journal article·2022-11Open access

Promoting the efficiency of quantum dots-based solar cells via the Cu:ZnSeS intermediate passivation layer

Feifei Yin, Xie Zou, Ming Chen, Zhe Sun, Xiaoli Bao, Zhonglin Du, Jianguo Tang

Abstract

Constructing the interface passivation layer is of importance to accelerate the charge transfer and thus enhance the efficiency of quantum dots-sensitized solar cells (QDSCs). Here, an effective Cu:ZnSeS intermediate passivation layer (IPL), aiming at improving the charge transfer process, is fabricated on the surface of photoanodes via the facile successive ionic layer adsorption and reaction (SILAR) process. Electrical chemical analysis results exhibit that the Cu:ZnSeS IPL can efficiently hinder the charge recombination and accelerate the charge transport process due to the synergistic effect of ZnSeS alloy layer and Cu ions dopant. Meanwhile, the time-resolved photoluminescence (TRPL) spectroscopy demonstrates the Cu:ZnSeS IPL can not only reduce the surface traps but also accelerate hole capture. Importantly, the laser confocal Kelvin probe force microscopy (KPFM) measurement is first used to investigate the charge transport process via the change of surface potential in dark and under laser irradiation. In terms of the merits of Cu:ZnSeS IPL, the power conversion efficiency (PCE) of the resultant CdSeTe-based QDSCs reaches up to 9.46%, with an enhancement of 14%, compared to the traditional single ZnS passivation layer (∼8.32%). Besides, employing the Cu:ZnSeS intermediate passivation layer, the PCE of 11.64% was achieved, which is considered the highest reported in the reported CdSeTe QDSCs so far. This work demonstrates a facile and effective approach to modulating the charge transport process, thus enhancing the device performance.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Quantum Dots Synthesis And Properties · Perovskite Materials and Applications · TiO2 Photocatalysis and Solar Cells

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

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