes Towards Higher-Performance DSSCs» J. Electron. Mater, vol. 53, p. 3384–3397, 2024. |
[4] | S. A. Abrol, C. Bhargava et P. Sharma, «Fabrication of DSSC using doctor blades method incorporating polymer electrolytes,» Mater. Res. Exp., vol. 8, p. 045010, 2021. |
[5] | N. K. N’guessan, K. Bamba, W. P. Ouattara et N. Ziao, «Theoretical Investigation of Structural and Electronic Properties of Ruthenium Azopyridine Complexes Dyes for Photovoltaic Applications by Using DFT and TD-DFT Methods,» European Scientific Journal, vol. 14, n° %121, p. 1857 – 7881, 2018. |
[6] | N. T. Sethu et S. Shanmugan, «Towards sustainable solar cells: unveiling the latest developments in bio-nano materials for enhanced DSSC efficiency,» Clean Energy, vol. 8, n° %13, p. 238–257, 2024. |
[7] | K. Sharma, V. Sharma et S. S. Sharma, «Dye-sensitized solar cells: fundamentals and current status,» Nanoscale Res Lett, vol. 13, p. 381, 2018. |
[8] | L. Ouattara, K. Bamba, M. G.-R. Koné, J. S. N’dri, K. N. N’Guessan, P. M. Ouattara et F. Diarrassouba, «Predictive Modeling of Breast Anticancer Activity of a Series of Coumarin Derivatives Using Quantum Descriptors,» Chemical Science International Journal, vol. 26, n° %14, pp. 1-10, 2019. |
[9] | A–A. M. Fenjan et I. S. Mahdi, «Synthesis and Characterization of New Mannich Bases Derived from 7-hydroxy-4-methyl Coumarin,» Baghdad Sci. J, vol. 13, 2016. |
[10] | J. M. Rehm, G. L. McLendon, Y. Nagasawa, K. Yoshihara, J. Moser et M. Grätzel, «Femtosecond Electron-Transfer Dynamics at a Sensitizing Dye Semiconductor (TiO2) Interface,» J. Phys. Chem., vol. 100, n° %123, p. 9577–9578, 1996. |
[11] | M. Pastore et F. De Angelis, «Aggregation of Organic Dyes on TiO_{2} in Dye-Sensitized Solar Cells Models: An ab Initio Investigation,» ACS Nano, vol. 4, n° %11, p. 556–562, 2010. |
[12] | Y. Ooyama et Y. Harima, «Molecular Designs and Syntheses of Organic Dyes for Dye Sensitized Solar Cells,» Eur. J. Org. Chem., pp. 2903-2934, 2009. |
[13] | K. Hara, K. Sayama, Y. Ohga, A. Shinpo, S. Suga et H. Arakawa, «A coumarin-derivative dye sensitized nanocrystalline TiO2 solar cell having a high solar-energy conversion efficiency up to 5.6%» Chem. Commun, pp. 569-570, 2001. |
[14] | A. Furube, R. Katoh, K. Hara, T. Sato, S. Murata, H. Arakawa et M. Tachiya, «Lithium ion effect on electron injection from a photoexcited coumarin derivative into a TiO_{2} nanocrystalline film investigated by visible-to-IR ultrafast spectroscopy», J. Phys. Chem., vol. 109, pp. 16406-16414, 2005. |
[15] | Z. D. Sun, J. S. Zhao, Z. Mei et J. Xue-Hai, «Theoretical study of nitrogen cation modified aromatics containing thiophene as π-linker for p-type photosensitizers,» J Mol Model, vol. 25, p. 300, 2019. |
[16] | F. Diarrassouba, M. Koné, K. Bamba, Y. Traoré, M. Koné et E. Assanvo, «Development of Predictive QSPR Model of the First Reduction Potential from a Series of Tetracyanoquinodimethane (TCNQ) Molecules by the DFT (Density Functional Theory) Method,» Computational Chemistry, vol. 7, pp. 121-142, 2019. |
[17] | M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennuci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnerberg, M. Hada, Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, A. Montgomery, J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Milliam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomeli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, S. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski et D. J. Fox, Gaussian 09, Revison A.02, WAllingford C T: inc, 2009. |
[18] | A. Segalina, X. Assfeld, A. Monari et M. Pastore, «Computational Modeling of Exciton Localization in Self-Assembled Perylene Helices: Effects of Thermal Motion and Aggregate Size» J. Phys. Chem. C, vol. 123, n° %111, p. 6427–6437, 2019. |
[19] | F. Bonachera, Les triplets pharmacophoriques flous: développement et applications, Lille: Université Lille1 sciences et téchnologies: PhD thèse, 2011. |
[20] | J. Ghasemi, S. Saaidpour et S. D. Brown, «QSPR study for estimation of acidity constants of some aromatic acids derivatives using multiple linear regression (MLR) analysis,» J. Mol. Struct. (Theochem), vol 85(1), pp 27-32, 2007. |
[21] | P. Geladi et B. R. Kowalski, «Partial Least Squares Regression: a Tutorial,» Anal. Chim. Acta., vol. 185, pp. 1-17., 1986. |
[22] | A. F. Duprat, T. Huynh et G. Dreyfus, «Toward a principled methodology for neural network design and performance evaluation in RQSA. Application to the prediction of LogP,» J. Chem. Inf. Comput. Sci., vol. 38, pp. 586-594, 1998. |
[23] | J. Gasteiger et J. Zupan, «Neural Networks in Chemistry,» Angew. Chem. Int. Ed. Engl., vol. 32, pp. 503-527, 1993. |
[24] | Microsoft Office Professionnel Plus «(15.0.4420.1017) MSO (15.0.4420.1017) 64 Bits (2013),» Partie de Microsoft Office Professionnel Plus, 2013. |
[25] |
XLSTAT v2014 1995-2014, «XLSTAT and Addinsoft are Registered Trademarks of Addinsoft,»
https://www.xlstat.com 2014. |
[26] | M. V. Diudea, « QSPR/RQSA Studies for Molecular Descriptors,,» New York: Nova Science: Huntingdon, 2000. |
[27] | M. G.-R. Koné, ÉTUDE THÉORIQUE DE QUELQUES PROPRIÉTÉS MOLÉCULAIRES DANS LA FAMILLE DES BENZIMIDAZOLYL-CHALCONES, Abidjan: Université Nangui Abrogoua, 2017. |
[28] | L. Eriksson, J. Jaworska, A. Worth, M. T. D. Cronin, R. M. M. Dowell et P. Gramatica, «Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based RQSAs,» Environmental Health Perspectives, vol. 111, n°110, pp. 1361-1375, 2003. |
[29] | L. Zhang, H. Zhu, T. I. Oprea, A. Golbraikh et A. Tropsha, «RQSA Modeling of theBlood-Brain Barrier Permeability for Diverse Organic Compounds,» Pharm. Res., vol. 25, p. 1902–1914., 2008. |
[30] | L. He et P. C. Jurs, «Assessing the reliability of a RQSA model's predictions,» J. Mol. Graph. Model, vol. 23, pp. 503-523, 2005. |
[31] | P. Gramatica, «Principles of QSAR models validation: internal and external,» RQSA. Comb. Sci., vol. 26, p. 694–701, 2007. |
[32] | N. Chirico et P. Gramatica, «Real External Predictivity of QSAR Models: How To Evaluate It ? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient,,» J. Chem. Inf. Model, vol. 51, n° 119, pp. 2320-2335, 2011. |
[33] | A. Golbraikh et A. Tropsha, «Beware of q2!,» J. Mol. Graph. Model, vol. 20, p. 269–276., 2002. |
[34] | C. Qin et A. E. Clark, «DFT characterization of the optical and redox properties of natural pigments relevant to dye-sensitized solar cells,» Chem. Phys. Lett., vol. 26, p. 438, 2007. |
[35] | M. Nazeeruddin, E. Baranoff et M. Grätzel, «Dye-sensitized solar cells: A brief overview,» Solar energy, vol. 85, p. 1175, 2011. |
[36] | W. Sang-aroona, b. S. Saekowb et V. Amornkitbamrungb, «Density functional theory study on the electronic structure of Monascus dyes as photosensitizer for dye-sensitized solar cells,» Journal of Photochemistry and Photobiology A: Chemistry, vol. 236, p. 35– 40, 2012. |
[37] | M. Li, L. Kou, L. Diao, Q. Zhang, Z. Li, Q. Wu, W. Lu, D. Pan et Z. Wei, «Theoretical study of WS-9-Based organic sensitizers for unusual vis/NIR absorption and highly efficient dye-sensitized solar cells,» J. Phys. Chem., vol. 119, p. 9782–9790, 2015. |
APA Style
N’guessan, N. K., Richard, M. K. G., Patrice, O. W., Stéphane, D. G., Bamba, K., et al. (2024). Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived. Modern Chemistry, 12(2), 33-46. https://doi.org/10.11648/j.mc.20241202.12
ACS Style
N’guessan, N. K.; Richard, M. K. G.; Patrice, O. W.; Stéphane, D. G.; Bamba, K., et al. Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived. Mod. Chem. 2024, 12(2), 33-46. doi: 10.11648/j.mc.20241202.12
AMA Style
N’guessan NK, Richard MKG, Patrice OW, Stéphane DG, Bamba K, et al. Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived. Mod Chem. 2024;12(2):33-46. doi: 10.11648/j.mc.20241202.12
@article{10.11648/j.mc.20241202.12, author = {Nobel Kouakou N’guessan and Mamadou Koné Guy Richard and Ouattara Wawohinlin Patrice and Dembélé Georges Stéphane and Kafoumba Bamba and Nahossé Ziao}, title = {Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived }, journal = {Modern Chemistry}, volume = {12}, number = {2}, pages = {33-46}, doi = {10.11648/j.mc.20241202.12}, url = {https://doi.org/10.11648/j.mc.20241202.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mc.20241202.12}, abstract = {The performance of an organic solar cell is strongly influenced by the structure of the photosensitizer. In this work, the open-circuit voltage (VOC) and conversion efficiency (η) of a series of coumarin dyes are quantitatively related to the structure of nine coumarin derivatives. The Quantitative Structure Property Relationship (QSPR) is performed using the statistical method of multiple linear regression. In addition, descriptors determined from the ground state at the Cam_B3lyp/6-31G(d, p) level of theory and from the 2D structure of the molecules are mathematically related to the photovoltaic properties. These VOC and η models are accredited with very good statistical indicators (R2 = 0.906 and 0.918; Qcv2= 0.845 and 0.849; S= 0.045 and 0.112; F = 14.524 and 16.846). These statistical indicators confirm the robustness and performance of the models developed. The results show that Voc improves with decreasing surface tension (ts) and increasing number of cycles (cycle). As for the conversion efficiency of light radiation into electrical energy, it is optimal when the light harvesting efficiency (LHEth) and the excited state lifetime (τth) are high. In conclusion, these models have good predictive capabilities and can be used to predict and explain the open-circuit voltage and efficiency of coumarin derivatives that belong to the same field of application. }, year = {2024} }
TY - JOUR T1 - Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived AU - Nobel Kouakou N’guessan AU - Mamadou Koné Guy Richard AU - Ouattara Wawohinlin Patrice AU - Dembélé Georges Stéphane AU - Kafoumba Bamba AU - Nahossé Ziao Y1 - 2024/09/11 PY - 2024 N1 - https://doi.org/10.11648/j.mc.20241202.12 DO - 10.11648/j.mc.20241202.12 T2 - Modern Chemistry JF - Modern Chemistry JO - Modern Chemistry SP - 33 EP - 46 PB - Science Publishing Group SN - 2329-180X UR - https://doi.org/10.11648/j.mc.20241202.12 AB - The performance of an organic solar cell is strongly influenced by the structure of the photosensitizer. In this work, the open-circuit voltage (VOC) and conversion efficiency (η) of a series of coumarin dyes are quantitatively related to the structure of nine coumarin derivatives. The Quantitative Structure Property Relationship (QSPR) is performed using the statistical method of multiple linear regression. In addition, descriptors determined from the ground state at the Cam_B3lyp/6-31G(d, p) level of theory and from the 2D structure of the molecules are mathematically related to the photovoltaic properties. These VOC and η models are accredited with very good statistical indicators (R2 = 0.906 and 0.918; Qcv2= 0.845 and 0.849; S= 0.045 and 0.112; F = 14.524 and 16.846). These statistical indicators confirm the robustness and performance of the models developed. The results show that Voc improves with decreasing surface tension (ts) and increasing number of cycles (cycle). As for the conversion efficiency of light radiation into electrical energy, it is optimal when the light harvesting efficiency (LHEth) and the excited state lifetime (τth) are high. In conclusion, these models have good predictive capabilities and can be used to predict and explain the open-circuit voltage and efficiency of coumarin derivatives that belong to the same field of application. VL - 12 IS - 2 ER -