journal article·2019-08-23

Realization of red shift of absorption spectra using optical near-field effect

Takashi Yatsui, Yusuke Nakahira, Yuki Nakamura, Tatsuki Morimoto, Yuma Kato, Muneaki Yamamoto, Tomoko Yoshida, Kenji Iida, Katsuyuki Nobusada

Nanotechnology· Volume 30· Issue 34
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Abstract

Abstract In many applications such as CO 2 reduction and water splitting, high-energy photons in the ultraviolet region are required to complete the chemical reactions. However, to realize sustainable development, the photon energies utilized must be lower than the absorption edge of the materials including the metal complex for CO 2 reduction, the electrodes for water splitting, because of the huge amount of lower energy than the visible region received from the sun. In the previous works, we had demonstrated that optical near-fields (ONFs) could realize chemical reactions, by utilizing photon energies much lower than the absorption edge because of the spatial non-uniformity of the electric field. In this paper, we demonstrate that an ONF can realize the red shift of the absorption spectra of the metal-complex material for photocatalytic reduction. By attaching the metal complex to ZnO nano-crystalline aggregates with nano-scale protrusions, the absorption spectra by using diffuse reflection of the metal complex can be shifted to a longer wavelength by 10.6 nm. The results of computational studies based on a first-principles computational program including the ONF effect provide proof of the increase in the absorption of the metal complex at lower photon energies. Since the near-field assisted field increase improves the carrier excitation in the metal-complex materials, this effect may be universal and it could applicable to CO 2 reduction using the other metal-complex materials, as well as to the other photo excitation process including water splitting.
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Research context

OpenAlex topics: Quantum Dots Synthesis And Properties · Near-Field Optical Microscopy · Nanowire Synthesis and Applications

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

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