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Rapidly and accurately identifying tumor cells and blood cells is an important part of circulating tumor cell detection. Raman spectroscopy is a molecular vibrational spectroscopy technique that can provide fingerprint information about molecular vibrational and rotational energy levels. Deep learning is an advanced machine learning method that can be used to classify various data accurately. In this paper, the surface-enhanced Raman scattering spectra of blood cells and various tumor cells are measured with the silver film substrate. It is found that there are significant differences in nucleic acid-related characteristic peaks between most tumor cells and blood cells. These spectra are classified by the feature peak ratio method, principal component analysis combined with K-nearest neighbor, and residual network, which is a kind of deep learning algorithm. The results show that the ratio method and principal component analysis combined with the K-nearest neighbor method could only distinguish some tumor cells from blood cells. The residual network can quickly identify various tumor cells and blood cells with an accuracy of 100%, and there is no complex preprocessing for the surface-enhanced Raman scattering spectra. This study shows that the silver film surface-enhanced Raman scattering technology combined with deep learning algorithms can quickly and accurately identify blood cells and tumor cells, indicating an important reference value for the label-free detecting circulating tumor cells. [ABSTRACT FROM AUTHOR]

The porous layered SnO2-Li4Ti5O12@C composite is successfully prepared by hydrothermal, freeze-drying, and calcination methods. SnO2 nanoparticles are embedded in the Li4Ti5O12 matrix with zero strain, which effectively inhibit the coarsening of Sn and enhance the conversion between Sn and Li2O. Therefore, the material structure is stable, and the cycle stability is enhanced. Carbon coating on the Li4Ti5O12 loaded with SnO2 nanoparticles not only moderates the volume change of SnO2 during the cycle but also improves the conductivity. The layered SnO2-Li4Ti5O12@C composite exhibits excellent electrochemical performance and shows a reversible specific capacity of 958 mAhg−1 at 0.2 Ag−1 after 300 cycles, a high rate-specific capacity of 425 mAhg−1 at 5.0 Ag−1, and a substantial reversible specific capacity of 766 mAhg−1 at 2.0 Ag−1 after 1000 cycles. [ABSTRACT FROM AUTHOR]

Studies have shown that the construction of a three-dimensional interconnected filler network is an effective method to improve the thermal conductivity of the through-plane under a lower filler load. However, huge challenges still exist in building a long-range layered filler network and reducing the thermal resistance resulting from contact between fillers and matrix. In this work, boron nitride nanosheets (BNNSs) were proposed to be connected with modified cellulose nanofibers (CNFs) to obtain a long-range layered structure filler skeleton by bidirectional freezing orientation. Then epoxy resin was dipped under vacuum condition to prepare composite with thermal conductivity up to 1.43 W/mK in through-plane when filler content is 4.3 vol%, and the composite had low thermal expansion coefficient of 64.1 ppm/°C and excellent volume resistivity up to 3.7 × 1012 Ω cm at the same time. [ABSTRACT FROM AUTHOR]