Mr. Ma Shengjie Profile

Ma Shengjie

at National Univ of Defense Technology

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Publications (3)

Proceedings Article | 31 January 2023 Paper

Self-assembled CsPbBr3 quantum nanosheet for optical reconnaissance satellite active jamming

You-Long Chen, Xing-yuan Zhang, Yi-Hua Hu, Yu-Shuang Zhang, Sheng-Jie Ma

Proceedings Volume 12505, 125051F (2023) https://doi.org/10.1117/12.2665449

KEYWORDS: Nanosheets, Satellites, Satellite imaging, Imaging systems, Perovskite, Optical imaging, Emission wavelengths, Absorption, Reconnaissance systems, Reconnaissance

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With the increasing frequency of space activities, space satellite systems are fully involved in joint operations. Numerous imaging and reconnaissance optical imaging satellite systems are arduous to evade and warn actively, and passive protection is cumbersome to implement. Therefore, developing new reflective imaging satellite reconnaissance systems and materials is particularly important. In countering optical imaging satellites, active jamming materials can form aerosol jamming smoke screens on the action path of the target to be protected. The jamming signal transmitted in all directions can suppress the imaging accuracy of the optical satellite to the target and form an effective deception. CsPbBr₃ perovskite quantum dots show great potential in anti-detection and active jamming due to their high absorption and emission efficiency, size-dependent excellent photoelectric properties, and low exciton binding energy. However, it is still challenging to synthesize CsPbBr₃ nanosheets with wide band emission for active interference. This paper uses the template induction method to regulate the morphology of the CsPbBr₃ crystal. CsPbBr₃ nanospheres and nanocubes were synthesized by the recrystallization method. CsPbBr₃ nanospheres were induced to self-assemble on CsPbBr₃ nanocubes to synthesize CsPbBr₃ nanosheets with wideband emissions. It maintained a strong quantum confinement effect, a simple synthesis process, high stability, and excellent photoelectric performance. The induced synthesis process was recorded by field emission transmission electron microscope. The nanosheets were face-to-face, close, and perpendicular to the carbon film substrate. The balance of van der Waals force and elastic repulsion force between ligands under thermodynamics determines the lamellar spacing of nanosheets in the self-assembled state. UV-Vi's absorption spectra further proved the change of morphology during self-assembly. The nanosheets were also very stable under 200kV electron beam bombardment. The fast Fourier transform patterns of the nanosheet demonstrated that the assembled NCs were orthogonal, with no crystal transition in the assembly process. X-ray photoelectron spectroscopy showed that the crystal is composed of CS, Pb, and Br elements. The assembly process did not change the ion state and element composition on the surface and inside. The photoluminescence spectrum showed that the emission wavelength of the nanosheets was 493nm, and a uniformly tunable emission wavelength was generated between 468-506nm. The disappearance of the tail band in the long wavelength region demonstrated that the defects in the self-assembly process were significantly reduced. The quantum yield increased from 65.78% to 67.33%, which directly confirmed that the large absorption cross section and defect reduction brought by the unique sheet structure were conducive to the conversion of excitation light in the active interference process. This paper provides an idea for the theoretical study of morphology regulation of perovskite nanocrystals and other system nanocrystals. Most importantly, the high coincidence between the steady-state fluorescence spectrum and the response curve of the Si-based CCD attested that quantum dots could be used to interfere with the optical imaging system based on CCD actively.

Proceedings Article | 31 August 2021 Paper

Estimation of atmospheric turbulence intensity based on deep learning

Shengjie Ma, Shiqia Hao, Qingsong Zhao, Chenlu Xu

Proceedings Volume 11907, 1190726 (2021) https://doi.org/10.1117/12.2603106

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Proceedings Article | 2 December 2020 Paper

Prediction of atmospheric turbulence refractive index structure constant based on deep learning

Shengjie Ma, Shiqi Hao, Qingsong Zhao, Chenlu Xu, Junling Xiao

Proceedings Volume 11717, 1171709 (2020) https://doi.org/10.1117/12.2583944

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