Hydrophobic long-chain two-dimensional perovskite scintillators for underwater X-ray imaging

The underwater X-ray imaging technology development is significant to subaqueous target reconnaissance/detection/identification, subfluvial archaeology,submerged resource exploration, etc. As the core of X-ray imaging detection, the scintillator has been plagued by inherent moisture absorption and decomposition, and strict requirements for seamless packaging and waterproofing.Here, we designed a manganese-doped two-dimensional(2D) perovskite scintillator modified by hydrophobic longchain organic amine through the combination of component and doping engineering. The modified perovskites show high water repellency that can be used as an underwater X-ray scintillator. X-ray images of aquatic organisms or other objects with a high spatial resolution of10 lp·mm^(-1) at a big view field(32 mm × 32 mm) were obtained by scintillation screen. This hydrophobic perovskite scintillator based on molecular design is of great promise in underwater X-ray nondestructive testing technology development.

Rapid and non-destructive decay detection of Yali pears using hyperspectral imaging coupled with 2D correlation spectroscopy

The black spot disease caused by Alternaria alternata on Yali pears is a great concern as it compromises their edible quality and commercial value.To realize rapid and non-destructive classification of this disease,hyperspectral imaging(HSI)technology was combined with two-dimensional correlation spectroscopy(2DCOS)analysis.A total of 150 pear samples at different decay grades were prepared.After obtaining the HSI images,the whole sample was demarcated as the region of interest,and the spectral information was extracted.Seven preprocessing methods were applied and compared to build the classification models.Thereafter,using the inoculation day as an external perturbation,2DCOS was used to select the feature-related wavebands for black spot disease identification,and the result was compared to those obtained using competitive adaptive reweighting sampling and the successive projections algorithm.Results demonstrated that the simplified least squares support vector model based on 2DCOS-identified feature wavebands yielded the best performance with the identification accuracy,precision,sensitivity,and specificity of 97.30%,94.60%,96.16%,and 98.21%,respectively.Therefore,2DCOS can effectively interpret the feature-related wavebands,and its combination with HSI is an effective tool to predict black spot disease on Yali pears.