journal article·2014-07-14

N-doped graphene quantum dots-functionalized titanium dioxide nanofibers and their highly efficient photocurrent response

Xiaotian Wang, Dandan Ling, Yueming Wang, Huan Long, Yibai Sun, Yanqiong Shi, Yuchao Chen, Yao Jing, Yueming Sun, Yunqian Dai

Journal of Materials Research· Volume 29· Issue 13
Publisher page

Abstract

Titanium dioxide (TiO2), a widely used inorganic semiconductor owing to its superb photoelectric properties, has frequently been fabricated into composites to reduce its relatively large band gap and overcome its limited visible light absorption. In this article, a “layer-by-layer” method has been developed to prepare the composite structure of nitrogen (N)-doped graphene quantum dots (GQDs)-sensitized TiO2nanofibers. The as-prepared structure shows considerable luminescence and exhibits excellent photoelectric properties. Various factors including the crystalline phase of TiO2, amount of N in GQDs, and irradiation wavelength were investigated to find the optimal conditions for enhanced photoelectric activity. It is demonstrated that the combination of highest N amount GQDs with TiO2nanofibers of mixed phases (750 °C-sintered TiO2nanofibers) possess the best photoelectric properties. The enhancement of properties using TiO2nanofibers with mixed phases mainly contributes to the transfer of electrons between conduction bands of different phases in TiO2and the distinctive photoluminescence (PL) property of N-GQDs. Furthermore, this enhancement can be achieved in most areas of the visible light range. The general mechanism of the electron generation and transfer of the structure is based on the normal PL and upconversion PL property of N-GQDs which serve as the sensitizer. We consider it a feasible method to improve the photoelectric conversion efficiency in photovoltaic devices.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Carbon and Quantum Dots Applications · Advanced Photocatalysis Techniques · Graphene research and applications

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

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