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arXiv 2307.03485physics.optics
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通过VSe2-还原氧化石墨烯杂化体系中的电荷转移增强超快三阶非线性光学响应

Enhancing the ultrafast third order nonlinear optical response by charge transfer in VSe2-reduced graphene oxide hybrid

Vinod Kumar, Afreen, K. A. Sree Raj, Pratap mane, Brahmananda Chakraborty, Chandra S. Rout, K. V. Adarsh

AI总结:

本文通过构建VSe2-还原氧化石墨烯电荷转移杂化体系,利用密度泛函理论与光致发光测量揭示其电子耦合机制,将超快三阶非线性光学响应提升至少一个数量级,并制备出性能更优的超快光限幅器件。

摘要:

非线性光学现象在理解微观光与物质相互作用方面发挥着关键作用,并在生物传感、量子信息、光开关和全光数据处理等各个领域具有深远的应用。这些应用大多需要具有高三阶吸收和折射光学非线性的材料。然而,大多数材料由于其微扰性质而表现出较弱的非线性光学响应,通常需要改进才能用于实际应用。在此,我们证明了VSe2-还原氧化石墨烯(rGO)的电荷供体-受体杂化体系与原始体系相比,表现出增强的超快三阶吸收和折射非线性,至少提高了一个数量级。通过密度泛函理论和Bader电荷分析,我们阐明了VSe2-rGO杂化体系中的强电子耦合,其中涉及电子从VSe2转移到rGO。稳态和时间分辨光致发光(PL)测量证实了电子耦合和电荷转移。此外,我们制备了一种超快光限幅器件,具有更优的性能参数,例如起始阈值为2.5 mJ cm⁻²,差分透射率为0.42。

英文摘要:

Nonlinear optical phenomena play a critical role in understanding microscopic light-matter interactions and have far-reaching applications across various fields, such as biosensing, quantum information, optical switching, and all-optical data processing. Most of these applications require materials with high third-order absorptive and refractive optical nonlinearities. However, most materials show weak nonlinear optical responses due to their perturbative nature and often need to be improved for practical applications. Here, we demonstrate that the charge donor-acceptor hybrid of VSe2-reduced graphene oxide (rGO) hybrid exhibits enhanced ultrafast third-order absorptive and refractive nonlinearities compared to the pristine systems, at least by one order of magnitude. Through density functional theory and Bader charge analysis, we elucidate the strong electronic coupling in the VSe2-rGO hybrid, involving the transfer of electrons from VSe2 to rGO. Steady-state and time-resolved photoluminescence (PL) measurements confirm the electronic coupling and charge transfer. Furthermore, we fabricate an ultrafast optical limiter device with better performance parameters, such as an onset threshold of 2.5 mJ cm-2 and differential transmittance of 0.42.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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