ORIGINAL PAPER Donor functionalized quinoline based organic sensitizers for dye sensitized solar cell (DSSC) applications: DFT and TD-DFT investigations P. Pounraj 1 & V. Mohankumar 1 & M. Senthil Pandian 1 & P. Ramasamy 1 Received: 7 June 2018 /Accepted: 5 November 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract The influence of different donor groups in quinoline based novel sensitizers for dye sensitized solar cell (DSSC) applications is analyzed by using density functional theory (DFT) and time dependent density functional theory (TD-DFT). Quinoline and donor functionalized quinoline based novel organic sensitizers have been designed with different π-spacers for DSSC applications. The ground state molecular structure of novel organic sensitizers is fully optimized by DFT calculation in both gas and chloroform phases. Electronic absorption characteristics are predicted by the TD-DFT calculation in both gas and chloroform phases. The polarizable continuum model is used for solvent phase optimization. The net electron transfer from the donor to acceptor is calculated from natural bond orbital (NBO) analysis. The injection energy and dye regeneration energy values are also calculated. Different donor groups are substituted in quinoline, and these substituted quinoline donors are used as the donor group. Cyanovinyl and thiophene groups act as π-spacers and cyanoacrylic acid acts as an acceptor. DFT and TD-DFT studies of the quinoline and donor functionalized quinoline sensitizers show that the coumarin based and N-hexyltetrahydroquinoline donors are more efficient for DSSC application. Keywords Quinoline . DSSC . DFT . TD-DFT . HOMO-LUMO Introduction Dye-sensitized solar cells (DSSCs) are a potentially lower cost alternative to the conventional silicon and other photovoltaic devices. They have attracted significant attention as a new generation photovoltaic (PV) device since demonstration by O’Regan and Grätzel reported in 1991 [1, 2]. Generally, DSSC consists of four parts, such as the sensitizer [ 3], photoanode [4], electrolyte [5], and counter electrode [6]. Among them, the sensitizer plays a significant role in light harvesting by excitation of electron and injection into the semiconductor band. DSSC based on conventional Ru- polypyridyl complexes, such as N3/N719 and the black dyes, have been investigated intensively and yielded a high photon- to-electron conversion efficiency exceeding 11% under stan- dard illumination [7–9]. However, the availability, cost, limit- ed Ru resource, and the environmental pollution issues have made them inappropriate for large-scale application. Fortunately, the metal free organic dyes possess some out- standing advantages, such as higher molar extinction coeffi- cient, lower cost, and more facile nature in molecular design. Because of these advantages, the organic dyes have made great progress in the DSSC field [10–12]. Generally, the metal free organic structures have donor-π- bridge-acceptor (D-π-A) bipolar configuration. The donor group (D) is an electron-rich group and is linked through a conjugated linker (π) to the electron acceptor group (A), which is directly anchored on the TiO 2 surface. This sensitizer structure can induce the intramolecular charge transfer (ICT) from the donor part to the acceptor part during photo excita- tion. It is favored for effective charge separation and injection of photo excited electrons into the conduction band of TiO 2 . However, the efficiency of the dye can be greatly improved by suitable modification on donor [13–15], π-spacer [16–19], and acceptor subunits [20–22]. The intramolecular charge transfer and red shifted absorption spectrum properties of the organic sensitizer were accelerated by new types of structures, such as D-D-π-A [23, 24], D-A-π-A [25–27], and double D-π-A [28–30]. The D-D-π-A structures are formed by adding the more electron donating (D) subunits in the D-π- * P. Ramasamy ramasamyp@ssn.edu.in 1 SSN Research Centre, SSN College of Engineering, Chennai, Tamilnadu 603 110, India Journal of Molecular Modeling (2018) 24:343 https://doi.org/10.1007/s00894-018-3872-8