Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network

Gao, Yun1; Zheng, Peng1; Meng, Zhao-Dong2; Wang, Hai-Long3; You, En-Ming2,4; Zhong, Jin-Hui5; Tian, Zhong-Qun2; Wang, Lei1; He, Hao1,5

2024-06-01

10.1021/acs.analchem.4c01211

ANALYTICAL CHEMISTRY   影响因子和分区

0003-2700

1520-6882

The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.

SCI ; EI

英语

通讯

National Natural Science Foundation of China["22174120","22204069","22272072"] ; National Natural Science Foundation of China["RCBS20221008093127073","JCYJ20230807093405012","20220815101643002"] ; Shenzhen Science and Technology Innovation Program[2023A1515012742]

Chemistry

Chemistry, Analytical

WOS:001237266500001

AMER CHEMICAL SOC

CHEMISTRY

Web of Science

1.Xiamen Univ, Pen Tung Sah Inst Micronano Sci & Technol, Xiamen 361102, Peoples R China
2.Xiamen Univ, Coll Chem & Chem Engn, Xiamen 361005, Peoples R China
3.Innovat Lab Sci & Technol Energy Mat Fujian Prov I, Xiamen 361102, Peoples R China
4.Jimei Univ, Sch Ocean Informat Engn, Xiamen 361021, Peoples R China
5.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China

Gao, Yun,Zheng, Peng,Meng, Zhao-Dong,et al. Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network[J]. ANALYTICAL CHEMISTRY,2024,96(23).

Gao, Yun.,Zheng, Peng.,Meng, Zhao-Dong.,Wang, Hai-Long.,You, En-Ming.,...&He, Hao.(2024).Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network.ANALYTICAL CHEMISTRY,96(23).

Gao, Yun,et al."Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network".ANALYTICAL CHEMISTRY 96.23(2024).