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Development of microwave absorbing materials with tunable thickness and bandwidth is particularly important for practical applications, but its realization remains a great challenge. Here, we report carbon coated core-shell FeSiCr/Fe3C embedded in two-dimensional carbon nanosheets network (FeSiCr/Fe3C@C/C) nanocomposites fabricated by plasma arc-discharging method. FeSiCr/Fe3C@C/C has the best microwave absorption properties in which the minimum calculated reflection loss (RL) can reach − 4 2. 3 dB at 11.5 GHz with a coating thickness of 2.4 mm and the efficient absorption bandwidth (RL < − 1 0 dB) almost covers 4.5–18 GHz range by adjusting the coating thicknesses from 1.3 to 5.0 mm. The improved microwave absorption properties could be ascribed to the optimized impedance matching and enhanced polarization loss, conduction loss and internal reflections of the propagated microwave. Carbon coated core-shell FeSiCr/Fe3C embedded in two-dimensional carbon nanosheets network (FeSiCr/Fe3C@C/C) nanocomposites was fabricated by plasma arc-discharging method. The 2D carbon nanosheets network and core-shell structure can be observed from FeSiCr/Fe3C@C/C, and the average diameter is about 5.4 nm. FeSiCr/Fe3C@C/C is a kind of absorbing material which has excellent microwave absorption performance. [ABSTRACT FROM AUTHOR]

ABSTRACT Perylene diimides (PDIs) and their derivatives are excellent semiconductors, while conjugated polymers based on PDIs have limited applications because of their low electron mobility ( μe) derived from low molecular weight. The reported maximum number-average molecular weight ( Mn) of related polymers is only 21 kDa because PDIs have very poor solubility due to strong π-π stacking of their big planar conjugated cores. Herein, it is found that suitable semi-perfluoroalkyl groups could enhance the solubility of PDIs significantly, and a series of semi-perfluoroalkyl modified conjugated polymers with high molecular weight and electron mobility were synthesized. The maximum Mn reaches 94.8 kDa [P(4CF8CH-PDI-T2)HW]. In their space-charge-limited current (SCLC) devices, all polymers exhibit typical characters of electron transporting semiconductors, and the highest μe is up to 8.40 × 10−3 cm2 V−1 s−1 [P(4CF8CH-PDI-T2)HW], which is similar as that of widely used electron transporting semiconductor PC61BM (6.41 × 10−3 cm2 V−1 s−1). © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2018, 56, 116-124 [ABSTRACT FROM AUTHOR]

Abstract: In the present work, the temperature dependences of ac transport and magnetoimpedance for sol–gel La0.67Ca0.33MnO3 manganite are investigated at various frequencies. The ac resistance R with absence of field shows a peak around the metal–insulator transition temperature T MI for the dc case. A low magnetic field can shift the metal–insulator transition temperature more easily at high frequency than at low frequency. The valley phenomenon is observed in the temperature dependence of reactance X at 2 and 4MHz, showing the effects of phase separation or grain boundaries. At 30MHz, the peak of X appears instead of the valley, since the contribution of insulator capacitance term drops greatly and the inductance of metal dominates the X term with an increase of ac frequency in the sample. At 2 and 4MHz, La0.67Ca0.33MnO3 sol–gel manganite under H=1kOe shows negative ac magnetoresistance ΔR/R 0, magnetoreactance ΔX/X 0 and magnetoimpedance ΔZ/Z 0 below 275K, but positive ac ΔR/R 0, ΔX/X 0 and ΔZ/Z 0 near room temperature much higher than the Curie temperature. The peak temperature of R/R 0 moves to low temperature with an increase of the ac frequency from 2 to 4MHz. At f=30MHz, the absolute value of negative ac ΔR/R 0 increases monotonically with a decrease of temperature for 175K

Purpose: This study aims to develop and validate a model predictive for the incidence of grade 4 radiation-induced lymphopenia (G4RIL), based on dosiomics features and radiomics features from the planning CT of nasopharyngeal carcinoma (NPC) treated by radiation therapy., Methods: The dataset of 125 NPC patients treated with radiotherapy from August 2018 to March 2019 was randomly divided into two sets-an 85-sample training set and a 40-sample test set. Dosiomics features and radiomics features of the CT image within the skull bone and cervical vertebrae were extracted. A feature selection process of multiple steps was employed to identify the features that most accurately forecast the data and eliminate superfluous or insignificant ones. A support vector machine learning classifier with correction for imbalanced data was trained on the patient dataset for prediction of RIL (positive classifier for G4RIL, negative otherwise). The model's predictive capability was gauged by gauging its sensitivity (the likelihood of a positive test being administered to patients with G4RIL) and specificity in the test set. The area beneath the ROC curve (AUC) was utilized to explore the association of characteristics with the occurrence of G4RIL., Results: Three clinical features, three dosiomics features, and three radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction. The combination model, based on nine robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with three dosiomics features, had an ACC value of 0.82, the radiomics model, with three radiomics features, had an ACC value of 0.82, and the clinical model, with its initial features, had an ACC value of 0.6 for prediction performance., Conclusion: The findings show that radiomics and dosiomics features are correlated with the G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Huang, Yang, Pang, Zeng, Yang, Zhou, Wu, Shen, Zhang, Lou, Jin, Abdilim, Jin, Zhang and Xie.)